A Nomogram Based on a Multiparametric Ultrasound Radiomics Model for Discrimination Between Malignant and Benign Prostate Lesions

Abstract

Objectives
To evaluate the potential of a clinical-based model, a multiparametric ultrasound-based radiomics model, and a clinical-radiomics combined model for predicting prostate cancer (PCa).

Methods
A total of 112 patients with prostate lesions were included in this retrospective study. Among them, 58 patients had no prostate cancer detected by biopsy and 54 patients had prostate cancer. Clinical risk factors related to PCa (age, prostate volume, serum PSA, etc.) were collected in all patients. Prior to surgery, patients received transrectal ultrasound (TRUS), shear-wave elastography (SWE) and TRUS-guided prostate biopsy. We used the five-fold cross-validation method to verify the results of training and validation sets of different models. The images were manually delineated and registered. All modes of ultrasound radiomics were retrieved. Machine learning used the pathology of “12+X” biopsy as a reference to draw the benign and malignant regions of interest (ROI) through the application of LASSO regression. Three models were developed to predict the PCa: a clinical model, a multiparametric ultrasound-based radiomics model and a clinical-radiomics combined model. The diagnostic performance and clinical net benefit of each model were compared by receiver operating characteristic curve (ROC) analysis and decision curve.

Results
The multiparametric ultrasound radiomics reached area under the curve (AUC) of 0.85 for predicting PCa, meanwhile, AUC of B-mode radiomics and SWE radiomics were 0.74 and 0.80, respectively. Additionally, the clinical-radiomics combined model (AUC: 0.90) achieved greater predictive efficacy than the radiomics model (AUC: 0.85) and clinical model (AUC: 0.84). The decision curve analysis also showed that the combined model had higher net benefits in a wide range of high risk threshold than either the radiomics model or the clinical model.

Conclusions
Clinical-radiomics combined model can improve the accuracy of PCa predictions both in terms of diagnostic performance and clinical net benefit, compared with evaluating only clinical risk factors or radiomics score associated with PCa.

Full text

A Nomogram Based on a
Multiparametric Ultrasound
Radiomics Model for Discrimination
Between Malignant and Benign
Prostate Lesions
Lei Liang
1†
, Xin Zhi
1†
, Ya Sun
1
, Huarong Li
1
, Jiajun Wang
1
, Jingxu Xu
2
and Jun Guo
1
*
1
Department of Ultrasound, Aerospace Center Hospital, Beijing, China,
2
Department of Research Collaboration, R&D
Center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, China
Objectives: To evaluate the potential of a clinical-based model, a multiparametric
ultrasound-based radiomics model, and a clinical-radiomics combined model for
predicting prostate cancer (PCa).
Methods: A total of 112 patients with prostate lesions were included in this retrospective
study. Among them, 58 patients had no prostate cancer detected by biopsy and 54
patients had prostate cancer. Clinical risk factors related to PCa (age, prostate volume,
serum PSA, etc.) were collected in all patients. Prior to surgery, patients received
transrectal ultrasound (TRUS), shear-wave elastography (SWE) and TRUS-guided
prostate biopsy. We used the five-fold cross-validation method to verify the results of
training and validation sets of different models. The images were manually delineated and
registered. All modes of ultrasound radiomics were retrieved. Machine learning used the
pathology of “12+X”biopsy as a reference to draw the benign and malignant regions of
interest (ROI) through the application of LASSO regression. Three models were developed
to predict the PCa: a clinical model, a multiparametric ultrasound-based radiomics model
and a clinical-radiomics combined model. The diagnostic performance and clinical net
benefit of each model were compared by receiver operating characteristic curve (ROC)
analysis and decision curve.
Results: The multiparametric ultrasound radiomics reached area under the curve (AUC)
of 0.85 for predicting PCa, meanwhile, AUC of B-mode radiomics and SWE radiomics
were 0.74 and 0.80, respectively. Additionally, the clinical-radiomics combined model
(AUC: 0.90) achieved greater predictive efficacy than the radiomics model (AUC: 0.85) and
clinical model (AUC: 0.84). The decision curve analysis also showed that the combined
Frontiers in Oncology | www.frontiersin.org March 2021 | Volume 11 | Article 6107851
Edited by:
Hong Huang,
Chongqing University, China
Reviewed by:
Cheng Wei,
University of Dundee, United Kingdom
Ayman Moussa,
Cleveland Clinic Abu Dhabi,
United Arab Emirates
*Correspondence:
Jun Guo
guojun0316@sohu.com
†
These authors have contributed
equally to this work
Specialty section:
This article was submitted to
Cancer Imaging and
Image-directed Interventions,
a section of the journal
Frontiers in Oncology
Received: 27 September 2020
Accepted: 25 January 2021
Published: 02 March 2021
Citation:
Liang L, Zhi X, Sun Y, Li H, Wang J,
Xu J and Guo J (2021) A Nomogram
Based on a Multiparametric
Ultrasound Radiomics Model for
Discrimination Between Malignant and
Benign Prostate Lesions.
Front. Oncol. 11:610785.
doi: 10.3389/fonc.2021.610785
ORIGINAL RESEARCH
published: 02 March 2021
doi: 10.3389/fonc.2021.610785

