Research progress on deep learning in magnetic resonance imaging–based diagnosis and treatment of prostate cancer: a review on the current status and perspectives

Abstract

Multiparametric magnetic resonance imaging (mpMRI) has emerged as a first-line screening and diagnostic tool for prostate cancer, aiding in treatment selection and noninvasive radiotherapy guidance. However, the manual interpretation of MRI data is challenging and time-consuming, which may impact sensitivity and specificity. With recent technological advances, artificial intelligence (AI) in the form of computer-aided diagnosis (CAD) based on MRI data has been applied to prostate cancer diagnosis and treatment. Among AI techniques, deep learning involving convolutional neural networks contributes to detection, segmentation, scoring, grading, and prognostic evaluation of prostate cancer. CAD systems have automatic operation, rapid processing, and accuracy, incorporating multiple sequences of multiparametric MRI data of the prostate gland into the deep learning model. Thus, they have become a research direction of great interest, especially in smart healthcare. This review highlights the current progress of deep learning technology in MRI-based diagnosis and treatment of prostate cancer. The key elements of deep learning-based MRI image processing in CAD systems and radiotherapy of prostate cancer are briefly described, making it understandable not only for radiologists but also for general physicians without specialized imaging interpretation training. Deep learning technology enables lesion identification, detection, and segmentation, grading and scoring of prostate cancer, and prediction of postoperative recurrence and prognostic outcomes. The diagnostic accuracy of deep learning can be improved by optimizing models and algorithms, expanding medical database resources, and combining multi-omics data and comprehensive analysis of various morphological data. Deep learning has the potential to become the key diagnostic method in prostate cancer diagnosis and treatment in the future.

Full text

Research progress on deep
learning in magnetic resonance
imaging–based diagnosis and
treatment of prostate cancer:
a review on the current status
and perspectives
Mingze He
1
,YuCao
2
, Changliang Chi
3
, Xinyi Yang
2
,
Rzayev Ramin
4
, Shuowen Wang
2
, Guodong Yang
2
,
Otabek Mukhtorov
5
, Liqun Zhang
6
, Anton Kazantsev
5
,
Mikhail Enikeev
1
*and Kebang Hu
3
*
1
Institute for Urology and Reproductive Health, I.M. Sechenov First Moscow State Medical University
(Sechenov University), Moscow, Russia,
2
I.M. Sechenov First Moscow State Medical University
(Sechenov University), Moscow, Russia,
3
Department of Urology, The First Hospital of Jilin University
(Lequn Branch), Changchun, Jilin, China,
4
Department of Radiology, The Second University Clinic,
I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia,
5
Regional State Budgetary Health Care Institution, Kostroma Regional Clinical Hospital named after
Korolev E.I. Avenue Mira, Kostroma, Russia,
6
School of Biomedical Engineering, Faculty of Medicine,
Dalian University of Technology, Dalian, Liaoning, China
Multiparametric magnetic resonance imaging (mpMRI) has emerged as a first-
line screening and diagnostic tool for prostate cancer, aiding in treatment
selection and noninvasive radiotherapy guidance. However, the manual
interpretation of MRI data is challenging and time-consuming, which may
impact sensitivity and specificity. With recent technological advances, artificial
intelligence (AI) in the form of computer-aided diagnosis (CAD) based on MRI
data has been applied to prostate cancer diagnosis and treatment. Among AI
techniques, deep learning involving convolutional neural networks contributes
to detection, segmentation, scoring, grading, and prognostic evaluation of
prostate cancer. CAD systems have automatic operation, rapid processing, and
accuracy, incorporating multiple sequences of multiparametric MRI data of the
prostate gland into the deep learning model. Thus, they have become a research
direction of great interest, especially in smart healthcare. This review highlights
the current progress of deep learning technology in MRI-based diagnosis and
treatment of prostate cancer. The key elements of deep learning-based MRI
image processing in CAD systems and radiotherapy of prostate cancer are briefly
described, making it understandable not only for radiologists but also for general
physicians without specialized imaging interpretation training. Deep learning
technology enables lesion identification, detection, and segmentation, grading
and scoring of prostate cancer, and prediction of postoperative recurrence and
prognostic outcomes. The diagnostic accuracy of deep learning can be
Frontiers in Oncology frontiersin.org01
OPEN ACCESS
EDITED BY
Marco Fusella,
Abano Terme Hospital, Italy
REVIEWED BY
Martina Murr,
University of Tübingen, Germany
Rakesh Shiradkar,
Emory University, United States
*CORRESPONDENCE
Kebang Hu
hukb@jlu.edu.cn
Mikhail Enikeev
enikeev_m_e@staff.sechenov.ru
RECEIVED 19 March 2023
ACCEPTED 30 May 2023
PUBLISHED 13 June 2023
CITATION
He M, Cao Y, Chi C, Yang X, Ramin R,
Wang S, Yang G, Mukhtorov O, Zhang L,
Kazantsev A, Enikeev M and Hu K (2023)
Research progress on deep learning in
magnetic resonance imaging–based
diagnosis and treatment of prostate
cancer: a review on the current status
and perspectives.
Front. Oncol. 13:1189370.
doi: 10.3389/fonc.2023.1189370
COPYRIGHT
© 2023 He, Cao, Chi, Yang, Ramin, Wang,
Yang,Mukhtorov,Zhang,Kazantsev,Enikeev
and Hu. This is an open-access article
distributed under the terms of the Creative
Commons Attribution License (CC BY). The
use, distribution or reproduction in other
forums is permitted, provided the original
author(s) and the copyright owner(s) are
credited and that the original publication in
this journal is cited, in accordance with
accepted academic practice. No use,
distribution or reproduction is permitted
which does not comply with these terms.
TYPE Review
PUBLISHED 13 June 2023
DOI 10.3389/fonc.2023.1189370

improved by optimizing models and algorithms, expanding medical database
resources, and combining multi-omics data and comprehensive analysis of
various morphological data. Deep learning has the potential to become the
key diagnostic method in prostate cancer diagnosis and treatment in the future.
KEYWORDS
deep learning, machine learning, computer-aided diagnosis, prostate cancer,
radiotherapy, precision therapy
1 Introduction
Prostate cancer (PCa) is a commonly occurring urological
malignancy among middle-aged and older men, with its incidence
on the rise. In 2020, there were approximately 1.4 million new PCa
cases reported globally, resulting in around 375,000 deaths, making
it the second most common cancer among men, following lung
cancer, and the fifth leading cause of cancer-related deaths among
men (1). Although digital rectal examination and prostate-specific
antigen (PSA) test are routinely conducted for the diagnosis of PCa,
transrectal ultrasound (TRUS) has been the primary imaging
technique for clinical suspicion and diagnosis of PCa in the past
(2,3). However, due to its low sensitivity and specificity, particularly
for detecting lesions present in the transitional zone (TZ), mpMRI
has replaced TRUS as the first-line radiological screening modality
for clinical suspicion of PCa (4–6). Compared with other imaging
examinations, MRI of the prostate provides a higher soft-tissue
resolution and multiple imaging data parameters non-invasively,
which facilitates better understanding of the complete prostate
gland and its relationship with the surrounding environment and
also provides improved guidance for PCa staging (7,8). Therefore,
MRI has become the preferable imaging tool for patients with
suspected PCa or those at risk of PCa (9–11). The prostate imaging-
reporting and data system (PI-RADS) provides a comprehensive set
of standards for scanning, interpreting, and reporting mpMRI (12).
Combining mpMRI with PI-RADS scaling results in more precise
PCa diagnosis and staging, as well as improved guidance for later
biopsies, and has contributed significantly to reducing
overdiagnosis (13–15). Although mpMRI is a valuable technique
in PCa diagnosis, manual interpretation of mpMRI data is complex,
time-consuming, and challenging due to low sensitivity and
specificity of the interpreting results (16–18). Deep learning (DL)
technology has the capability to mine various features from medical
images that are difficult to identify and distinguish using the naked
eye in the macroscopic view (19,20). DL technology can guide
clinicians in medical diagnosis and help reduce diagnostic accuracy
issues caused by factors mentioned above, providing physicians
with accurate disease information (21,22). Over the past few years,
several computer-aided diagnosis (CAD) systems have been applied
to PCa diagnosis with positive outcomes (23–26). CAD systems can
be classified into two categories: computer-aided detection (CADe)
and computer-aided diagnosis (CADx) (27,28). CADe can
determine if a patient has PCa and localize the possible PCa
lesion based on the entire mpMRI data. CADx can evaluate a
series of manually or automatically selected tumor-suspected areas
by radiologists or CADe systems, followed by assessing and
evaluating the aggressiveness of PCa (24,29–31). This review
presents a cross-disciplinary summary of research progress in
PCa using DL-based CADs to make the artificial intelligence (AI)
process understandable to not only radiologists but also general
physicians who lack systematic and specialized imaging
interpretation training. We briefly describe the application of DL
techniques based on prostate MRI data and provide possible
research ideas for future studies.
2 Overview of deep
learning techniques
According to the latest Prostate Imaging-Reporting and Data
System Version 2.1 (PI-RADS 2.1) recommendations, mpMRI image
sequences for PCa detection and diagnosis typically consist of T
2
-
weighted imaging (T
2
W), diffusion-weighted imaging (DWI),
average diffusion coefficient (ADC) maps, and dynamic contrast-
enhanced (DCE) imaging (12). DWI and ADC sequences are
primarily employed to detect peripheral zone lesions, while T
2
W
focuses on detecting transition zone lesions (32,33). The PI-RADS
score, calculated from mpMRI data, ranges from 1 (low likelihood of
clinically significant PCa) to 5 (high likelihood of clinically significant
PCa) and serves as a crucial diagnostic measure to determine the
necessity of a biopsy (34,35). Due to its significant clinical value, PI-
RADS recommendations have been updated for standardizing
prostate MRI scanning and interpretation processes. However,
accurately interpreting mpMRI data requires a high level of
expertise and skill. Furthermore, inter-observer and intra-observer
variability values, which pertain to different radiologists interpreting
the same MRI results and a single radiologist interpreting the same
MRI results multiple times, respectively, tend to exhibit high
variability (36,37). This affects the broader utilization of mpMRI in
PCa diagnosis. Consequently, to reduce interpretation time, enhance
image interpretation quality, and minimize the risk of overtreatment,
DL has emerged as the predominant AI method within machine
learning (ML) technology (38,39). Inspired by human learning
patterns, ML can be broadly categorized into supervised learning,
He et al. 10.3389/fonc.2023.1189370
Frontiers in Oncology frontiersin.org02

