Figure - available from: Scientific Reports
This content is subject to copyright. Terms and conditions apply.
![A representative false negative case. In (a), there are numerous number of infiltrating degenerative cancer cells (c, d, f) which were not predicted as diffuse-type ADC cells on heatmap image (b, e) in necrotic and granulation tissues. After immunohistochemical stainings with AE1/AE3 (g), CD20 (h), and CD34 (i), infiltrating cancer cells (f) exhibited AE1/AE3 positive, CD20 negative, and CD34 negative, indicating cancer of epithelial origin (carcinoma). Therefore, histopathologically, this case was diagnosed as a diffuse-type differentiated ADC. Model applied at ×\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times $$\end{document} 20, where the 224 ×\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times $$\end{document} 224px heatmap square represents 112 ×\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times $$\end{document} 112 μ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\upmu }$$\end{document}m2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^2$$\end{document}.](publication/355204893/figure/fig10/AS:1079048138686474@1634276532432/A-representative-false-negative-case-In-a-there-are-numerous-number-of-infiltrating.png)
A representative false negative case. In (a), there are numerous number of infiltrating degenerative cancer cells (c, d, f) which were not predicted as diffuse-type ADC cells on heatmap image (b, e) in necrotic and granulation tissues. After immunohistochemical stainings with AE1/AE3 (g), CD20 (h), and CD34 (i), infiltrating cancer cells (f) exhibited AE1/AE3 positive, CD20 negative, and CD34 negative, indicating cancer of epithelial origin (carcinoma). Therefore, histopathologically, this case was diagnosed as a diffuse-type differentiated ADC. Model applied at ×\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times $$\end{document} 20, where the 224 ×\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times $$\end{document} 224px heatmap square represents 112 ×\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times $$\end{document} 112 μ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\upmu }$$\end{document}m2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^2$$\end{document}.
Source publication
Gastric diffuse-type adenocarcinoma represents a disproportionately high percentage of cases of gastric cancers occurring in the young, and its relative incidence seems to be on the rise. Usually it affects the body of the stomach, and it presents shorter duration and worse prognosis compared with the differentiated (intestinal) type adenocarcinoma...
Similar publications
Purpose
A lot of strategies have been attempted to achieve high-quality skin wound healing, among them, fat transplantation has been used for skin wound repair and scar management and has shown beneficial effects. However, the underlying mechanism is still unclear. Recently, studies found that transplanted cells underwent apoptosis within a short p...
Citations
... For example, the accuracy of an AI diagnosis system in examining upper gastrointestinal cancer is reportedly over 91.7%. 10 In addition, researchers have proposed that deep learning models can classify gastric diffuse adenocarcinoma with high specificity and help pathologists diagnose the potential of workflow systems. 11 ML has also made progress in the treatment of GA and its complications. Researchers have developed a powerful ML method to forecast anastomotic leakage in patients with GA undergoing gastrectomy in real-time, which can guide surgeons' intraoperative decision-making. ...
Background: Although gastric adenocarcinoma (GA) related ocular metastasis (OM) is rare, its occurrence indicates a more severe disease. We aimed to utilize machine learning (ML) to analyze the risk factors of GA-related OM and predict its risks. Methods: This is a retrospective cohort study. The clinical data of 3532 GA patients were collected and randomly classified into training and validation sets in a ratio of 7:3. Those with or without OM were classified into OM and non-OM (NOM) groups. Univariate and multivariate logistic regression analyses and least absolute shrinkage and selection operator were conducted. We integrated the variables identified through feature importance ranking and further refined the selection process using forward sequential feature selection based on random forest (RF) algorithm before incorporating them into the ML model. We applied six ML algorithms to construct the predictive GA model. The area under the receiver operating characteristic (ROC) curve indicated the model's predictive ability. Also, we established a network risk calculator based on the best performance model. We used Shapley additive interpretation (SHAP) to identify risk factors and to confirm the interpretability of the black box model. We have de-identified all patient details. Results: The ML model, consisting of 13 variables, achieved an optimal predictive performance using the gradient boosting machine (GBM) model, with an impressive area under the curve (AUC) of 0.997 in the test set. Utilizing the SHAP method, we identified crucial factors for OM in GA patients, including LDL, CA724, CEA, AFP, CA125, Hb, CA153, and Ca ²⁺ . Additionally, we validated the model's reliability through an analysis of two patient cases and developed a functional online web prediction calculator based on the GBM model. Conclusion: We used the ML method to establish a risk prediction model for GA-related OM and showed that GBM performed best among the six ML models. The model may identify patients with GA-related OM to provide early and timely treatment.