model had higher net benefits in a wide range of high risk threshold than either the
radiomics model or the clinical model.
Conclusions: Clinical-radiomics combined model can improve the accuracy of PCa
predictions both in terms of diagnostic performance and clinical net benefit, compared
with evaluating only clinical risk factors or radiomics score associated with PCa.
Keywords: radiomics, multiparametric ultrasound, clinical risk factors, machine learning, prostate cancer,
nomogram model
INTRODUCTION
The incidence rate of prostate cancer (PCa) is rapidly increasing
in China (1) and is the second most common cancer and the fifth
leading cancer-related cause of death among males (2). As such,
it has been one of the main health problems affecting many
families. PCa screening has been studied in many randomized
controlled trials, and different caveats have been proposed.
Unfortunately, after detecting the serum level of prostate
specific antigen (PSA) and/or performing a digital rectal
examination, a 10- to 12-core systematic biopsy (3) is required
by the standard diagnostic method. In addition to the
complications related to this procedure (4), it has been
reported that underestimation and overtreatment are high
(5).Therefore, in order to avoid unnecessary trauma, the
accuracy of non-invasive diagnostic methods before prostate
biopsy must be improved.
In general, patients with PCa are divided into the low,
medium, or high risk groups based on the level of prostate
specific antigen (PSA), pathological assessment/Gleason score
(GS), and clinical stage (i.e. T stage) (6). Although free prostate-
specific antigen (fPSA), total prostate-specific antigen (tPSA),
and the ratio of free PSA to total PSA (f/tPSA) are frequently
applied to clinical PCa detection and grading indicators, (7–9),
which indicators are more appropriate for the diagnosis and
classification of PCa remains a controversy, and no agreement
has been reached (10,11). Based on the European Urology
Association treatment guidelines for PCa in 2017, it is
recommended that patients suffering from GS <7 PCa undergo
active surveillance and wait for observation. On the contrary,
because there is an increased risk of exacerbation and shorter rate
of survival among patients with GS ≥7 PCa, it is necessary to take
timely measures (3). Therefore, accurate risk assessment is
important to select the best treatment option for these patients.
Multi-parameter magnetic resonance imaging (mpMRI) has
become an important tool for PCa risk assessment. In the
European Urological Association’s 2019 guidelines, the
application of pre-biopsy mpMRI is recommended in their
diagnostic approach. Nevertheless, in addition to several
intrinsic limitations of MRI, such as high cost, limited
availability, and unrealistic clinical application, the learning
curve of prostate imaging report and data system (PI-RADS),
is steep and there is a high risk of inconsistency between
operators (12).
Ultrasound is another cost-efficient, widely available, and
practical potential candidate for PCa imaging. Although some
ultrasound modalities, such as shear-wave elastography (SWE),
have shown encouraging results, targeted biopsies using B-mode
ultrasound remain inferior to systematic biopsies (13). A
multiparametric method has the principle of imaging well-
known multifocal and heterogeneous diseases such as PCa (14)
which is applicable to MRI and ultrasound by extracting
information from tissue texture, elasticity, or perfusion and
other complementary biomarkers. However, until now, a
multiparametric ultrasound method has rarely been studied
(15). Furthermore, there is growing interest in the use of
quantitative features called radiomics. According to the
definition, radiomics acts as the high-throughput extraction of
many medical imaging characteristics and their conversion into
mined, high-dimensional data whose quantitative analysis offers
unprecedented opportunities to improve clinical decision-
making (16,17).
In previous studies, the analysis of radiomics features focused
on evaluating and classifying PCa lesions (18,19) using mpMRI.
However, transrectal B-mode ultrasound is also a common
imaging method to examine the prostate. Additionally, it is
considered that tissue stiffness acts as an important indicator
of malignant tumor for SWE, and recent studies have shown that
it can also be used to detect PCa (20). In this study, our main
purpose is to verify the feasibility of multiparametric ultrasound
radiomics in discriminating between malignant and benign
prostate lesions. Nevertheless, no studies exist that combine the
features of ultrasound radiomics with clinical factors for
risk assessment.
In consequence, we constructed models according to the
principle of multiparametric ultrasound radiomics in
combination with clinical factors to predict PCa, and
compared whether the combination of these methods helps to
improve diagnostic efficiency.
METHODS
Patients Enrolled in This Study
The institutional Ethics Committee of our hospital approved this
retrospective research, and an informed consent was been signed
by all participants. A total of 128 patients were included in our
hospital from July 2019 to November 2020. Inclusion criteria
were as follows: (1) patients have clinical symptoms (frequency
and urgency of urination, urination or dysuria pains) or
enhanced PSA level; (2) patients have completed transrectal B-
Liang et al. Nomogram Model for Discrimination Prostate Lesions
Frontiers in Oncology | www.frontiersin.org March 2021 | Volume 11 | Article 6107852