which utilizes well-labeled training data examples with fully
controlled data input and output, and unsupervised learning, which
operates on unlabeled datasets and aims to identify correlations
within the dataset (38–40). Semi-supervised learning, a hybrid
mode between supervised and unsupervised learning, uses partially
labeled training data while the remainder stays unlabeled (39,41,42).
Another renowned learning framework, reinforcement learning,
obtains feedback based on each action’sresponseinthe
environment, modifying model parameters to maximize anticipated
benefits (39,43). DL technology, introduced by Hinton in 2006 (44),
is an ML subset sharing similar working principles but featuring an
advanced multilayer neural network that mimics human biological
neural networks for data representation learning (39,45). A key
distinction between ML and DL lies in feature extraction methods,
where conventional ML relies on hand-crafted approaches by expert
specialists, and DL automatically extracts features within network
layers (45,46). As hand-crafted feature design demands significant
effort and considerable workload, a growing number of DL algorithm
(DLA) are emerging, tending to replace ML in medical image
processing (20,45,47). However, hand-crafted approaches persist
in situations with limited annotated data, and several studies have
demonstrated promising results by fusing models with classic hand-
crafted features and DL-extracted features (46,48,49). Typically, the
majority of CAD systems for PCa involve processing steps such as
imaging alignment, prostate localization and segmentation, feature-
based lesion detection, and task-based classification (16,24). With the
increasing volume of labeled imaging data, supervised learning
models represented by convolutional neural networks (CNNs) and
non-supervised learning frameworks, including generative
adversarial networks (GANs), have been incorporated into various
medical imaging processes (50–53). In general, DLA-based analysis
constitutes a new form of computer-aided diagnosis that facilitates
the automatic acquisition of data-related features and identification of
lesions without the requirement of manual segmentation, provided
that the dataset is sufficiently large. This approach is innovative in
that it allows for spontaneous learning of features, resulting in
improved efficiency and accuracy of lesion detection (16,20,45,
54)(Figure 1).
2.1 Main working principles of
DLA (Figure 2A)
In summary, within the medical imaging domain, DLAs perform
tasks such as image classification, object detection, and semantic
segmentation. Image classification determines benignity or
malignancy, tumor types, grading, and staging from medical images;
object detection localizes tumors and extracts their information from
images; and semantic segmentation outlines tumors or adjacent
organs in the images (54–57). Among the various DL-based models,
deep CNNs have garnered significant attention due to their promising
performance in medical imaging (52,58). The multilayer neural
network structure of DLAs, inspired by the human visual system,
has the potential to process convolution operations (38,52,59). CNN
structures primarily consist of a convolutional layer, max-pooling
layer, and fully connected layer (20,38,58–61). These layers pertain to
specific calculation methods or functions that receive, compute, and
output relevant data. The convolution layer, the core of CNNs,
extracts image features by constructing multiple convolution kernels
(60). The max-pooling layer, also known as the down-sampling layer,
reduces computational effort by consolidating data within a certain
range. The fully connected layer, used as a classifier, integrates all local
information acquired from the previous max-pooling or convolutional
layer that is class-distinctive, ultimately producing the desired class
predictions (58,61,62). In brief, the convolutional layer of a CNN
functions as a feature extractor, while the fully connected layer serves
as a classifier (63)(Figure 2B). The trained CNN forms its network
structure and weight files, which are the foundation for predicting the
same type of unknown data. Function-dependent networks based on
CNN have been designed for specific computer vision tasks, such as
AlexNet and ResNet for image classification (64,65), YOLO and
Faster R-CNN for object detection (66,67), and U-Net and Mask R-
CNN for semantic segmentation (58,61,62,68). Another valuable
deep learning network, the GAN, has demonstrated effectiveness for
semi-supervised learning (69), supervised learning (70), and
reinforcement learning (71), despite its initial proposal for
unsupervised learning. GAN can be simply described as a deep
learning model used to create alternative imaging data similar to the
target data, but with improved quality and reduced noise (51,53,72).
GAN primarily consists of two separate but interdependent neural
networks, functioning as the generator and the discriminator.
Generated data (false) from the generator, using random variables
as input, or target data (true) are then input into the discriminator (53,
72). These two networks are trained competitively and adversarially,
with the aim of making the discriminator model strictly capable of
distinguishing between the synthesized data generated by the
generator and the true data, while the generator intends to create
data as realistic as possible compared to the target data (53,72,73).
The primary objective of GAN is to render the discriminator network
incapable of differentiating between the output data generated by the
generator network and real data (Figure 2C). More recently, deep
convolutional GANs (DCGANs) have emerged by combining CNNs
and GANs to achieve better performance and effectiveness, resulting
in their increasing popularity for designing various computer-aided
diagnosis(CADx)models(53,74–76).
FIGURE 1
Stratification of artificial intelligence. Created with BioRender.com.
He et al. 10.3389/fonc.2023.1189370
Frontiers in Oncology frontiersin.org03

2.2 Evaluation metrics of DLA
Evaluation metrics for DLA in medical imaging applications
encompass accuracy, specificity, sensitivity, dice similarity
coefficient (DSC), Jaccard index, receiver operating characteristic
(ROC) curve, and area under the ROC curve (AUC) (77–81). While
general clinicians need not fully grasp the complex equations and
processes involved in these evaluation metrics, a basic
understanding of their core principles is crucial for accurately
interpreting the performance of pertinent DL models. Sensitivity
denotes the likelihood of detecting a positive sample within a
positive population (1.2.1), while specificity refers to the
probability of identifying a negative result in a negative
population (1.2.2) (77). DSC (1.2.3) and the Jaccard index are
promising evaluation metrics for assessing segmentation quality.
They are typically employed to calculate the similarity between two
samples, with values ranging from 0 to 1. A value closer to 1
indicates a better model performance (78,82). The ROC curve is
used to evaluate the diagnostic accuracy and performance of various
models. A curve closer to the upper left corner signifies higher
diagnostic value, and a larger AUC corresponds to greater
application value (79–81). AUC serves as a criterion for
determining the quality of classification models, referring to the
likelihood of positive examples ranking higher than negative
examples in prediction outcomes. An AUC between 0.5 and 1
implies that the model possesses predictive value, and a value closer
to 1 signifies superior model performance (79–81).
Sensitivity =TP
(TP +FN)1:2:1
Specificity =TN
(TN +FP)1:2:2
DSC = 2*∣X∩Y∣
X
jj
+Y
jj 1:2:3
B
C
A
FIGURE 2
Schematic illustration of deep learning algorithm. (A) Working algorithm of image processing. (B) Principle and architecture of CNN. (C) Principle and
architecture of GAN: T, True data; F, False data; ∫1-Function 1 of the generator network; ∫2-Function 2 of the discriminator network. Created with
BioRender.com.
He et al. 10.3389/fonc.2023.1189370
Frontiers in Oncology frontiersin.org04

3 Application of mpMRI-based
DLA on PCa
3.1 Diagnosis of PCa
3.1.1 Detection and classification of PCa
In the clinical management of PCa, accurately distinguishing
between low-risk and high-risk cases is crucial to prevent
overdiagnosis or delayed treatment (92). For patients with low-
risk PCa, mpMRI serves as the primary imaging technique to
determine if the lesion has grown or metastasized and to assess
disease progression during active surveillance (93). Therefore, a
reliable noninvasive assessment system is of significant importance.
Fusco et al. (94) performed a systematic literature review, reporting
that MRI holds considerable clinical value in localizing and staging
PCa. Vente et al. (95) developed a multitasking U-Net model using
T
2
W and DWI sequences of MRI, capable of simultaneously
detecting and grading PCa with excellent diagnostic outcomes.
Wang et al. (96) designed an end-to-end CNN comprising two
sub-networks: one for aligning apparent DWI and T
2
W, and the
other as a convolutional neural classification network. The end-to-
end CNN model was trained and assessed on 360 patients using a
fivefold cross-validation method, ultimately exhibiting a sensitivity
of 0.89 for identifying high-risk PCa cases. Ishioka et al. (97)
developed a fully automated PCa detection system using patients’
T
2
W sequence data, combining two distinct algorithms and
demonstrating an AUC of 0.793. Wang et al. (98) compared the
detection capabilities of DLAs and non-DLAs in differentiating
PCa, using T
2
W sequences from prostate MRI findings of 172
patients, which included 79 patients with PCa and 93 with benign
prostatic hyperplasia (BPH). The final ROC curve value was 0.84 for
the DL model, compared to 0.70 for the non-DL model. Sanford
et al. (99) conducted PI-RADS scoring with a CNN trained on T
2
W/
ADC/high-b values, confirming that DLAs possess a PCa
assessment potential comparabletoclinicalPI-RADSscoring.
Yang et al. (31) collected T
2
W and DWI sequences from prostate
MRI findings of 160 patients and built two parallel deep CNNs. The
final features extracted by these two CNNs were input into a
classifier based on the support vector machine algorithm,
ultimately achieving spontaneous identification of PCa (Table 1).
3.1.2 Segmentation of the prostate gland
The clinical measurement of prostate-specific antigen density
(PSA-D) is closely related to prostate volume (PV), and PSA-D
serves as an indicator of prostate cancer (PCa), with higher PSA-D
values suggesting a greater likelihood of clinically significant PCa
(100–102). PV is employed to diagnose BPH in clinical settings and
assists urologists in selecting suitable surgical procedures and
medication strategies for BPH patients (103–105). TRUS is the
most common imaging method for calculating PV in clinical
practice (106), but it is susceptible to significant measurement
errors when the prostate has an irregular shape. Computing PV
based on pixel size and layer thickness, in which the prostate gland
is segmented on each MRI image, may be more accurate. In a
clinical setting, determinization of the type of surgery, such as
prostate tissue-preserving surgery and fascial-sparing surgery
requires precise differentiation of prostate gland boundaries.
Preservation of the neurovascular bundle for performing the
nerve-sparing radical prostatectomy (107) to save the erectile
function and sparing of the pelvic fascia for fascial-sparing radical
prostatectomy (108) to prevent positive surgical margins followed
by high risk of clinical recurrences rely on preoperative imaging
guidance. In the case of PCa radiotherapy (discussed in more detail
later), precise MRI-guided segmentation in radiotherapy
significantly improves target accuracy, effectively prevents damage
to normal prostate tissues surrounding the tumor, and reduces toxic
side effects (109). Hence, accurate, robust, and efficient MRI-guided
segmentation of the prostate gland is crucial for evaluating PCa
tumors, calculating PV, selecting surgical options for prostate
abnormalities, outlining target areas for radiation planning, and
monitoring progressive changes in tumor lesions. However, due to
heterogeneity in MRI imaging quality and signal intensity, as well as
interference from periprostatic tissues and organs like the bladder
or rectum, prostate segmentation remains highly challenging (110,
TABLE 1 Currently available models of DL-mpMRI-based PCa detection or segmentation.
Author [Reference] Year Sample sizes MRI sequences Evaluation
Schelb P. et al. (83) 2019 250 + 62 T2, ADC, DWI
Xu H. et al. (84) 2019 346 T2, ADC 93.0% Accuracy; 0.95 AUC
Chen Y. et al. (85) 2020 136 T2, ADC
Arif M. et al. (86) 2020 292 T2, ADC 76.0% Accuracy; 0.89 AUC
Cao R. et al. (17) 2021 427 + 126 T2, ADC
Ushinsky A. et al. (87) 2021 287 T2 0.898 DSC
Bardis M. et al. (88) 2021 242 T2 0.940 DSC
Soerensen S.J.C. et al. (89) 2021 156 T2 0.92 ± 0.02 DSC
Soni M. et al. (90) 2022 140 T2, ADC 0.654 DSC, 0.695 sensitivity, 0.970 specificity
Li D. et al. (91) 2022 200 T2, DWI,
ADC 0.79 AUC
He et al. 10.3389/fonc.2023.1189370
Frontiers in Oncology frontiersin.org05