mode ultrasound and SWE examinations before receiving
ultrasound-guided biopsy; (3) pathological results were
confirmed through biopsy, and (4) patients with initial biopsy.
Exclusion criteria were shown below: (1) it is difficult to describe
pathological biopsy by transrectal ultrasound (TRUS) (according
to pathological results, TRUS images fail to show lesion location)
(n = 6); (2) surgery, radiotherapy or endocrine therapy prior to
TRUS examination (n = 4); (3) PSA was too high to calculate
(n = 3), or (4) incomplete TRUS data (lack of SWE data) (n = 3).
Ultimately, the study population consisted of 112 patients
including 58 PCa patients and 54 patients who do not show
any histological evidence of cancer. Figure 1 shows the details of
patient selection.
Clinical Data
Age, prostate volume (PV), serum PSA (including tPSA and
fPSA), f/t PSA, Prostate-specific antigen density (PSAD), DRE
result (normal vs abnormal), prostate biopsy pathology and other
clinical information were collected from the patients selected. On
the images of TRUS, PV was calculated as anteroposterior
diameter × vertical diameter × transverse diameter× 0.52.
PSAD was calculated as total PSA/PV.
Ultrasound Image Data Acquisition
Each patient underwent B-mode ultrasound and SWE recording
of the apical, middle, and bottom of the prostate. The
examination was performed using an Aixplorer®Ultrasound
scanner (SuperSonic Imagine, Aix en Provence, France)
equipped with a SE 12-3 transrectal probe.
After standard PV measurement and assessment of the
prostate capsule and seminal vesicles, B-mode ultrasound was
applied to slowly capture the transverse and sagittal scans of the
whole prostate. Abnormal echo patterns (calcifications, cysts,
and hypoechoic lesions) were recorded, and the pictures of the
apical, middle, and bottom transverse plane of interest were
determined and stored by the operator visually based on the
anatomical shape of the prostate. If the prostate areas were
considered to more suspicious than the anatomically selected
FIGURE 1 | Patient selection flow chart.
Liang et al. Nomogram Model for Discrimination Prostate Lesions
Frontiers in Oncology | www.frontiersin.org March 2021 | Volume 11 | Article 6107853

imaging plane, these areas would be brought and stored to the
field of view.
If necessary, the settings specifictoSWE(maximum
penetration and suitable elasticity level) were reviewed and
optimized before SWE imaging. The SWE box would be used
to scan each pre-defined transverse plane in one side (left/right
only) and both sides (whole plane; maximum prostate plane
coverage). During each scanning, a stable signal is ensured in
case of 5-s stay of the sensor remained in a stable position. After
storing the pictures and cine loops, elastic values could be
determined later. If prostate areas on the SWE outside the
predetermined imaging plane were considered more
suspicious, then these areas would also be taken into account.
Figure 2 shows an example of SWE.
We conducted a retrospective review of the image data and
selected ultrasound image data in digital imaging and
communications in medicine (DICOM) format to clearly show
the maximum cross section of each lesion. The above image
information and format were retained for later image segmentation.
Biopsy Procedure and Pathology
All patients ceased taking anti-coagulants for one week before
biopsy and took antibiotics for three days after biopsy. No local
anesthetic was applied during the biopsy. The biopsies were
performed by two sonographers with more than five years of
biopsy experience. An Aixplorer®Ultrasound scanner
(SuperSonic Imagine, Aix en Provence, France) equipped with
a SE 12-3 transrectal probe (end-fire) was applied. An 18-G
biopsy gun with the length of 18 mm and a penetration depth of
22 mm was applied to perform the procedure (Bard Biopsy
Systems, Tempe, Arizona, USA).
All patients underwent the “12+X”biopsy, which is a targeted
biopsy for suspicious areas (combined with B-mode and SWE)
on the basis of 12-core transrectal systematic biopsy. Systematic
biopsy means that, according to the plan, the needle is inserted
into 12 regions of the prostate (medial and lateral apex, medial
and lateral mid prostate, and medial and lateral base in both
lobes), with one needle in each region (21). In addition to the
above-mentioned 12 needles, one to two needles were punctured
in the suspicious area.
Prostate Segmentation
We imported the images into the ITK-SNAP software (version
3.8.0) to manually draw the tumor boundary and determine the
tumor region of interest (ROI). To ensure the consistency of the
ROIs in the B-mode ultrasound and SWE images, the same criteria
were applied to rigorously depict all the ROIs, and the same expert
visually verified them. The following content shows the location
and size of the lesion: (1) detailed records of prostate biopsy
(puncture site and depth) and pathological findings were used to
determine the location and nature of the lesion; (2) the description
of pathology location matches the related lesion on the TRUS
image, and (3) due to the uncertainty of tumor boundary in SWE
images, ROIs of B-mode ultrasound images were applied to the
corresponding SWE images. There is a notable aspect of ROI
drawing: for multifocal PCa, biopsy pathology was applied to
select and confirm the ROI of the lesion with the highest GS value;
in the case of the same GSs, the ROI of the lesion with the largest
diameter was used. Figure 3 shows an example of lesion
segmentation for enrolled patients. At the same time, special
personnel were responsible for checking the accuracy of the
segmentation and relevant pathological results.
The repeatability of feature extraction was assessed on the
basis of intra-observer and inter-observer repeatability of lesion
segmentation. In order to assess the repeatability of characteristic
extraction between intra-observer and inter-observer, 40 patients
were randomly selected and the ROI was delineated by two
radiologists. Both radiologists had more than three years of
experience in prostate ultrasound diagnosis.
Radiomics Feature Extraction
This study used the Dr. Wise Multimodal Research Platform
(https://keyan.deepwise.com) (Beijing Deepwise & League of
PHD Technology Co., Ltd, 193 Beijing, China) for feature
extraction. 1,218 features were extracted from ROI of B-mode
and SWE; the extracted features were divided into seven
categories: First Order Features, Shape Based, Gray-scale Co-
occurrence Matrix(GLCM) Features, Gray-level Size Zone
Matrix(GLSZM) Features, Gray-level Run Length Matrix
(GLRLM) Features, Gray-Level Distance-Zone Matrix (GLDM)
and Neighboring Gray Level Dependence matrix.
FIGURE 2 | A 67-year-old patient had no obvious abnormal lesions in B-mode ultrasound (A), SWE (B) showed that the local tissue became stiff, and the biopsy
result was Gleason = 3 + 4. ROI was delineated under the guidance of the abnormal area of SWE.
Liang et al. Nomogram Model for Discrimination Prostate Lesions
Frontiers in Oncology | www.frontiersin.org March 2021 | Volume 11 | Article 6107854