111). Applying DL for accurate prostate gland segmentation on
MRI images could facilitate more precise and easy determination of
PV and prostate boundaries. CNNs do not require complex feature
extraction and are widely utilized for medical image segmentation
(112,113). Zhu et al. (114) developed a three-dimensional (3D)
deep learning model containing dense blocks to segment the
prostate gland. The 3D structures enable the network to fully
exploit the relationship between adjacent images, and the dense
blocks make complete use of both shallow and deep information,
achieving a DSC of 0.82. Yan et al. (115) proposed a
backpropagation neural network that integrates the optimal
combination selected from multi-level feature extraction into a
single model for prostate MRI image segmentation, achieving a
DSC of 0.84, an average increase of 3.19% compared to traditional
ML segmentation algorithms based on random forests. To et al.
(116) segmented MRI images and identified PCa using a 3D deep
dense multipath CNN constructed from T
2
W and ADC sequences,
achieving DSCs of 0.95 and 0.89 in two independent test sets,
respectively. Dai et al. (117) developed a mask region-based CNN
for prostate gland and intraprostatic lesion segmentation, showing
that this end-to-end DL model could automatically segment the
prostate gland and identify suspicious lesions directly from prostate
MRI images without manual intervention, demonstrating its
potential to guide clinicians in tumor delineation.
3.2 Advanced radiotherapy of PCa
Radiotherapy is a vital component of PCa treatment and relies
on a complex series of multimodal medical imaging techniques,
such as computed tomography (CT), MRI, cone-beam CT, and
positron emission tomography, to localize tumors, establish
radiotherapy treatment plans, and assess radiotherapy efficacy
(118). Radiotherapy is an indispensable treatment modality for
cancer patients, either as neoadjuvant or postoperative therapy, in
combination with chemotherapy (119,120). The main goal of
radiotherapy is to maximize the therapeutic gain ratio by
delivering an effective radiation dose to the planned target volume
(PTV) and avoiding unnecessary radiation exposure to adjacent
healthy tissues and organs at risk (OARs) (121,122). However,
manual segmentation of the prostate gland, which is necessary for
accurate mapping of PTV and OARs, is prone to errors and can
result in less accurate and sensitive outcomes than those desired
clinically. In addition, respiratory movements, setup errors, and
fluctuations in body weight can lead to displacement of PTV and
OARs, potentially resulting in under-measurement of the radiation
dose received by the PTV or over-measurement of the radiation
dose delivered to OARs (123). To achieve precise tumor localization
and appropriate treatment, continuous technological advances have
led to the development of precision therapy, such as intensity-
modulated radiotherapy (IMRT) and 3D conformal radiation
therapy, which aim to provide personalized, precise anticancer
treatment by setting an appropriate radiation dose according to
the tumor shape while avoiding radiation exposure to OARs as
much as possible (124–126). Although precision therapy has
improved the accuracy of radiotherapy to some extent, further
optimization is necessary to achieve the desired efficacy. Therefore,
image-guided adaptive radiotherapy (ART) has emerged as a
potential solution to overcome PTV and OAR displacement
caused by various factors (123,127,128).
3.2.1 ART technology
ART technology allows for systematic monitoring of target
lesions and changes in adjacent tissues based on imaging features
to optimize radiotherapy plans further (123,128,129). ART enables
the acquisition of feedback and tracking of target area-related
information primarily through offline, online, and real-time
modes (129,130). For instance, offline ART involves measuring
setup errors on MRI images obtained during the patient’s initial few
treatments, after which the clinical target volume (CTV) coverage is
adjusted, and both the dose and treatment plan for subsequent
fractions are modified (130,131). Online ART calculates the
necessary data based on the patient’s anatomical imaging
information acquired at the time, allowing for modifications to
the radiotherapy plan that are directly applied to the current
treatment (129–131). Real-time ART involves intra-fraction and
inter-beam reprogramming and automatic adjustment of the
radiotherapy plan during treatment execution based on dynamic
tracking of radiation dose and anatomical details of the target area
without manual intervention (129–131). Since the anatomical and
geometric variations of PCa are influenced by the degree of bladder
and rectal filling, the morphology, location, and volume of PTV and
OARs may differ between treatments. Consequently, offline ART is
not flexible enough to accommodate these changes (131). While
CTV expansion is often used clinically to compensate for these
limitations, it can result in increased post-radiotherapy toxicities
(132). Although online ART offers improved accuracy compared to
offline ART, its time-consuming nature limits its clinical application
to some extent. Real-time ART overcomes the drawbacks of both
offline and online ART and has been implemented in clinical
practice (133), but its safety and robustness require validation due
to the lack of a sufficiently comprehensive database for model
adaptation and training (134). As mentioned in Section 3.1.2,
significant progress has been made in DL-based automatic
segmentation of the prostate gland. However, research on
developing subsequent radiotherapy systems remains
underexplored. Developing an accurate and efficient automated
radiotherapy delivery system using DL technology to enhance
radiotherapy outcomes has far-reaching clinical implications.
Sprouts et al. (135) developed a virtual treatment planner (VTP)
based on deep reinforcement learning (DRL) to implement a
treatment planning system. The VTP, based on the Q-learning
framework, was evaluated using 50 samples, achieving a mean
ProKnow plan score of 8.14 ± 1.27 (standard deviation),
indicating its potential for IMRT planning in PCa. The
application of the conventional ϵ-greedy algorithm for training
VTP is time-consuming, restricting its clinical use. Shen et al. (136)
introduced a knowledge-guided DRL to adjust treatment plan
parameters to enhance VTP training efficiency, achieving a plan
quality score of 8.82 ( ± 0.29). Lempart et al. (137) proposed a
densely connected DL model based on a modified U-Net, trained on
a triplet of 160 patients to predict dose distribution for volumetric-
He et al. 10.3389/fonc.2023.1189370
Frontiers in Oncology frontiersin.org06

modulated arc therapy. The model maintained the mean percentage
error within 1.9% for both CTV and PTV and within 2.6% for OAR,
demonstrating its capacity to partially automate the radiotherapy
planning process and accelerate treatment progress.
3.2.2 MRI-only radiotherapy
Although MRI offers excellent soft-tissue contrast and facilitates
relatively precise tumor segmentation, it does not provide the
electron density map or Hounsfield units needed for radiation
dose calculation. Consequently, it is essential to map relative
regions such as CTV, PTV, and OARs on MRI, after which the
outlined contours are mapped to CT via image alignment for
clinical radiotherapy planning (134,138,139). Combining MRI
and CT simulations in PCa radiotherapy plans may reduce acute
urogenital toxicity (140). However, the labor-intensive process of
CT and MRI alignment and the challenges in achieving full
alignment can result in systematic errors, potentially leading to
dose distribution issues in the target region and diminishing
radiotherapy effectiveness (141). To address these problems,
recently developed MRI simulators enable the conversion of MRI
data to synthetic CT (sCT), allowing radiation dose measurements
to directly contribute to radiotherapy planning and establishing
MRI-only radiotherapy (138,139,142)(Figure 3). Common
approaches for converting MRI data to sCT include bulk density
assignment–based methods, voxel-based methods, and atlas-based
methods (143,144). Currently, a bulk density assignment–based
system called magnetic resonance for calculating attenuation
(MRCAT™by Philips) and an atlas-based system called
MriPlanner™(by Spectronic Medical) have been employed for
automatic generation of pelvic sCT in clinical practice (145,146).
MriPlanner™has demonstrated promising performance, as
evaluated in the MR-OPERA and MR-PROTECT studies (147,
148). In contrast to sCT generation methods, MRCAT™, due to its
bulk density assignment–based nature, requires multiple MRI
sequences, such as air, liquid, and bone, each assigned to
corresponding electron density or Hounsfield unit values
necessary for creating a CT image (144,149)(Table 2). As
illustrated previously, the conventional approach to radiotherapy
planning, which involves the use of both CT and MRI, necessitates
manual intervention for aligning and fusing CT and MRI images, as
well as determining the CTV, PTV, and OARs. This manual
intervention considerably reduces both accuracy and efficiency.
However, by employing DL, which automatically extracts
informative features from a large number of training samples to
establish a nonlinear mapping from MRI to CT (150), a trained
model can swiftly generate highly precise synthetic CT (sCT)
images in just a few seconds. These sCT images provide more
accurate guidance for ART (151). Fu et al. (152) utilized two-
dimensional (2D) and three-dimensional (3D) fully connected
CNN based on U-Net to generate pelvic region sCT, with results
indicating that accurate sCT was effectively executed using DLA.
Conditional GAN, developed by adding a discriminator to U-Net,
enable the generated sCT to provide more details, enhancing sCT
accuracy and robustness and allowing for more precise
radiotherapy planning (153,154). CycleGAN (cGAN) is a
modified adversarial network based on GAN, with additional
generators and discriminators incorporated for improved
unpaired training data (155,156). Liu et al. (157) proposed a
multi-CycleGAN network and designed a new generator, Z-Net,
to improve anatomical details. This approach exhibited lower mean
error and mean absolute error and higher dose accuracy of the
sCT (Table 3).
FIGURE 3
Algorithm of CT+MRI and MRI-only radiotherapy technique. Created
with BioRender.com.
TABLE 2 MRCAT™(Magnetic Resonance for Calculating Attenuation) vs. MriPlanner™.
Magnetic Resonance for Calculating Attenuation MriPlanner™
Method Bulk density assignment-based Atlas-based
Region Pelvic region Pelvic region
MRI sequence engaged Multi-sequences needed (including air, liquid, and bone) A single sequence is sufficient
Accuracy determination Determined by the accuracy of segmentation Determined by the MRI-MRI alignment
Time required Longer Less
He et al. 10.3389/fonc.2023.1189370
Frontiers in Oncology frontiersin.org07

3.3 Prognostic assessment of PCa
To improve prognosis monitoring of PCa and reduce mortality,
it is essential to consider patients at low risk during active
surveillance and those who have undergone radical prostatectomy
(163). The European Association of Urology guidelines widely
recognize PSA as the primary metric for assessing BCR in clinical
practice (164). To mitigate diagnostic bias, leveraging the precise
anatomical information provided by MRI is invaluable, as it offers
non-invasive insights. This is particularly crucial since PSA levels
can fluctuate and be influenced by various factors (165,166).
Furthermore, the role of mpMRI in assessing PCa recurrence has
gained importance (167), underscoring the need for comprehensive
investigation into MRI-based PCa recurrence prediction. Yan et al.
(168) conducted a multicenter study using a DL technique and a
novel model called deep radiomic signature for BCR prediction.
They combined quantitative features and radiomics extracted from
prostate MRI with DL-based survival analysis. The performance of
the model was evaluated using data from approximately 600
patients who underwent radical prostatectomy, achieving
maximum AUC values of 0.85 and 0.88 for BCR-free survival
prediction at 3 years and 5 years, respectively. In addition to
recurrence prediction, thereshouldbeasignificant focus on
monitoring metastasis, particularly considering the high
occurrence rate of bone metastases in over 80% of patients with
advanced PCa (169). The accuracy and sensitivity of conventional
bone scintigraphy for detecting skeletal metastases have been
questioned (170). Therefore, of the potential in detecting earlier
PCa metastasis using PSMA PET-CT and MRI has been established
(170,171). As part of a routine radiological examination for
suspected PCa, Liu et al. successfully detected and segmented
pelvic bone metastases using dual 3D U-net DLAs rely on T
1
-
weighted imaging and diffusion-weighted imaging sequences (172).
Through two rounds of evaluation, they achieved a mean DSC value
above 0.85 for pelvic bone segmentation and a maximum AUC of
0.85 for metastasis detection, demonstrating accurate detection and
segmentation of pelvic bone metastases.
4 Discussion
Prostate MRI holds significant potential as a first-line diagnostic
and therapeutic approach for prostate gland abnormalities.
However, the broader application of prostate MRI is currently
limited for various reasons. CNN-based DL models have been
employed for fully automatic target segmentation. More
importantly, DLA can be easily applied to large-scale samples,
making them suitable for real-world clinical practice. In addition
to their utility in detecting and segmenting lesions on prostate MRI,
DLA have a wide range of applications. Presently, some studies have
demonstrated the potential applications of DL in multiple areas. A
few studies have employed DL to predict the Gleason score of PCa
by using DLAs to assess pathological sections, which demonstrated
diagnostic power equal to that of pathologists (173–175). DL has
also yielded satisfactory results in prostate gland segmentation on
TRUS images (176–178). In radiotherapy, prostate gland and
adjacent organ contouring based on DL auto-contouring
algorithms may reduce workload and inter-observer variability, as
evidenced by several clinical evaluations conducted at different
radiotherapy centers (179,180). Additionally, using DLA for
detecting and tracking marker seeds during PCa treatment
enhances precise target dosage delivery and minimizes radiation-
induced adverse events in normal tissues surrounding the tumor
(181,182). Remarkably, DL has been employed for PI-RADS
scoring based on mpMRI of the prostate gland in real-world
settings, yielding results similar to PI-RADS scores determined by
radiologist experts (83). In recent times, there has been notable
progress in integrating DL with nomograms. This integration
enables the inclusion of crucial variables such as PSA, PV, patient
age, free/total PSA ratio, and PSA-D into the diagnostic process of
PCa using MRI data (183,184).
4.1 Limitations and outlook
The research examined in this review highlights the significant
potential and wide-ranging prospects of DL applications. Future
studies should concentrate on employing DL in prostate MRI for in-
depth understanding. Firstly, there is a strong demand for 3D
information processing. Presently, most available DLAs rely on
2D images for feature extraction and analysis, indicating that these
DLAs may not be suitable for extracting 3D spatial anatomical
information from clinically obtained patient images. Although DL
has been employed for segmenting 3D medical images of the liver
and cardiovascular system (185), there is a scarcity of research on
using DL for segmenting 3D images of the prostate gland,
TABLE 3 Recent designed models of deep learning–based synthetic CT generation in the prostate gland or pelvic region.
Model Author, Reference Year Sample size Target Time of sCT generation (s)
U-net Chen et al. (158) 2018 36 Prostate gland 3.8–7.7
U-net Arabi et al. (159) 2018 30 Pelvic region 9
cGAN Nie et al. (160) 2018 21 Pelvic region 30
cGAN Maspero et al. (161) 2018 32 Pelvic region 5.6
U-net Fu et al. (152) 2019 16 Prostate gland 5.5
cGAN Gusumano et al. (162) 2020 40 Pelvic region 175 ± 43
He et al. 10.3389/fonc.2023.1189370
Frontiers in Oncology frontiersin.org08