Model Construction
Prostate lesions were identified by clinical elements and
radiomics characteristics. We used the five-fold cross-
validation method to verify the results of training sets and
validation sets of different models. With regard to clinical
elements, logistic regression models were established by
univariate and multivariate logistic analyses to identify the
relationship between clinical factors and prostate lesions. In
terms of the multiparametric ultrasound-based radiomics
model, we attempted to use six kinds of feature-screening
methods including F-Test, Pearson Correlation Coefficient,
Mutual Information, L1-based, Tree-based models, and
Recursive Feature Elimination. Only one of the above methods
was used for feature screening each time to build the model.
Finally, the L1-based model was selected because of its optimal
diagnostic performance. Through analyzing the logistic
regression of the selected characteristics weighted by their
coefficients, a formula called RAD-SCORE was generated. An
integrated clinical-radiomics combined model with the weight of
radiomics characteristics and clinical risk factors was established
by using multivariate logistic regression and was presented in the
form of nomogram.
Statistical Analysis
When establishing the clinical model, the clinical factors were
selected by applying univariate logistic regression, and the
clinical model was established by introducing the clinical
factors with p<0.05 into multivariate logistic regression. In
logistic regression, the forward stepwise selection method was
adopted. Finally, the clinical model was set up. The area under
curve (AUC) with 95% confidence intervals (95% CIs) was used
to quantify the performance of each model. Whether the AUC
values of the three models were significantly different was
determined by employing the DeLong test. The nomogram of
the clinical-radiomics model was constructed to improve
decision making. Decision curve analysis was conducted to
determine the clinical usefulness of the clinical, radiomics and
clinical-radiomics combined model. R software (version 4.0.2)
and SPSS (version 23.0) were employed to perform analysis.
Theentireworkflow of this analysis was presented in
Figure 4.
RESULTS
Patient Characteristics
Among the 112 patients, 58 (51.7%) presented with benign
lesions, and 54 (48.2%) were diagnosed with PCa. The GS
results of all patients were as follows: 3 + 3 = 6 (10 cases); 3 +
4 = 7(nine cases); 4 + 3 = 7 (10 cases); >4 + 3 (25 cases). Table 1
shows the features of all patients.
Clinical Model
With regard to clinical factors, tPSA, fPSA, and PSAD were
important factors for the prediction of PCa based on the
univariate logistic regression analysis. According to multivariate
logistic analysis, age and PSAD were important (p<0.05)as
independent predictors. The outcomes of the univariate and
multivariate logistic regression analysis were presented in Table 2.
At the end, a logistic regression classifier was set up according to the
clinical characteristics selected. The AUC of the training set was 0.88
(95% CI: 0.82–0.95), accuracy rate was 0.81, sensitivity was 0.75, and
specificity was 0.87. The AUC of the validation set was 0.84 (95%
CI: 0.76–0.91), accuracy rate was 0.76, sensitivity was 0.67 and
specificity was 0.85.
Radiomics Model
Intra-observer and inter-observer consistency for characteristic
extraction were assessed by using intra-class and inter-class
correlation coefficients (ICCs). Feature extraction of intra-
observer and inter-observer showed good reproducibility, with
intra-observer ICCs ranging from 0.78 to 0.85 and the inter-
observer ICCs ranging from 0.75 to 0.88.
In the training set, after the LASSO algorithm was applied,
1,218 B-mode features were reduced to 20 risk predictors, and
corresponding steps were also completed for the SWE data set.
Then, after the lasso regression, the total 2,436 features of the two
modes were reduced to 20 related features, and the
Multiparametric RAD-SCORE was obtained. The sum of the
FIGURE 3 | TRUS B-mode ultrasound imaging (A) and SWE imaging (B) from the same position of 74-year-old PCa patient (fPSA, 1.28 ng/ml; tPSA, 7.855 ng/ml;
biopsy GS,4 + 3 = 7). ROI (red solid line) was outlined in the B-mode ultrasound and SWE.
Liang et al. Nomogram Model for Discrimination Prostate Lesions
Frontiers in Oncology | www.frontiersin.org March 2021 | Volume 11 | Article 6107855