necessitating further evaluation. Developing computational
segmentation methods appropriate for 3D medical images while
preserving the high performance of DL models for PCa detection
and diagnosis remains highly challenging. In addition, future
research should continually extend to multimodal and
multisequence data analyses. Currently, most prostate MRI
studies include only T
2
W and DWI sequences. Despite the
diminishing role of DCE according to recent PI-RADS guidelines
(186), incorporating ADC into the analysis and fusing multiple
modalities of feature descriptions for 3D tumor image segmentation
may further enhance the accuracy of CNN in identifying PCa.
Furthermore, the effectiveness of using DCE sequence in detecting
PCa is still debated, given its time-consuming nature and the
associated risk of nephrogenic systemic fibrosis (187,188).
Therefore, focusing on biparametric MRI, which assesses only
T
2
W and DWI sequences, should be prioritized for rapid
screening. Improving CNN architectures may also enhance the
computational capabilities of DLAs. Based on cumulative findings,
we propose that parallelizing sub-networks analyzing different
sequences and then inputting the final result into the classifier or
connecting various sub-networks in series and generating the final
result directly could yield promising outcomes. Utilizing diverse
DLAs, developed by modifying neural network architectures, can
further improve detection effectiveness. One primary limitation of
DL, not only in medical image processing but also in other
professional fields, is the incomprehensibility and lack of
interpretability of predictions and decisions made by DLAs (189–
191). This becomes critically important in cases where DL-based
decisions can result in significant consequences, particularly in
medical and biological contexts (189,191). To prevent
misdiagnosis and mistreatment that may lead to life-threatening
conditions, the rationale and evidence for DL-provided conclusions
must be clarified. Developing “explainable AI”(XAI) for accurate
predictions with understandable assessment criteria should be
further investigated as a future direction. More ambitiously,
comprehending complex biological contexts, such as molecular
mechanisms, genetic expression, and cellular microenvironments,
is crucial for developing novel biomarkers, discovering disease
pathogenesis, proposing new treatment strategies, and evaluating
analytical approach performance (192,193). All these
advancements necessitate updating CNN architectures to not only
make predictions based on data-driven DL approaches but also
learn the biological mechanisms behind the data by integrating
biological knowledge into the learning process (193,194). A new
concept called digital biopsy involves analyzing digital images and
identifying characteristic features focusing on tumor heterogeneity
rather than its contour, using computer power from multi-omics to
aid in diagnosing or predicting various diseases (195). Investigating
DL-based digital biopsy techniques will significantly contribute to
assessing and predicting diseases non-invasively, making it a
valuable tool in clinical settings. Digital biopsy holds considerable
potential to become the “next-generation biopsy”for patients with
low risk PCa, substantially benefiting healthcare.
5 Conclusion
DLAs have shown promising results in tumor identification
and detection, lesion segmentation, PCa grading and scoring, as
well as postoperative recurrence and prognostic outcome
prediction, making them gain significant attention and play
important roles in urology. However, the diagnostic accuracy of
DL models still has room for improvement, and the amount of
annotated sample data used is relatively limited. Therefore,
optimization of models and algorithms, expansion of medical
database resources, and combination of multi-omics data and
comprehensive analysis of various morphological data will
enhance the usefulness of DL for the diagnosis and treatment of
urological diseases. Additionally, continued exploration in
developing explainable AI will bring greater transparency and
trustworthiness to DL. Undoubtedly, DL has shown a steep
learning curve in the interpretation of prostate MRI (196), and
its advent will benefit not only radiologists but also general
physicians who lack systematic and specialized imaging
interpretation training in terms of imaging evaluation of
prostatic diseases. We believethatwithadvancementsin
technology and research, a significant leap in DLA development
would occur, which would be beneficial in PCa diagnosis
and treatment.
Author contributions
MH performed the literature search regarding the available
databases and drafted the manuscript. YC, XY and AK helped in
consulting the relevant literature. RR, SW assisted in implementing
images. OM, LZ and GY polished the manuscript. CC evaluated and
reinfored the technical background. KH, ME contributed to editing
the manuscript. All authors contributed to the article and approved
the submitted version.
Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed or
endorsed by the publisher.
He et al. 10.3389/fonc.2023.1189370
Frontiers in Oncology frontiersin.org09

References
1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global
cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for
36 cancers in 185 countries. CA: Cancer J Clin (2021) 71(3):209–49. doi: 10.3322/
caac.21660
2. Song JM, Kim CB, Chung HC, Kane RL. Prostate-specific antigen, digital rectal
examination and transrectal ultrasonography: a meta-analysis for this diagnostic triad
of prostate cancer in symptomatic korean men. Yonsei Med J (2005) 46(3):414–24.
doi: 10.3349/ymj.2005.46.3.414
3. Moe A, Hayne D. Transrectal ultrasound biopsy of the prostate: does it still have a
role in prostate cancer diagnosis? Trans Androl Urol (2020) 9(6):3018–24.
doi: 10.21037/tau.2019.09.37
4. Numao N, Yoshida S, Komai Y, Ishii C, Kagawa M, Kijima T, et al. Usefulness of
pre-biopsy multiparametric magnetic resonance imaging and clinical variables to
reduce initial prostate biopsy in men with suspected clinically localized prostate
cancer. J Urol (2013) 190(2):502–8. doi: 10.1016/j.juro.2013.02.3197
5. Borghesi M, Bianchi L, Barbaresi U, Vagnoni V, Corcioni B, Gaudiano C, et al.
Diagnostic performance of MRI/TRUS fusion-guided biopsies vs. systematic prostate
biopsies in biopsy-naïve, previous negative biopsy patients and men undergoing active
surveillance. Nephrology (2021) 73(3):357–66.
6. Chen FK, de Castro Abreu AL, Palmer SL. Utility of ultrasound in the diagnosis,
treatment, and follow-up of prostate cancer: state of the art. J Nucl Med (2016) 57(Suppl
3):13s–8s. doi: 10.2967/jnumed.116.177196
7. Pecoraro M, Messina E, Bicchetti M, Carnicelli G, Del Monte M, Iorio B, et al.
The future direction of imaging in prostate cancer: MRI with or without contrast
injection. Andrology (2021) 9(5):1429–43. doi: 10.1111/andr.13041
8. Bittencourt LK, Hausmann D, Sabaneeff N, Gasparetto EL, Barentsz JO.
Multiparametric magnetic resonance imagin g of the prostate: curren t concepts.
Radiologia brasileira (2014) 47(5):292–300. doi: 10.1590/0100-3984.2013.1863
9. Würnschimmel C, Chandrasekar T, Hahn L, Esen T, Shariat SF, Tilki D. MRI As
a screening tool for prostate cancer: current evidence and future challenges. World J
Urol (2023) 41(4):921–928. doi: 10.1007/s00345-022-03947-y
10. Rouvière O, Puech P, Renard-Penna R, Claudon M, Roy C, Mège-Lechevallier F,
et al. Use of prostate systematic and targeted biopsy on the basis of multiparametric MRI
in biopsy-naive patients (MRI-FIRST): a prospective, multicentre, paired diagnostic study.
Lancet Oncol (2019) 20(1):100–9. doi: 10.1016/s1 470-2045 (18)30569-2
11. Mastrogiacomo S, Dou W, Jansen JA, Walboomers XF. Magnetic resonance
imaging of hard tissues and hard tissue engineered bio-substitutes. Mol Imaging Biol
(2019) 21(6):1003–19. doi: 10.1007/s11307-019-01345-2
12. Turkbey B, Rosenkrantz AB, Haider MA, Padhani AR, Villeirs G, Macura KJ,
et al. Prostate imaging reporting and data system version 2.1: 2019 update of prostate
imaging reporting and data system version 2. Eur Urol (2019) 76(3):340–51.
doi: 10.1016/j.eururo.2019.02.033
13. van der Leest M, Cornel E, Israël B, Hendriks R, Padhani AR, Hoogenboom M,
et al. Head-to-head comparison of transrectal ultrasound-guided prostate biopsy versus
multiparametric prostate resonance imaging with subsequent magnetic resonance-
guided biopsy in biopsy-naïve men with elevated prostate-specific antigen: a Large
prospective multicenter clinical study. Eur Urol (2019) 75(4):570–8. doi: 10.1016/
j.eururo.2018.11.023
14. Gupta RT, Mehta KA, Turkbey B, Verma S. PI-RADS: past, present, and future. J
Magn Reson Imaging: JMRI (2020) 52(1):33–53. doi: 10.1002/jmri.26896
15. Ahmed HU, El-Shater Bosaily A, Brown LC, Gabe R, Kaplan R, Parmar MK,
et al. Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer
(PROMIS): a paired validating confirmatory study. Lancet (London England) (2017)
389(10071):815–22. doi: 10.1016/s0140-6736(16)32401-1
16. Song Y, Zhang YD, Yan X, Liu H, Zhou M, Hu B, et al. Computer-aided
diagnosis of prostate cancer using a deep convolutional neural network from
multiparametric MRI. JMagnResonImaging:JMRI(2018) 48(6):1570–7.
doi: 10.1002/jmri.26047
17. Cao R, Zhong X, Afshari S, Felker E, Suvannarerg V, Tubtawee T, et al.
Performance of deep learning and genitourinary radiologists in detection of prostate
cancer using 3-T multiparametric magnetic resonance imaging. J Magn Reson Imaging:
JMRI (2021) 54(2):474–83. doi: 10.1002/jmri.27595
18. Sanders JW, Mok H, Hanania AN, Venkatesan AM, Tang C, Bruno TL, et al.
Computer-aided segmentation on MRI for prostate radiotherapy, part I: quantifying
human interobserver variability of the prostate and organs at risk and its impact on
radiation dosimetry. Radiother oncol: J Eur Soc Ther Radiol Oncol (2022) 169:124–31.
doi: 10.1016/j.radonc.2021.12.011
19. Ahsan MM, Luna SA, Siddique Z. Machine-Learning-Based disease diagnosis: a
comprehensive review. Healthcare (Basel Switzerland) (2022) 10(3):541. doi: 10.3390/
healthcare10030541
20. Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Annu Rev
Biomed Engineering (2017) 19:221–48. doi: 10.1146/annurev-bioeng-071516-044442
21.KumarY,KoulA,SinglaR,IjazMF.Artificial intelligence in disease
diagnosis: a systematic literature review, synthesizing framework and future
research ag enda. J Ambient Intell Humaniz Comput (2022) 13:1–28. doi: 10.1007/
s12652-021-03612-z
22. Sebastian AM, Peter D. Artificial intelligence in cancer research: trends,
challenges and future directions. Life (Basel Switzerland) (2022) 12(12):1991.
doi: 10.3390/life12121991
23. Lemaitre G. Computer-aided diagnosis for prostate cancer using multi-
parametric magnetic resonance imaging [Doctoral dissertation]. France: Universitat
de Girona (2016). Available at: https://www.researchgate.net/profile/Guillaume-
Lemaitre-2/publication/312063690_Computer-Aided_Diagnosis_for_Prostate_
Cancer_using_Multi-Parametric_Magnetic_Resonance_Imaging/links/
58710e0108ae8fce491ede41/Computer-Aided-Diagnosis-for- Prostate-Cancer-using-
Multi-Parametric-Magnetic-Resonance-Imaging.pdf.
24. Wang S, Burtt K, Turkbey B, Choyke P, Summers RM. Computer aided-
diagnosis of prostate cancer on multiparametric MRI: a technical review of current
research. BioMed Res Int (2014) 2014:789561. doi: 10.1155/2014/789561
25. Fehr D, Veeraraghavan H, Wibmer A, Gondo T, Matsumoto K, Vargas HA,
et al. Automatic classification of prostate cancer Gleason scores from multiparametric
magnetic resonance images. Proc Natl Acad Sci USA (2015) 112(46):E6265–73.
doi: 10.1073/pnas.1505935112
26. Lemaitre G, Marti R, Rastgoo M, Meriaudeau F. (2017). Computer-aided
detection for prostate cancer detection based on multi-parametric magnetic
resonance imaging. Annu Int Conf IEEE Eng Med Biol Soc (2017) 2017:3138–41.
doi: 10.1109/embc.2017.8037522
27. Firmino M, Angelo G, Morais H, Dantas MR, Valentim R. Computer-aided
detection (CADe) and diagnosis (CADx) system for lung cancer with likelihood of
malignancy. Biomed Eng Online (2016) 15(1):2. doi: 10.1186/s12938-015-0120-7
28. Chan HP, Hadjiiski LM, Samala RK. Computer-aided diagnosis in the era of
deep learning. Med Physics (2020) 47(5):e218–27. doi: 10.1002/mp.13764
29. Liu L, Tian Z, Zhang Z, Fei B. Computer-aided detection of prostate cancer with
MRI: technology and applications. Acad Radiol (2016) 23(8):1024–46. doi: 10.1016/
j.acra.2016.03.010
30. Telecan T, Andras I, Crisan N, Giurgiu L, Cata ED, Caraiani C, et al. More than
meets the eye: using textural analysis and artificial intelligence as decision support tools
in prostate cancer Diagnosis—A systematic review. J Pers Med (2022) 12(6):983.
doi: 10.3390/jpm12060983
31.YangX,LiuC,WangZ,YangJ,MinHL,WangL,etal.Co-Trained
convolutional neural networks for automated detection of prostate cancer in multi-
parametric MRI. Med Image Anal (2017) 42:212–27. doi: 10.1016/j.media.2017.08.006
32. Hassanzadeh E, Glazer DI, Dunne RM, Fennessy FM, Harisinghani MG,
Tempany CM. Prostate imaging reporting and data system version 2 (PI-RADS v2):
a pictorial review. Abdominal Radiol (New York) (2017) 42(1):278–89. doi: 10.1007/
s00261-016-0871-z
33. Barentsz JO, Weinreb JC, Verma S, Thoeny HC, Tempany CM, Shtern F, et al.
Synopsis of the PI-RADS v2 guidelines for multiparametric prostate magnetic
resonance imaging and recommendations for use. Eur Urol (2016) 69(1):41–9.
doi: 10.1016/j.eururo.2015.08.038
34. Park BK, Park SY. New biopsy techniques and imaging features of transrectal
ultrasound for targeting PI-RADS 4 and 5 lesions. JClinMed(2020) 9(2):530.
doi: 10.3390/jcm9020530
35. Weinreb JC, Barentsz JO, Choyke PL, Cornud F, Haider MA, Macura KJ, et al.
And data system: 2015, version 2. Eur Urol (2016) 69(1):16–40. doi: 10.1016/
j.eururo.2015.08.052
36. Brembilla G, Dell’Oglio P, Stabile A, Damascelli A, Brunetti L, Ravelli S, et al.
Interreader variability in prostate MRI reporting using prostate imaging reporting and
data system version 2.1. Eur Radiol (2020) 30(6):3383–92. doi: 10.1007/s00330-019-
06654-2
37. Smith CP, Harmon SA, Barrett T, Bittencourt LK, Law YM, Shebel H, et al.
Intra- and interreader reproducibility of PI-RADSv2: a multireader study. J Magn
Reson Imaging: JMRI (2019) 49(6):1694–703. doi: 10.1002/jmri.26555
38. Barragan-Montero A, Javaid U, Valdes G, Nguyen D, Desbordes P, Macq B,
et al. Artificial intelligence and machine learning for medical imaging: a technology
review. Physica medica: PM: an Int J devoted to Appl Phys to Med biol: Off J Ital Assoc
Biomed Phys (AIFB) (2021) 83:242–56. doi: 10.1016/j.ejmp.2021.04.016
39. Yang R, Yu Y. Artificial convolutional neural network in object detection and
semantic segmentation for medical imaging analysis. Front Oncol (2021) 11:638182.
doi: 10.3389/fonc.2021.638182
40. Jiang T, Gradus JL, Rosellini AJ. Supervised machine learning: a brief primer.
Behav Ther (2020) 51(5):675–87. doi: 10.1016/j.beth.2020.05.002
41. Wallin E, Svensson L, Kahl F, Hammarstrand L. DoubleMatch: improving semi-
supervised learning with self-supervision. 26th International Conference on Pattern
Recognition (ICPR). (2022) 2871–7.
42. Chapelle O, Schölkopf B, Zien A. Semi-supervised learning. IEEE Transactions
on Neural Networks (2009) 20(3):542.
He et al. 10.3389/fonc.2023.1189370
Frontiers in Oncology frontiersin.org10

43. Hu J, Niu H, Carrasco J, Lennox B, Arvin F. Voronoi-based multi-robot
autonomous exploration in unknown environments via deep reinforcement learning.
IEEE Trans Vehicular Technol (2020) 69(12):14413–23. doi: 10.1109/TVT.2020.3034800
44. Hinton GE, Osindero S, Teh YW. A fast learning algorithm for deep belief nets.
Neural computation (2006) 18(7):1527–54. doi: 10.1162/neco.2006.18.7.1527
45. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature (2015) 521(7553):436–44.
doi: 10.1038/nature14539
46. Huang X, Li Z, Zhang M, Gao S. Fusing hand-crafted and deep-learning features
in a convolutional neural network model to identify prostate cancer in pathology
images. Front Oncol (2022) 12:994950. doi: 10.3389/fonc.2022.994950
47. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A
survey on deep learning in medical image analysis. Med Image Anal (2017) 42:60–88.
doi: 10.1016/j.media.2017.07.005
48. Lee H, Hong H, Kim J, Jung DC. Deep feature classification of angiomyolipoma
without visible fat and renal cell carcinoma in abdominal contrast-enhanced CT images
with texture image patches and hand-crafted feature concatenation. Med Physics (2018)
45(4):1550–61. doi: 10.1002/mp.12828
49. Li Z, Wu F, Hong F, Gai X, Cao W, Zhang Z, et al. Computer-aided diagnosis of
spinal tuberculosis from CT images based on deep learning with multimodal feature
fusion. Front Microbiol (2022) 13:823324. doi: 10.3389/fmicb.2022.823324
50. Zeng X, Huang R, Zhong Y, Xu Z, Liu Z, Wang Y. A reciprocal learning strategy
for semisupervised medical image segmentation. Med Physics (2023) 50(1):163–77.
doi: 10.1002/mp.15923
51. Iglesias G, Talavera E, Dı