AB D EC
FIGURE 4 | The workflow of this study. (A) Regions of interest were segmented from B-mode ultrasound and SWE. (B) The quantitative imaging texture features were extracted and selected to construct the
radiomics model. (C) Clinical risk factors were used to establish the clinical model. (D) At the same time, the radiomics and clinical factors were added to construct the combined clinical-radiomics model. (E) ROC
curve analysis and DCA were used to evaluate the performance of the model.
TABLE 1 | Characteristics of all patients (n = 112).
Characteristics Cohort of benign
patients
Cohort of PCa
patients
Number of patients, n 58 54
Age, years (median; IQR) 72.00(66.00–75.00) 78.00 (71.75–83.00)
PV, ml (median; IQR) 52.70(40.70–83.12) 43.70(34.08–58.83)
tPSA, ng/ml(median; IQR) 6.40 (4.62–10.58) 12.38 (8.85–62.00)
fPSA, ng/ml(median; IQR) 1.19(0.72–1.70) 1.92(1.13–7.63)
f/tPSA, % (median; IQR) 18.31(12.62–25.16) 16.52(11.20–20.85)
PSAD, ng/ml/ml (median;
IQR)
0.12(0.07–0.20) 0.32(0.17–1.25)
Abnormal DRE, n (%) 32(55.2) 30(55.5)
TABLE 2 | Univariate and multivariate logistic analysis results of clinical factors.
Clinical
factors
Univariate logistic analysis
results
Multivariate logistic analysis
results
OR (95% CI) p
value
OR (95% CI) p
value
Age 1.071(1.019–1.126) 0.007 1.123(1.049–1.202) 0.001
fPSA 1.561(1.108–2.200) 0.011 0.907
tPSA 1.129(1.040–1.226) 0.004 0.563
f/t PSA 0.978(0.935–1.023) 0.339
PV 0.994(0.983–1.006) 0.321
PSAD 227.470 (7.374–
7017.363)
0.002 550.945 (13.503–
22479.425)
0.001
DRE results 1.141(0.348–3.734) 0.828
FIGURE 5 | ROC of B-mode ultrasound, SWE, and Multiparametric.
Liang et al. Nomogram Model for Discrimination Prostate Lesions
Frontiers in Oncology | www.frontiersin.org March 2021 | Volume 11 | Article 6107856