az-A

lvarez A. A survey on GANs for computer vision:
recent research, analysis and taxonomy. Comput Sci Rev (2023) 48:100553.
doi: 10.1016/j.cosrev.2023.100553
52. Sarıgül M, Ozyildirim BM, Avci M. Differential convolutional neural network.
Neural Networks (2019) 116:279–87. doi: 10.1016/j.neunet.2019.04.025
53. Koshino K, Werner RA, Pomper MG, Bundschuh RA, Toriumi F, Higuchi T,
et al. Narrative review of generative adversarial networks in medical and molecular
imaging. Ann Trans Med (2021) 9(9):821. doi: 10.21037/atm-20-6325
54. Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts H. Artificial
intelligence in radiology. Nat Rev Cancer (2018) 18(8):500–10. doi: 10.1038/s41568-
018-0016-5
55. Cuocolo R, Cipullo MB, Stanzione A, Ugga L, Romeo V, Radice L, et al. Machine
learning applications in prostate cancer magnetic resonance imaging. Eur Radiol
Experimental (2019) 3(1):35. doi: 10.1186/s41747-019-0109-2
56. Rickman J, Struyk G, Simpson B, Byun BC, Papanikolopoulos N. The growing
role for semantic segmentation in urology. Eur Urol Focus (2021) 7(4):692–5.
doi: 10.1016/j.euf.2021.07.017
57. Ahmed M, Hashmi KA, Pagani A, Liwicki M, Stricker D, Afzal MZ. And
performance analysis of deep learning based object detection in challenging
environments. Sensors (Basel Switzerland) (2021) 21(15):5116. doi: 10.3390/s21155116
58. Anwar SM, Majid M, Qayyum A, Awais M, Alnowami M, Khan MK. Medical
image analysis using convolutional neural networks: a review. J Med Systems (2018) 42
(11):226. doi: 10.1007/s10916-018-1088-1
59. Puttagunta M, Ravi S. Medical image analysis based on deep learning approach.
Multimedia Tools Appl (2021) 80(16):24365–98. doi: 10.1007/s11042-021-10707-4
60. Yamashita R, Nishio M, Do RKG, Togashi K. Convolutional neural networks: an
overview and application in radiology. Insights into Imaging (2018) 9(4):611–29.
doi: 10.1007/s13244-018-0639-9
61. Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, et al.
Review of deep learning: concepts, CNN architectures, challenges, applications, future
directions. J Big Data (2021) 8(1):53. doi: 10.1186/s40537-021-00444-8
62. Min S, Lee B, Yoon S. Deep learning in bioinformatics. Briefings Bioinf (2017) 18
(5):851–69. doi: 10.1093/bib/bbw068
63. Michaely HJ, Aringhieri G, Cioni D, Neri E. Current value of biparametric
prostate MRI with machine-learning or deep-learning in the detection, grading, and
characterization of prostate cancer: a systematic review. Diagnostics (Basel Switzerland)
(2022) 12(4):799. doi: 10.3390/diagnostics12040799
64. Yi Z. Researches advanced in image recognit ion based on deep learning.
Highlights Science Eng Technol (2023) 39:1309–16. doi: 10.54097/hset.v39i.6760
65. Li Z, Wang Y, Zhang N, Zhang Y, Zhao Z, Xu D, et al. Deep learning-based
object detection techniques for remote sensing images: a survey. Remote Sens (2022) 14
(10):2385. doi: 10.3390/rs14102385
66. Li M, Zhang Z, Lei L, Wang X, Guo X. Agricultural greenhouses detection in
high-resolution satellite images based on convolutional neural networks: comparison of
faster r-CNN, YOLO v3 and SSD. Sensors (Basel Switzerland) (2020) ;20(17):4938.
doi: 10.3390/s20174938
67. Sumit SS, Watada J, Roy A, Rambli DRA. In object detection deep learning
methods, YOLO shows supremum to mask r-CNN. J Physics: Conf Ser (2020) 1529(4).
doi: 10.1088/1742-6596/1529/4/042086
68. Zanaty E, Abdel-Aty MMJI. Comparing U-net convolutional network with mask
r-CNN in nuclei segmentation. IJCSNS Int J Comput Sci Netw Secur (2022) 22(3):273.
69. Li C, Liu H. Medical image segmentation with generative adversarial semi-
supervised network. Phys Med Biol (2021) 66(24). doi: 10.1088/1361-6560/ac3d15
70. Isola P, Zhu J-Y, Zhou T, Efros A. Image-to-Image translation with conditional
adversarial networks. arXiv (2017), 5967. doi: 10.48550/arXiv.1611.07004
71. Ho J, Ermon S. Generative adversarial imitation learning. Advances in neural
information processing systems (2016) 29.
72. Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al.
Generative adversarial nets. NIPS (2014).
73. Mohammadi B, Sabokrou M. End-to-End adversarial learning for intrusion
detection in computer networks. (2019). doi: 10.1109/LCN44214.2019.8990759
74. Laddha S, Kum ar V. DGCNN: deep convolutional generative adversarial
network based convolutional neural network for diagnosis of COVID-19.
Multimedia Tools Appl (2022) 81(22):31201–18. doi: 10.1007/s11042-022-12640-6
75.RadfordA,MetzL,ChintalaS.Unsupervised representation learning with deep
convolutional generative adversarial network. sarXiv. (2015). doi: 10.48550/arXiv.1511.06434
76. Wang Y, Zhou L, Wang M, Shao C, Shi L, Yang S, et al. Combination of
generative ad versarial network and convolutional neural network for automatic
subcentimeter pulmonary adenocarcinoma classification. Quantitative Imaging Med
surgery (2020) 10(6):1249–64. doi: 10.21037/qims-19-982
77. Shreffler J, Huecker MR. Diagnostic testing accuracy: sensitivity, specificity,
predictive values and likelihood ratios. In: StatPearls. Treasure Island (FL: StatPearls
Publishing Copyright ©2023, StatPearls Publishing LLC (2023).
78. Eelbode T, Bertels J, Berman M, Vandermeulen D, Maes F, Bisschops R, et al.
Optimization for medical image segmentation: theory and practice when evaluating
with dice score or jaccard index. IEEE Trans Med Imaging (2020) 39(11):3679–90.
doi: 10.1109/tmi.2020.3002417
79. Park SH, Goo JM, Jo CH. Receiver operating characteristic (ROC) curve: a
practical review for radiologists. Korean J Radiol (2004) 5(1):11–8. doi: 10.3348/
kjr.2004.5.1.11
80. Nahm FS. Receiver operating characteristic curve: overview and practical use for
clinicians. Korean J anesthesiol (2022) 75(1):25–36. doi: 10.4097/kja.21209
81. Li F, He H. Assessing the accuracy of diagnostic tests. Shanghai Arch Psychiatry
(2018) 30(3):207–12. doi: 10.11919/j.issn.1002-0829.218052
82. Yeung M, Rundo L, Nan Y, Sala E, Schönlieb C-B, Yang G. Calibrating the dice
loss to handle neural network overconfidence for biomedical image segmentation. J
Digital Imaging (2023) 36(2):739–52. doi: 10.1007/s10278-022-00735-3
83. Schelb P, Kohl S, Radtke JP, Wiesenfarth M, Kickingereder P, Bickelhaupt S,
et al. Classification of cancer at prostate MRI: deep learning versus clinical PI-RADS
assessment. Radiology (2019) 293(3):607–17. doi: 10.1148/radiol.2019190938
84. Xu H, Baxter JSH, Akin O, Cantor-Rivera D. Prostate cancer detection using
residual networks. Int J Comput assisted Radiol surgery (2019) 14(10):1647–50.
doi: 10.1007/s11548-019-01967-5
85.ChenY,XingL,YuL,BagshawHP,BuyyounouskiMK,HanB.Automatic
intraprostatic lesion segmentation in multiparametric magnetic resonance images with
proposed multiple branch UNet. Med Physics (2020) 47(12):6421–9. doi: 10.1002/mp.14517
86. Arif M, Schoots IG, Castillo Tovar J, Bangma CH, Krestin GP, Roobol MJ, et al.
Clinically significant prostate cancer detection and segmentation in low-risk patients
using a convolutional neural network on multi-parametric MRI. Eur Radiol (2020) 30
(12):6582–92. doi: 10.1007/s00330-020-07008-z
87. Ushinsky A, Bardis M, Glavis-Bloom J, Uchio E, Chantaduly C, Nguyentat M,
et al. A 3D-2D hybrid U-net convolutional neural network approach to prostate organ
segmentation of multiparametric MRI. AJR Am J Roentgenol (2021) 216(1):111–6.
doi: 10.2214/ajr.19.22168
88. Bardis M, Houshyar R, Chantaduly C, Tran-Harding K, Ushinsky A, Chahine C,
et al. Segmentation of the prostate transition zone and peripheral zone on MR images
with deep learning. Radiol Imaging Cancer (2021) 3(3):e200024. doi: 10.1148/
rycan.2021200024
89. Soerensen SJC, Fan RE, Seetharaman A, Chen L, Shao W, Bhattacharya I, et al.
Deep learning improves speed and accuracy of prostate gland segmentations on
magnetic resonance imaging for targeted biopsy. JUrol(2021) 206(3):604–12.
doi: 10.1097/ju.0000000000001783
90. Soni M, Khan IR, Babu KS, Nasrullah S, Madduri A, Rahin SA. Light weighted
healthcare CNN model to detect prostate cancer on multiparametric MRI. Comput
Intell Neurosci (2022) 2022:5497120. doi: 10.1155/2022/5497120
91. Li D, Han X, Gao J, Zhang Q, Yang H, Liao S, et al. Deep learning in prostate
cancer diagnosis using multiparametric magnetic resonance imaging with whole-
mount histopathology referenced delineations. Front Med (2021) 8:810995.
doi: 10.3389/fmed.2021.810995
92. Boehm K, Borgmann H, Ebert T, Höfner T, Khaljani E, Schmid M, et al. Stage
and grade migration in prostate cancer treated with radical prostatectomy in a Large
German multicenter cohort. Clin Genitourinary Cancer (2021) 19(2):162–166.e1.
doi: 10.1016/j.clgc.2020.12.004
93. Moore CM, Giganti F, Albertsen P, Allen C, Bangma C, Briganti A, et al.
Reporting magnetic resonance imaging in men on active surveillance for prostate
cancer: the PRECISE recommendations-a report of a European school of oncology task
force. Eur Urol (2017) 71(4):648–55. doi: 10.1016/j.eururo.2016.06.011
94. Fusco R, Sansone M, Granata V, Setola SV, Petrillo A. A systematic review on
multiparametric MR imaging in prostate cancer detection. Infect Agents Cancer (2017)
12:57. doi: 10.1186/s13027-017-0168-z
He et al. 10.3389/fonc.2023.1189370
Frontiers in Oncology frontiersin.org11