weighted features of B-mode RAD-SCORE, SWE RAD-SCORE
and Multiparametric RAD-SCORE are shown in Appendix 1.
The AUC values of the training set of the B-mode RAD-
SCORE, SWE RAD-SCORE, and multiparametric RAD-SCORE
were 0.97 (95% CI: 0.94–1.00), 0.97(95% CI: 0.94–1.00), and 1.00
(95% CI: 0.949–1.00)), respectively. And the AUC values of the
validation set were 0.74 (95% CI: 0.65–0.84), 0.80(95% CI: 0.72–
0.88) and 0.85 (95% CI: 0.77–0.92), respectively. The ROC curves
of the three models were shown in Figure 5.
Clinical-Radiomics Combined Model
The nomogram of the clinical-radiomics combined model
including age, PSAD, and RAD-SCORE is shown in Figure 6.
The clinical-radiomics combined model displayed excellent
predictive capacity with AUC of 0.91(95% CI: 0.86–0.97) in the
training group and 0.89 (95% CI: 0.82–0.96) in the validation
group. Despite showing the same diagnostic efficacy as the
radiomics model, the clinical-radiomics combined model was
better than the clinical model in diagnostic efficacy (p< 0.05).
The AUC, accuracy, sensitivity, and specificity of the three
models are compared and presented in Table 3. The ROC
curves of the three models are compared in Figure 7.
Decision Curve
The decision curves of the clinical model, the radiomics model, and
the clinical-radiomics combined model are shown in Figure 8.
According to the decision curve, the clinical-radiomics combined
model was more beneficial in a wide range of high risk threshold
than the clinical and radiomics models alone in predicting PCa.
DISCUSSION
In this study, the development of a multiparametric radiomics
classifier was reported for the classification of prostate lesions on
basis of co-registration of B-mode ultrasound and SWE. The
radiomics model was established by extracting quantitative
imaging characteristics and effectively choosing these
characteristics. Subsequently, a clinical-radiomics combined
model was developed by combining the RAD-SCORE with
clinical factors. According to the results, clinical-radiomics
combined model can improve the accuracy of PCa predictions
both in terms of diagnostic performance and clinical net benefit,
FIGURE 6 | Nomogram of the combined model for predicting PCa.
TABLE 3 | AUC results of the clinical, radiomics, and clinical-radiomics combined models for predicting PCa.
Clinical model Radiomics model Clinical-radiomics combined model
Training set Validation set Training set Validation set Training set Validation set
AUC
(95% CI)
0.88 (0.82–0.95) 0.84 (0.76–0.91) 1.00 (0.99–1.00) 0.85 (0.77–0.92) 0.92 (0.86–0.97) 0.90 (0.84–0.96)
Accuracy 0.81 0.76 0.99 0.78 0.84 0.81
Sensitivity 0.75 0.67 1.00 0.77 0.79 0.75
Specificity 0.87 0.85 0.98 0.80 0.89 0.87
FIGURE 7 | Comparison of ROC curves for differentiation of the three
models for predicting PCa.
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compared with evaluating only clinical risk factors or radiomics
score associated with PCa.
Clinical Factors Associated With PCa
In previous studies, prostate related clinical factors were identified
by researchers to assist with diagnosing PCa and assessing its
invasiveness. However, agreement has not been reached. Niu et al.
showed that age, PI-RADS version 2 score, and adjusted PSAD
were independent predictors of high-grade PCa (HGPCa), with an
AUC of 0.83 (22). Fang et al. predicted the presence of PCa and
HGPCa by applying clinical factors (age, PSA, fPSA, PV, and
TRUS) with or without MRI outcomes. The AUC values for the
prediction of PCa with or without MRI were 0.875 and 0.841,
respectively, while those for the prediction of HGPCa were 0.872
and 0.850, respectively (23). In a study by Li et al., patients with
benign lesions and GS = 6 were grouped into clinically
insignificant PCa. After univariate and multivariate logistic
analysis, the results showed that age, tPSA, fPSA and clinical
factors were important factors for predicting significantly
important PCa, with an AUC value of 0.842 (24). Our study
also showed that age and PSAD were important predictors of PCa.
Considering the total level of PSA and prostate volume, PSAD
may have more individualized significance than serum PSA level,
as previously reported in the literature (25). However, our results
were not completely consistent with the previous studies
mentioned above. This may be due to the different clinical
factors selected in each study and inconsistent case grouping.
Therefore, a more simple and accurate method should be
developed for the grouping and scoring of prostate cancer patients.
Some Radiomics Features That Can
Discriminate Prostate Lesions
According to the previous studies, hypoechoic lesions have a
relatively low ability to predict PCa (26,27). This may be due to
the subjective choice of hypoechoic lesions by the operator,
thereby making its reproducibility and representativeness
problematic (26,28). Although hypoechoic lesions of B-mode
ultrasound cannot be used as a predictor of PCa, some studies
have shown that PCa with hypoechoic lesions may represent high-
grade Gleason. According to the report by Nakano Junqueira et al.,
patients who have received prostatectomy with hypoechoic lesions
experienced greatly worse prognosis than those without
hypoechoic lesions, despite great differences in Gleason score,
PSA, and percentage of positive cores (29). However, previous
studies have limitations in the repeatability and representativeness
of the quantitative expression of outcomes. The significance of our
study is that radiomics can provide a numerical value by
quantitative analysis of gray level. Our results showed that the
Mean@b (i.e. The average gray level intensity within the ROI) of
PCa was lower than that of benign lesions, and the Mean@b can be
used as a predictor of PCa. Moreover, the average gray level of
different nodules can be obtained by using radiomics, which can
provide an objective index for the prediction of PCa.
Variations in cellular composition, fluid content, collagen level,
and fibromuscular stroma in different types of prostate lesions
may be reflected by quantitative analysis through radiomics. Our
study showed that patients with benign lesions displayed lower
values in Correlation, which is a value between 0 (uncorrelated)
and 1 (perfectly correlated) indicating the linear dependence of
gray level values on their respective voxels, with high relative
weight. In another parameter of homogeneity, benign nodules
displayed higher levels of Gray Level Non-Uniformity Normalized
(GLNN), that is the changes of gray-level intensity values in the
image, with a lower value reflecting a larger similarity in intensity
values, than that of PCa. The above results indicated that the
consistency in benign prostatic lesions was lower than that in PCa.
The most common benign nodular lesion in the prostate is benign
prostatic hyperplasia (BPH), which is usually accompanied by an
inflammatory reaction. Inflammatory cells and pro-inflammatory
cytokines can be detected in the histopathology of the resected
BPH specimens (30). In addition, studies have confirmed that
BPH is often characterized by the increased number of cells of
different components, not only in the number of gland cells, but
also in the number of smooth muscle cells, or may even consist
entirely of stromal nodules (31). However, the pathological
changes of PCa are mostly because of the increase of the
number of cancer cells and the changes of extracellular space
(24). Therefore, we speculated that the heterogeneity of cellular
components may be the reason for the lower consistency of
ultrasound radiomics in benign prostatic lesions than in PCa.
FIGURE 8 | Decision curves of the clinical, radiomics, and clinical-radiomics combined models for predicting PCa.
Liang et al. Nomogram Model for Discrimination Prostate Lesions
Frontiers in Oncology | www.frontiersin.org March 2021 | Volume 11 | Article 6107858

Even before morphological changes are detected by MRI and
ultrasound examination, the stiffness of prostate tissue changes
early with the effect of desmoplastic reaction or cancer cells
infiltrating into the interstitial tissues, leading to PCa tissue
feeling harder than normal tissue (32–34). Therefore, current
guidelines suggest that SWE can be used as an auxiliary means
for TRUS to detect PCa (35). Considering the above factors, we
incorporated SWE into the radiomics model in this study. As a
rule, in SWE images, the redder the color, the stiffer the tissue,
and in the radiomics standards, the redder the color, the higher
the gray level. Our study showed that the value of Mean@SWE in
PCa was higher, indicating that the nodules of PCa are stiffer,
which is consistent with the results of previous studies. Our
results also showed that the multiparametric radiomics model
combined with B-mode ultrasound and SWE had better
diagnostic performance than the single-parametric model, and
the AUC can reach 0.85.
Some Advantages of Our Study: Widely
Used Radiomics Features and Nomograms
Recently, some studies have compared or combined radiomics,
including mpMRI, CT, and prostate specific membrane antigen-
positron emission computed tomography (PSMA-PET), with
common approaches to assess the diagnostic value of prostate
lesions (25,36,37). The research focused on the use of mpMRI
combined with PI-RADS, which can accurately characterize the
prostate index lesions derived from mpMRI by using quantitative
image data (38). Another important examination is ultrasound,
which not only has the advantage of simplicity, but also plays an
indispensable role in prostate biopsy. However, ultrasound-based
radiomics studies of prostate lesions are rare. Only Wildeboer et al.
reported that the multiparametric classifier combined B-mode,
SWE and contrast-enhanced ultrasound (CEUS) radiomics can
reach AUC of 0.75, PCa of 0.90 and great PCa, respectively (39).
However,this study used unique radiomics characteristicsthat were
inconsistent with the characteristics adopted by moststudies. In our
study, a more widely used Pyradiomic approach (40) was used to
establish a combined model of prostate cancer diagnosis by
combining radiomics with clinical factors associated with
the diagnosis of prostate cancer. It can be seen that adding RAD-
SCORE to the clinical model can improve the diagnostic efficiency
and clinical net benefit in PCa diagnosis. As such, the model
based on radiomics characteristics is obviously valuable in
diagnosing PCa.
Lately, the field of clinical medicine has widely applied the
nomogram figure forecast model. A lot of researches associated
with this model have been published in the clinical journals
with high impacts (41,42). The nomogram figure forecast
model represents a variety of disease risk elements and predicts
the prognosis of patients by using risk score, which is more
distinct, simple, and easy to understand. Meanwhile, after
being used in clinical work effectively, it is conducive to
physician–patient communication and an improved
physician–patient relationship. In this study, the developed
nomogram of the clinical-radiomics combined model offered
an intuitive and convenient approach for physicians to
diagnosePCa,andwillbeanewmethodofauxiliary
diagnosis in clinical work.
Limitations
Although the proposed method was used to improve the
performance of discrimination between malignant and benign
prostate lesions, several malignant ROIs were missed, and several
benign ROIs were wrongly categorized as malignant. In the
future, the nature of false readings (39) may be clarified further
by immunohistochemical techniques. It has been suggested that
prostatitis or prostatic hyperplasia, which sometimes occurs
simultaneously with prostate cancer, is also considered to
promote angiogenesis and change the stiffness of prostate
tissue, which may attribute to the false characterization (43,
44). Future analysis including CEUS parameters may help us find
more radiomics features of the multiparameter model.
Despite the hopeful outcomes, there were several limitations in
this research. Firstly, our results showed that there was no
statistical difference in RAD-SCORE among patients with
different Gleason scores, which may be due to the small number
of enrolled patients. Moreover, this study was a retrospective study
conducted at a single institution. Although cross-validation was
used to test the model, better evidence for clinical application
needs to be obtained by multi-center validation with a larger
sample size. Secondly, the Peripheral zone (PZ) and Transition
zone (TZ) of the prostate were not separated in this research due
to highly malignant diseases in both regions. However, there may
be differences between the two regions, including in B-mode
ultrasound and SWE (35). Therefore, further investigation is
required to expand the size of the research object and
distinguish PZ from TZ of the prostate in different ways.
CONCLUSIONS
In our study, we developed a radiomics model to discriminate
between benign and malignant lesions of the prostate with high
diagnostic power and clinical net benefit. Moreover, we proved
the feasibility of a multiparametric ultrasound classifier to
improve the PCa localization. Using the nomogram to
comprehensively consider the radiomics features and clinical
factors can provide radiologists with a quantitative and intuitive
method to predict PCa with more confidence. Our goal is to
further expand the data set so that the performance of the model
can be consolidated.
DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation.
ETHICS STATEMENT
The studies involving human participants were reviewed and
approved by the Ethics Committee of Aerospace Center
Liang et al. Nomogram Model for Discrimination Prostate Lesions
Frontiers in Oncology | www.frontiersin.org March 2021 | Volume 11 | Article 6107859

Hospital. The patients/participants provided their written
informed consent to participate in this study.
AUTHOR CONTRIBUTIONS
LL and XZ contributed equally to this work. They jointly
completed the study design, data collection, statistical analysis,
and article writing. YS and HL mainly completed the data
collection work. JX and JW completed the Radiomics analysis
and chart making. All authors contributed to the article and
approved the submitted version.
FUNDING
This study is supported by Hospital level project of Aerospace
Center Hospital (YN202105) and Scientific research and
cultivation plan of health development in Haidian District
(HP2021-32-50702).
ACKNOWLEDGMENTS
The authors thank AiMi Academic Services (www.aimieditor.
com) for English language editing and review services.
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Conflict of Interest: Author JX was employed by Beijing Deepwise & League of
PHD Technology Co., Ltd.
The remaining 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.
Copyright © 2021 Liang, Zhi, Sun, Li, Wang, Xu and Guo. 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 forumsis permitted, provided
the original author(s) and the copyright owner(s) are credited and that the original
publicationin this journal is cited, in accordance with accepted academic practice. No
use, distribution or reproduction is permitted which does not comply with these terms.
Liang et al. Nomogram Model for Discrimination Prostate Lesions
Frontiers in Oncology | www.frontiersin.org March 2021 | Volume 11 | Article 61078511