95. Vente C, Vos P, Hosseinzadeh M, Pluim J, Veta M. Deep learning regression for
prostate cancer detection and grading in bi-parametric MRI. IEEE Trans Bio-medical
Engineering (2021) 68(2):374–83. doi: 10.1109/tbme.2020.2993528
96. Wang Z, Liu C, Cheng D, Wang L, Yang X, Cheng KT. Automated detection of
clinically significant prostate cancer in mp-MRI images based on an end-to-End deep
neural network. IEEE Trans Med Imaging (2018) 37(5):1127–39. doi: 10.1109/
tmi.2017.2789181
97. Ishioka J, Matsuoka Y, Uehara S, Yasuda Y, Kijima T, Yoshida S, et al.
Computer-aided diagnosis of prostate cancer on magnetic resonance imaging using a
convolutional neural network algorithm. BJU Int (2018) 122(3):411–7. doi: 10.1111/
bju.14397
98. Wang X, Yang W, Weinreb J, Han J, Li Q, Kong X, et al. Searching for prostate
cancer by fully automated magnetic resonance imaging classification: deep learning
versus non-deep learning. Sci Rep (2017) 7(1):15415. doi: 10.1038/s41598-017-15720-y
99. Sanford T, Harmon SA, Turkbey EB, Kesani D, Tuncer S, Madariaga M, et al.
Deep-Learning-Based artificial intelligence for PI-RADS classification to assist
multiparametric prostate MRI interpretation: a development study. J Magn Reson
Imaging: JMRI (2020) 52(5):1499–507. doi: 10.1002/jmri.27204
100. Omri N, Kamil M, Alexander K, Alexander K, Edmond S, Ariel Z, et al.
Association between PSA density and pathologically significant prostate cancer: the
impact of prostate volume. Prostate (2020) 80(16):1444–9. doi: 10.1002/pros.24078
101. Feng ZJ, Xue C, Wen JM, Li Y, Wang M, Zhang N. PSAD test in the diagnosis
of prostate cancer: a meta-analysis. Clin Laboratory (2017) 63(1):147–55. doi: 10.7754/
Clin.Lab.2016.160727
102. Jue JS, Barboza MP, Prakash NS, Venkatramani V, Sinha VR, Pavan N, et al.
Re-examining prostate-specific antigen (PSA) density: defining the optimal PSA range
and patients for using PSA density to predict prostate cancer using extended template
biopsy. Urology (2017) 105:123–8. doi: 10.1016/j.urology.2017.04.015
103. Zoltan E, Lee R, Staskin DR, Te AE, Kaplan SA. Combination therapy for
benign prostatic hyperplasia: does size matter? Curr Bladd Dysfunct Rep (2008) 3
(2):102–8. doi: 10.1007/s11884-008-0016-5
104. Milonas D, Trumbeckas D, Juska P. The importance of prostatic measuring by
transrectal ultrasound in surgical management of patients with clinically benign
prostatic hyperplasia. Medicina (Kaunas Lithuania) (2003) 39(9):860–6.
105. Garvey B, Türkbey B, Truong H, Bernardo M, Periaswamy S, Choyke PL.
Clinical value of prostate segmentation and volume determination on MRI in benign
prostatic hyperplasia. Diagn Interventional Radiol (Ankara Turkey) (2014) 20(3):229–
33. doi: 10.5152/dir.2014.13322
106. Harvey CJ, Pilcher J, Richenberg J, Patel U, Frauscher F. Applications of
transrectal ultrasound in prostate cancer. Br J Radiol (2012) 85 Spec No 1(Spec Iss 1):
S3–17. doi: 10.1259/bjr/56357549
107. Walz J, Epstein JI, Ganzer R, Graefen M, Guazzoni G, Kaouk J, et al. A critical
analysis of the current knowledge of surgical anatomy of the prostate related to
optimisation of cancer control and preservation of continence and erection in
candidates for radical prostatectomy: An update. Eur Urol (2016) 70(2):301–11.
doi: 10.1016/j.eururo.2016.01.026
108. Hoeh B, Wenzel M, Hohenhorst L, Köllermann J, Graefen M, Haese A, et al.
Anatomical fundamentals and current surgical knowledge of prostate anatomy related
to functional and oncological outcomes for robotic-assisted radical prostatectomy.
Front Surg (2022) 8:825183. doi: 10.3389/fsurg.2021.825183.
109. Munck Af Rosenschold P, Zelefsky MJ, Apte AP, Jackson A, Oh JH, Shulman
E, et al. Image-guided radiotherapy reduces the risk of under-dosing high-risk prostate
cancer extra-capsular disease and improves biochemical control. Radiat Oncol (London
England) (2018) 13(1):64. doi: 10.1186/s13014-018-0978-1
110. Murphy G, Haider M, Ghai S, Sreeharsha B. The expanding role of MRI in
prostate cancer. AJR Am J Roentgenol (2013) 201(6):1229–38. doi: 10.2214/ajr.12.10178
111. Jia H, Xia Y, Song Y, Cai W, Fulham M, Feng DD. Atlas registration and
ensemble deep convolutional neural network-based pr ostate segmentation using
magnetic resonance imaging. Neurocomputing (2018) 275:1358–69. doi: 10.1016/
j.neucom.2017.09.084
112. Bibault JE, Giraud P, Burgun A. Big data and machine learning in radiation
oncology: state of the art and future prospects. Cancer Letters (2016) 382(1):110–7.
doi: 10.1016/j.canlet.2016.05.033
113. Mehralivand S, Harmon SA, Shih JH, Smith CP, Lay N, Argun B, et al.
Multicenter multireader evaluation of an artificial intelligence-based attention mapping
system for the detection of prostate cancer with multiparametric MRI. AJR Am J
roentgenol (2020) 215(4):903–12. doi: 10.2214/ajr.19.22573
114. Zhu Q, Du B, Wu J, Yan P. “A deep learning health data analysis approach:
automatic 3D prostate MR segmentation with densely-connected volumetric
ConvNets,”2018 International Joint Conference on Neural Networks (IJCNN), (Rio
de Janeiro, Brazil) (2018), pp. 1–6. doi: 10.1109/IJCNN.2018.8489136
115. Yan K, Wang X, Kim J, Khadra M, Fulh am M, Feng D. A propagation-DNN:
deep combination learning of multi-level features for MR prostate segmentation.
Comput Methods Programs Biomed (2019) 170:11–21. doi: 10.1016/j.cmpb.
2018.12.031
116. To MNN, Vu DQ, Turkbey B, Choyke PL, Kwak JT. Deep dense multi-path
neural network for prostate segmentation in magnetic resonance imaging. Int J Comput
Assisted Radiol Surgery (2018) 13(11):1687–96. doi: 10.1007/s11548-018-1841-4
117. Dai Z, Carver E, Liu C, Lee J, Feldman A, Zong W, et al. Segmentation of the
prostatic gland and the intraprostatic lesions on multiparametic magnetic resonance
imaging using mask region-based convolutional neural networks. Adv Radiat Oncol
(2020) 5(3):473–81. doi: 10.1016/j.adro.2020.01.005
118. Wang C, Zhu X, Hong JC, Zheng D. Artificial intelligence in radiotherapy
treatment planning: present and future. Technol Cancer Res Treat (2019)
18:1533033819873922. doi: 10.1177/1533033819873922
119. Thoms J, Goda JS, Zlotta AR, Fleshner NE, van der Kwast TH, Supiot S, et al.
Neoadjuvant radiotherapy for locally advanced and high-risk prostate cancer. Nat Rev
Clin Oncol (2011) 8(2):107–13. doi: 10.1038/nrclinonc.2010.207
120. Podder TK, Fredman ET, Ellis RJ. Advances in radiotherapy for prostate cancer
treatment. Adv Exp Med Biol (2018) 1096:31–47. doi: 10.1007/978-3-319-99286-0_2
121. Muralidhar A, Potluri HK, Jaiswal T, McNeel DG. Targeted radiation and
immune therapies-advances and opportunities for the treatment of prostate cancer.
Pharmaceutics (2023) 15(1):252. doi: 10.3390/pharmaceutics15010252
122. Thariat J, Hannoun-Levi JM, Sun Myint A, Vuong T, Gerard JP. Past, present,
and future of radiotherapy for the benefit of patients. Nat Rev Clin Oncol (2013) 10
(1):52–60. doi: 10.1038/nrclinonc.2012.203
123. Sonke JJ, Aznar M, Rasch C. Adaptive radiotherapy for anatomical changes.
Semin Radiat Oncol (2019) 29(3):245–57. doi: 10.1016/j.semradonc.2019.02.007
124. Baskar R, Lee KA, Yeo R, Yeoh KW. Cancer and radiation therapy: current
advances and future directions. Int J Med Sci (2012) 9(3):193–9. doi: 10.7150/ijms.3635
125. Alterio D, Marvaso G, Ferrari A, Volpe S, Orecchia R, Jereczek-Fossa BA.
Modern radiotherapy for head and neck cancer. Semin Oncol (2019) 46(3):233–45.
doi: 10.1053/j.seminoncol.2019.07.002
126. Yang WC, Hsu FM, Yang PC. Precision radiotherapy for non-small cell lung
cancer. J Biomed sci (2020) 27(1):82. doi: 10.1186/s12929-020-00676-5
127. Zhang Y, Liang Y, Ding J, Amjad A, Paulson E, Ahunbay E, et al. A prior
knowledge-guided, deep learning-based semiautomatic segmentation for complex
anatomy on magnetic resonance imaging. Int J Radiat Oncol Biol Phys (2022) 114
(2):349–59. doi: 10.1016/j.ijrobp.2022.05.039
128. Wang M, Zhang Q, Lam S, Cai J, Yang R. A review on application of deep
learning algorithms in external beam radiotherapy automated treatment planning.
Front Oncol (2020) 10:580919. doi: 10.3389/fonc.2020.580919
129. Hunt A, Hansen VN, Oelfke U, Nill S, Hafeez S. Adaptive radiotherapy enabled
by MRI guidance. Clin Oncol (Royal Coll Radiologists (Great Britain)) (2018) 30
(11):711–9. doi: 10.1016/j.clon.2018.08.001
130. Paganetti H, Botas P, Sharp GC, Winey B. Adaptive proton therapy. Phys Med
Biol (2021) 66(22):10.1088/1361-6560/ac344f. doi: 10.1088/1361-6560/ac344f
131. Glide-Hurst CK, Lee P, Yock AD, Olsen JR, Cao M, Siddiqui F, et al. Adaptive
radiation therapy (ART) strategies and technical considerations: a state of the ART
review from NRG oncology. Int J Radiat Oncol Biol Phys (2021) 109(4):1054–75.
doi: 10.1016/j.ijrobp.2020.10.021
132. Jing X, Meng X, Sun X, Yu J. Delineation of clinical target volume for
postoperative radiotherapy in stage IIIA-pN2 non-small-cell lung cancer.
OncoTargets Ther (2016) 9:823–31. doi: 10.2147/ott.S98765
133. Keall PJ, Nguyen DT, O’Brien R, Caillet V, Hewson E, Poulsen PR, et al. The
first clinical implementation of real-time image-guided adaptive radiotherapy using a
standard linear accelerator. Radiother Oncol: J Eur Soc Ther Radiol Oncol (2018) 127
(1):6–11. doi: 10.1016/j.radonc.2018.01.001
134. Thorwarth D, Low DA. Technical challenges of real-time adaptive MR-guided
radiotherapy. Front Oncol (2021) 11:634507. doi: 10.3389/fonc.2021.634507
135. Sprouts D, Gao Y, Wang C, Jia X, Shen C, Chi Y. The development of a deep
reinforcement learning network for dose-volume-constrained treatment planning in
prostate cancer intensity modulated radiotherapy. Biomed Phys Eng Express (2022) 8
(4):10.1088/2057-1976/ac6d82. doi: 10.1088/2057-1976/ac6d82
136. Shen C, Chen L, Gonzalez Y, Jia X. Improving efficiency of training a virtual
treatment planner network via knowledge-guided deep reinforcement learning for
intelligent automatic treatment plann ing of radiotherapy. Med Physics (2021) 48
(4):1909–20. doi: 10.1002/mp.14712
137. Lempart M, Benedek H, Jamtheim Gustafsson C, Nilsson M, Eliasson N, Bäck
S, et al. Volumetric modulated arc therapy dose prediction and deliverable treatment
plan generation for prostate cancer patients using a densely connected deep learning
model. Phys Imaging Radiat Oncol (2021) 19:112–9. doi: 10.1016/j.phro.2021.07.008
138. Owrangi AM, Greer PB, Glide-Hurst CK. MRI-Only treatment planning:
benefits and challenges. Phys Med Biol (2018) 63(5):05tr01. doi: 10.1088/1361-6560/
aaaca4
139. Hsu SH, Han Z, Leeman JE, Hu YH, Mak RH, Sudhyadhom A. Synthetic CT
generation for MRI-guided adaptive radiotherapy in prostate cancer. Front Oncol
(2022) 12:969463. doi: 10.3389/fonc.2022.969463
140. Lachance C, McCormack S. Magnetic re sonance imaging simulators for
simulation and treatment for patients requiring radiation therapy: a review of the
clinical effectiveness, cost-effectiveness, and guidelines. In: CADTH rapid response
reports. Ottawa (ON: Canadian Agency for Drugs and Technologies in Health (2019).
141. Chandarana H, Wang H, Tijssen RHN, Das IJ. Emerging role of MRI in
radiation therapy. J Magn Reson Imaging: JMRI (2018) 48(6):1468–78. doi: 10.1002/
jmri.26271
He et al. 10.3389/fonc.2023.1189370
Frontiers in Oncology frontiersin.org12