APPENDIX 1. FORMULAS OF THREE
RADIOMICS MODELS
B-mode RAD-SCORE: −0.1832+wavelet-HLH_glszm_
ZoneEntropy@b×0.5937–logarithm_firstorder_Kurtosis@
b×0.5746–wavelet-HLL_firstorder_Minimum@b×0.5204
+wavelet-HHH_glszm_ZoneEntropy@b×0.5094–
exponential_glszm_SmallAreaHighGrayLevelEmphasis@
b×0.4606–wavelet-HHH_firstorder_Kurtosis@b×0.4558
+wavelet-HHL_gldm_DependenceNonUniformity
Normalized@b×0.4526–wavelet-LHL_gldm_SmallDependence
LowGrayLevelEmphasis@b×0.44+wavelet-LHH_glszm_
GrayLevelVariance@b×0.3751–wavelet-HHH_glcm_Maximum
Probability@b×0.3649–wavelet-HHL_firstorder_Mean@
b×0.3207–wavelet-LHL_gldm_DependenceVariance@b×0.3118
+wavelet-HHH_glszm_SmallAreaHighGrayLevelEmphasis@
b×0.3083–wavelet-HHH_glszm_ZonePercentage@b×0.2929
+original_shape_Elongation@b×0.2905+wavelet-
HHH_glrlm_LowGrayLevelRunEmphasis@b×0.2846+wavelet-
HLL_gldm_DependenceNonUniformityNormalized@b×0.2809
+ gradient_glszm_SizeZoneNonUniformityNormalized@
b×0.2713–wavelet-HHH_firstorder_Mean@b×0.2596
+square_glszm_LowGrayLevelZoneEmphasis@b×0.2353
SWE RAD-SCORE: −0.0836–wavelet-LLH_glszm_Zone
Entropy@swe×0.8769+wavelet-HHH_glszm_ZoneEntropy@
swe×0.6888+wavelet-LLL_gldm_LargeDependenceHighGray
LevelEmphasis@swe×0.5955–wavelet-HLL_glszm_
LargeAreaLowGrayLevelEmphasis@swe×0.5776–gradient_
firstorder_10Percentile@swe×0.4288–wavelet-HHH_firstorder_
Mean@swe×0.38–wavelet-LHH_glcm_MaximumProbability@
swe×0.3763–wavelet-HLL_firstorder_Skewness@swe×0.3663
+wavelet-LHH_glszm_ZoneEntropy@swe×0.3531+wavelet-
LHH_glrlm_GrayLevelVariance@swe×0.3201
+original_glszm_SizeZoneNonUniformityNormalized@
swe×0.3135–wavelet-HHH_firstorder_Skewness@swe×0.2899
+wavelet-HHL_glcm_ClusterShade@swe×0.2702–
logarithm_glrlm_RunLengthNonUniformity@swe×0.2654–
logarithm_glszm_SmallAreaLowGrayLevelEmphasis@
swe×0.2582–wavelet-HLL_glcm_MaximumProbability@
swe×0.2419+wavelet-HHH_glrlm_ShortRunHighGray
LevelEmphasis@swe×0.2303+wavelet-LHH_glszm_
SmallAreaHighGrayLevelEmphasis@swe×0.1917–wavelet-
LHH_glszm_GrayLevelVariance@swe×0.1844–wavelet-
LLH_glszm_ZoneEntropy×1.4254
Multiparametric RAD-SCORE: -0.2383–wavelet-LLH_
glszm_ZoneEntropy@swe×0.7143–logarithm_firstorder_
Kurtosis@b×0.6721–wavelet-HLL_firstorder_Minimum@
b×0.6536+wavelet-HHL_gldm_DependenceNonUniformity
Normalized@b×0.5464+wavelet-HHH_glszm_ZoneEntropy@
swe×0.5366–gradient_firstorder_10Percentile@swe×0.532
+wavelet-LHH_glszm_ZoneEntropy@swe×0.4812–wavelet-
LHL_gldm_SmallDependenceLowGrayLevelEmphasis@
b×0.4767–logarithm_glrlm_RunLengthNonUniformity@
swe×0.4657–wavelet-HHL_firstorder_Mean@b×0.4663–
exponential_glszm_SmallAreaHighGrayLevelEmphasis@
b×0.4619+wavelet-LHH_glrlm_LowGrayLevelRunEmphasis@
b×0.4544+wavelet-LHH_glszm_GrayLevelVariance@b×0.4479
+exponential_glcm_Correlation@b×0.4148+original_glszm_
SizeZoneNonUniformityNormalized@swe×0.4077+wavelet-
HHH_glszm_GrayLevelNonUniformityNormalized@
swe×0.3646–wavelet-LHH_glcm_MaximumProbability@
swe×0.3372+gradient_glszm_SizeZoneNonUniformity
Normalized@b×0.3029–logarithm_glszm_SmallAreaLowGray
LevelEmphasis@swe×0.2991–wavelet-HHH_glrlm_
GrayLevelNonUniformityNormalized@swe×0.2899–wavelet-
LLH_glszm_ZoneE-ntropy@swe×0.2865
Liang et al. Nomogram Model for Discrimination Prostate Lesions
Frontiers in Oncology | www.frontiersin.org March 2021 | Volume 11 | Article 61078512