142. Das IJ, McGee KP, Tyagi N, Wang H. Role and future of MRI in radiation
oncology. Br J Radiol (2019) 92(1094):20180505. doi: 10.1259/bjr.20180505
143. Li X, Yadav P, McMillan AB. Synthetic computed tomography generation from
0.35T magnetic resonance images for magnetic resonance-only radiation therapy
planning using perceptual loss models. Pract Radiat Oncol (2022) 12(1):e40–8.
doi: 10.1016/j.prro.2021.08.007
144. Li X, Yadav P, McMillan AB. Synthetic computed tomography generation from
0.35T magnetic resonance images for magnetic resonance–only radiation therapy
planning using perceptual loss models. Pract Radiat Oncol (2022) 12(1):e40–e48.
doi: 10.1016/j.prro.2021.08.007
145. Depauw N, Keyriläinen J, Suilamo S, Warner L, Bzdusek K, Olsen C, et al. MRI-
Based IMPT planning for prostate cancer. Radiother oncol: J Eur Soc Ther Radiol Oncol
(2020) 144:79–85. doi: 10.1016/j.radonc.2019.10.010
146. Palmer E, Persson E, Ambolt P, Gustafsson C, Gunnlaugsson A, Olsson LE.
Cone beam CT for QA of synthetic CT in MRI only for prostate patients. J Appl Clin
Med Physics (2018) 19(6):44–52. doi: 10.1002/acm2.12429
147. Persson E, Jamtheim Gustafsson C, Ambolt P, Engelholm S, Ceberg S, Bäck S,
et al. MR-PROTECT: clinical feasibility of a prostate MRI-only radiotherapy treatment
workflow and investigation of acceptance criteria. Radiat Oncol (2020) 15(1):77.
doi: 10.1186/s13014-020-01513-7
148. Persson E, Gustafsson C, Nordström F, Sohlin M, Gunnlaugsson A, Petruson
K, et al. MR-OPERA: a Multicenter/Multivendor validation of magnetic resonance
imaging–only prostate treatment planning using synthetic computed tomography
images. IntJRadiatOncolBiolPhys(2017) 99(3):692–700. doi: 10.1016/
j.ijrobp.2017.06.006
149. Johnstone E, Wyatt JJ, Henry AM, Short SC, Sebag-Montefiore D, Murray L,
et al. Systematic review of synthetic computed tomography generation methodologies
for use in magnetic resonance imaging-only radiation therapy. Int J Radiat Oncol Biol
Phys (2018) 100(1):199–217. doi: 10.1016/j.ijrobp.2017.08.043
150. Nie D, Cao X, Gao Y, Wang L, Shen D. Estimating CT image from MRI data
using 3D fully convolutional networks. Deep Learn Data labeling Med applications:
First Int Workshop LABELS 2016 Second Int Workshop DLMIA 2016 held conjunction
MICCAI 2016 Athens Greece October 21 (2016) 2016:170–8. doi: 10.1007/978-3-319-
46976-8_18
151. Kazemifar S, McGuire S, Timmerman R, Wardak Z, Nguyen D, Park Y, et al.
MRI-Only brain radiotherapy: assessing the dosimetric accuracy of synthetic CT
images generated using a deep learning approach. Radiother Oncol (2019) 136:56–63.
doi: 10.1016/j.radonc.2019.03.026
152. Fu J, Yang Y, Singhrao K, Ruan D, Chu FI, Low DA, et al. Deep learning
approaches using 2D and 3D convolutional neural networks for generating male pelvic
synthetic computed tomography from magnetic resonance imaging. Med Physics
(2019) 46(9):3788–98. doi: 10.1002/mp.13672
153. Zhang Y, Ding SG, Gong XC, Yuan XX, Lin JF, Chen Q, et al. Generating
synthesized computed tomography from CBCT using a conditional generative
adversarial network for head and neck cancer patients. Technol Cancer Res Treat
(2022) 21:15330338221085358. doi: 10.1177/15330338221085358
154. Schonfeld E, Schiele B, Khoreva A. Available at: https://openaccess.thecvf.com/
content_CVPR_2020/html/Schonfeld_A_U-Net_Based_Discriminator_for_
Generative_Adversarial_Networks_CVPR_2020_paper.html.
155. Wolterink JM, Dinkla AM, Savenije MH, Seevinck PR, van den Berg CA, Is gum
I, et al. Deep MR to CT synthesis using unpaired data. international workshop on
simulation and synthesis in medical Imaging Vol. vol. 10557). . Quebec City, QC,
Canada: Springer (2017) p. 14–23 p.
156. Yang H, Sun J, Carass A, Zhao C, Lee J, Prince JL, et al. Unsupervised MR-to-
CT synthesis using structure-constrained CycleGAN. IEEE Trans Med Imaging (2020)
39(12):4249–61. doi: 10.1109/tmi.2020.3015379
157. Liu Y, Chen A, Shi H, Huang S, Zheng W, Liu Z, et al. CT synthesis from MRI
using multi-cycle GAN for head-and-neck radiation therapy. Computerized Med
Imaging graphics: Off J Computerized Med Imaging Society (2021) 91:101953.
doi: 10.1016/j.compmedimag.2021.101953
158. Chen S, Qin A, Zhou D, Yan D. Technical note: U-net-generated synthetic CT
images for magnetic resonance imaging-only prostate intensity-modulated radiation
therapy treatment planning. Med Physics (2018) 45(12):5659–65. doi: 10.1002/mp.13247
159. Arabi H, Dowling JA, Burgos N, Han X, Greer PB, Koutsouvelis N, et al.
Comparative study of algorithms for synthetic CT generation from MRI: consequences
for MRI-guided radiation planning in the pelvic region. Med Physics (2018) 45
(11):5218–33. doi: 10.1002/mp.13187
160. Nie D, Trullo R, Lian J, Wang L, Petitjean C, Ruan S, et al. Medical image
synthesis with deep convolutional adversarial networks. IEEE Trans Bio-medical
Engineering (2018) 65(12):2720–30. doi: 10.1109/tbme.2018.2814538
161. Maspero M, Savenije MHF, Dinkla AM, Seevinck PR, Intven MPW,
Jurgenliemk-Schulz IM, et al. Dose evaluation of fast synthetic-CT generation using
a generative adversarial network for general pelvis MR-only radiotherapy. Phys Med
Biol (2018) 63(18):185001. doi: 10.1088/1361-6560/aada6d
162. Cusumano D, Lenkowicz J, Votta C, Boldrini L, Placidi L, Catucci F, et al. A
deep learning approach to generate synthetic CT in low field MR-guided adaptive
radiotherapy for abdominal and pelvic cases. Radiother Oncol: J Eur Soc Ther Radiol
Oncol (2020) 153:205–12. doi: 10.1016/j.radonc.2020.10.018
163. Dall’Era MA, Albertsen PC, Bangma C, Carroll PR, Carter HB, Cooperberg
MR, et al. Active surveillance for prostate cancer: a systematic review of the literature.
Eur Urol (2012) 62(6):976–83. doi: 10.1016/j.eururo.2012.05.072
164. Van den Broeck T, van den Bergh RCN, Briers E, Cornford P, Cumberbatch M,
Tilki D, et al. Biochemical recurrence in prostate cancer: the European association of
urology prostate cancer guidelines panel recommendations. Eur Urol Focus (2020) 6
(2):231–4. doi: 10.1016/j.euf.2019.06.004
165. David MK, Leslie SW. Prostate specific antigen. StatPearls. Treasure Island FL:
StatPearls Publishing Copyright ©2023, StatPearls Publishing LLC (2023).
166. Kobayashi M, Kijima T, Yashi M, Kamai T. Prostate-specific antigen kinetics
contributes to decision making for biopsy referral: the predictive implication for PSA
retest in patients with elevated PSA levels. Prostate Int (2023) 11(1):27–33.
doi: 10.1016/j.prnil.2022.08.001
167. Bergaglio C, Giasotto V, Marcenaro M, Barra S, Turazzi M, Bauckneht M, et al.
The role of mpMRI in the assessment of prostate cancer recurrence using the PI-RR
system: diagnostic accuracy and interobserver agreement in readers with different
expertise. Diagnostics (Basel) (2023) 13(3):387. doi: 10.3390/diagnostics13030387
168. Yan Y, Shao L, Liu Z, He W, Yang G, Liu J, et al. Deep learning with
quantitative features of magnetic resonance images to predict biochemical recurrence
of radical prostatectomy: a multi-center study. Cancers (2021) 21:13(12). doi: 10.3390/
cancers13123098
169. Hensel J, Thalmann GN. Biology of bone metastases in pr ostate cancer.
Urology (2016) 92:6–13. doi: 10.1016/j.urology.2015.12.039
170. Padhani AR, Lecouvet FE, Tunariu N, Koh DM, De Keyzer F, Collins DJ, et al.
Rationale for modernising imaging in advanced prostate cancer. Eur Urol Focus (2017)
3(2-3):223–39. doi: 10.1016/j.euf.2016.06.018
171. Combes AD, Palma CA, Calopedos R, Wen L, Woo H, Fulham M, et al. PSMA
PET-CT in the diagnosis and staging of prostate cancer. Diagnostics (Basel Switzerland)
(2022) 12(11):2594. doi: 10.3390/diagnostics12112594
172. Liu X, Han C, Cui Y, Xie T, Zhang X, Wang X. Detection and segmentation of
pelvic bones metastases in MRI images for patients with prostate cancer based on deep
learning. Front Oncol (2021) 11:773299. doi: 10.3389/fonc.2021.773299
173. Kott O, Linsley D, Amin A, Karagounis A, Jeffers C, Golijanin D, et al.
Development of a deep learning algorithm for the histopathologic diagnosis and
Gleason grading of prostate cancer biopsies: a pilot study. Eur Urol Focus (2021) 7
(2):347–51. doi: 10.1016/j.euf.2019.11.003
174. Arvaniti E, Fricker KS, Moret M, Rupp N, Hermanns T, Fankhauser C, et al.
Automated Gleason grading of prostate cancer tissue microarrays via deep learning. Sci
Rep (2018) 8(1):12054. doi: 10.1038/s41598-018-30535-1
175. Bulten W, Pinckaers H, van Boven H, Vink R, de Bel T, van Ginneken B, et al.
Automated deep-learning system for Gleason grading of prostate cancer using biopsies:
a diagnostic study. Lancet Oncol (2020) 21(2):233–41. doi: 10.1016/s1470-2045(19)
30739-9
176. van Sloun RJG, Wildeboer RR, Mannaerts CK, Postema AW, Gayet M,
Beerlage HP, et al. Deep learning for real-time, automatic, and scanner-adapted
prostate (Zone) segmentation of transrectal ultrasound, for example, magnetic
resonance imaging-transrectal ultrasound fusion prostate biopsy. Eur Urol Focus
(2021) 7(1):78–85. doi: 10.1016/j.euf.2019.04.009
177. Karimi D, Zeng Q, Mathur P, Avinash A, Mahdavi S, Spadinger I, et al.
Accurate and robust deep learning-based segmentation of the prostate clinical target
volume in ultrasound images. Med Image Analysis (2019) 57:186–96. doi: 10.1016/
j.media.2019.07.005
178. Feng Y, Yang F, Zhou X, Guo Y, Tang F, Ren F, et al. A deep learning approach
for targeted contrast-enhanced ultrasound based prostate cancer detection. IEEE/ACM
Trans Comput Biol Bioinf (2019) 16(6):1794–801. doi: 10.1109/tcbb.2018.2835444
179. Walker Z, Bartley G, Hague C, Kelly D, Navarro C, Rogers J, et al. Evaluating
the effectiveness of deep learning contouring across multiple radiotherapy centres. Phys
Imaging Radiat Oncol (2022) 24:121–8. doi: 10.1016/j.phro.2022.11.003
180. Zabel WJ, Conway JL, Gladwis h A, Skliarenko J, Didiodato G, Goorts-
Matthews L, et al. Clinical evaluation of deep learning and atlas-based auto-
contouring of bladder and rectum for prostate radiation therapy. Pract Radiat Oncol
(2021) 11(1):e80–9. doi: 10.1016/j.prro.2020.05.013
181. Amarsee K, Ramachandran P, Fielding A, Lehman M, Noble C, Perrett B, et al.
Automatic detection and tracking of marker seeds implanted in prostate cancer
patients using a deep learning algorithm. JMedPhysics(2021) 46(2):80–7.
doi: 10.4103/jmp.JMP_117_20
182. Motley R, Ramachandran P, Fielding A. A feasibility study on the development
and use of a deep learning model to automate real-time monitoring of tumor position
and assessment of interfraction fiducial marker migration in prostate radiotherapy
patients(). Biomed Phys Eng Express (2022) 8(3):10.1088/2057-1976/ac34da.
doi: 10.1088/2057-1976/ac34da
183. Hiremath A, Shiradkar R, Fu P, Mahran A, Rastinehad AR, Tewari A, et al. An
integrated nomogram combining deep learning, prostate imaging-reporting and data
system (PI- RADS) scoring, and clinical var iables for identification of clinically
significant prostate cancer on parametri MRI: a retrospective multicentre study.
Lancet Digital Health (2021) 3(7):e445–54. doi: 10.1016/s2589-7500(21)00082-0
184. Zhou Z, Liang Z, Zuo Y, Zhou Y, Yan W, Wu X, et al. Development of a
nomogram combining multiparametric magnetic resonance imaging and PSA-related
He et al. 10.3389/fonc.2023.1189370
Frontiers in Oncology frontiersin.org13

parameters to enhance the detection of clinically significant cancer across different
region. Prostate (2022) 82(5):556–65. doi: 10.1002/pros.24302
185. Dou Q, Yu L, Chen H, Jin Y, Yang X, Qin J, et al. 3D deeply supervised network
for automated segmentation of volumetric medical images. Med Image Analysis (2017)
41:40–54. doi: 10.1016/j.media.2017.05.001
186. Tamada T, Kido A, Yamamoto A, Takeuchi M, Miyaji Y, Moriya T, et al.
Compariso n of biparametric and multiparametric MRI for clinically significant
prostate cancer detection with PI-RADS version 2.1. J Magn Reson Imaging: JMRI
(2021) 53(1):283–91. doi: 10.1002/jmri.27283
187. Xu L, Zhang G, Shi B, Liu Y, Zou T, Yan W, et al. Comparison of biparametric
and multiparametric MRI in the diagnosis of prostate cancer. Cancer imaging: Off Publ
Int Cancer Imaging Soc (2019) 19(1):90. doi: 10.1186/s40644-019-0274-9
188. Scialpi M, Prosperi E, D’ANDREA A, Martorana E, Malaspina C, Palumbo B,
et al. Biparametric versus multiparametric MRI with non-endorectal coil at 3T in the
detection and localization of prostate cancer. Anticancer Res (2017) 37(3):1263–71.
doi: 10.21873/anticanres.11443
189. Gohel P, Singh P, MJa pa M. Explainable AI: cu rrent status and futur e
directions. arXiv. (2021). doi: 10.48550/arXiv.2107.07045
190. Danilevsky M, Qian K, Aharonov R, Katsis Y, Kawas B, PJapa S. A survey of the
state of explainable AI for natural language processing. arXiv. (2020). doi: 10.48550/
arXiv.2010.00711
191. Chaddad A, Peng J, Xu J, Bouridane A. Survey of Explainable AI Techniques in
Healthcare. Sensors (Basel) (2023) 23(2):634. doi: 10.3390/s23020634
192. Zimmermann MT. The importance of biologic knowledge and gene expression
context for genomic data interpretation. Front Genet. (2018) 9:670. doi: 10.3389/
fgene.2018.00670
193. Sidak D, SchwarzerovaJ, Weckwerth W, Waldherr S. Interpretable machine
learning methods for predictions in systems biology from omics data. Front Mol Biosci.
(2022) 9:926623. doi: 10.3389/fmolb.2022.926623
194. Elmarakeby HA, Hwang J, Arafeh R, Crowd is J, Gang S, Liu D, et al.
Biologically informed deep neural network for prostate cancer discovery. Nature
(2021) 598(7880):348–52. doi: 10.1038/s41586-021-03922-4
195. E chegaray S, Nair V, Kadoch M, L eung A, Rubin D, Gevaert O, et al. A
rapid segmentation-insensitive “Digital biopsy”method for radiomic feature
extraction: method and pilot study using CT images of non-small cell lung
cancer. Tomography (Ann Arbor Mich) (2016) 2(4):283–94. doi: 10.18383/
j.tom.2016.00163
196. Gaziev G, Wadhwa K, Barrett T, Koo BC, Gallagher FA, Serrao E, et al.
Defining the learning curve for multiparametric magnetic resonance imaging (MRI) of
the prostate using MRI-transrectal ultrasonography (TRUS) fusion-guided
transperineal prostate biopsies as a validation tool. BJU Int (2016) 117(1):80–6.
doi: 10.1111/bju.12892
He et al. 10.3389/fonc.2023.1189370
Frontiers in Oncology frontiersin.org14