... Therefore, deep learning is expected to be a novel method for analyzing surgical techniques. In radiology and histopathology, where the digital transformation of medical images and the subsequent construction of big databases started some time ago, deep learning models, which segment pathological lesions on X-ray images [17][18][19] or screen for malignant lesions on histopathological specimen images [20,21], have been developed and are in clinical use. Furthermore, in diagnostic intestinal endoscopy, endoscopic devices with deep learning models that segment or classify lesions are being developed [22][23][24]. ...
To develop deep learning models using thoracoscopic images to identify visceral pleural invasion (VPI) in patients with clinical stage I lung adenocarcinoma, and to verify if these models can be applied clinically.
Two deep learning models, one based on a convolutional neural network (CNN) and the other based on a vision transformer (ViT), were applied and trained via 463 images (VPI negative: 269 images, VPI positive: 194 images) captured from surgical videos of 81 patients. Model performances were validated via an independent test dataset containing 46 images (VPI negative: 28 images, VPI positive: 18 images) from 46 test patients.
The areas under the receiver operating characteristic curves of the CNN-based and ViT-based models were 0.77 and 0.84 (p = 0.304), respectively. The accuracy, sensitivity, specificity, and positive and negative predictive values were 73.91, 83.33, 67.86, 62.50, and 86.36% for the CNN-based model and 78.26, 77.78, 78.57, 70.00, and 84.62% for the ViT-based model, respectively. These models’ diagnostic abilities were comparable to those of board-certified thoracic surgeons and tended to be superior to those of non-board-certified thoracic surgeons.
The deep learning model systems can be utilized in clinical applications via data expansion.
... Additionally, we achieved a higher mean ROC AUC of 0.99 while their ROC AUC was around 0.978 and 0.9873 for different test sets. Another study, by Kanavati et al. [15] which also utilized partial transfer learning on gastric diffuse-type adenocarcinoma, obtained ROC AUCs in the range 0.95-0.99 so their mean is lower than 0.99. ...
Deep learning has demonstrated great potential in pathology as a tool to aid in the accurate and efficient diagnosis of
cancer, by learning from large datasets and providing additional insights to pathologists. This study focuses on developing a
generalized deep learning model for classifying colon, stomach, and kidney cancers using whole slide images. We employed the
transfer learning approach with partial transfusion using the Efficient-Net model, pre-trained on the ImageNet dataset. Our model
achieved a mean ROC AUC of 0.99 and an unseen test set accuracy of 95%. While prior studies have proposed various methods
for accurate classification for individual origin sites, this work demonstrates the generalization ability of partial transfusion in
pathology image classification.
... The deep learning model was trained using a weakly-supervised training method exactly as described in previous works [5,6]. To refer the reader to the referenced publications for training details. ...
HER2 (human epidermal growth factor receptor 2) is a protein that is found on the surface of some cells, including breast cells. HER2 plays a role in cell growth, division, and repair, and when it is overexpressed, it can contribute to the development of certain types of cancer, particularly breast cancer. HER2 overexpression occurs in approximately 20\% of cases, and it is associated with more aggressive tumor phenotypes and poorer prognosis. This makes its status an important factor in determining treatment options for breast cancer.
While HER2 expression is typically diagnosed through a combination of immunohistochemistry (IHC) and/or fluorescence in situ hybridization (FISH) testing on breast cancer tissue samples, we sought to determine to what extent it is possible to diagnose from H\&E-stained specimens. To this effect we trained a deep learning model to classify HER2-positive image patches using a dataset of 10 whole-slide images (5 HER2-positive, 5 HER2-negative). We evaluated the model on a different test set consisting of patches extracted from 10 WSIs (5 HER2-positive, 5 HER2-negative), and we compared the performance against two pathologists on 100 512x512 patches (50 HER2-positive, 50 HER2-negative).
Overall, the model achieved an accuracy of 73\% while the pathologists achieved 58\% and 47\%, respectively.
... For managing the bare minimum of medical data, transfer learning offers the ideal solution, as it dramatically accelerates the training process and reduces the computational cost of the network. In addition, freezing [34] and fine-tuning [35] techniques can be applied to further modify the DL model to achieve even better accuracy. Figure 4 shows the implementation strategies of transfer learning for training segmentation models. ...
Simple Summary
Differentiating growth patterns of the tumor glands in prostate biopsy tissue images is a challenging task for pathologists. Therefore, advanced technology, especially deep learning techniques, is needed to improve cancer diagnosis and reduce the workload of the pathologist. In this research work, we aimed to analyze whole-slide images of prostate biopsies and differentiate between stroma, benign, and cancer tissue components through deep learning techniques. Instead of image classification, we developed different deep CNN models for tissue-level prostate cancer adenocarcinoma histological segmentation. With these techniques, different patterns in a whole-slide image can be analyzed for cancer diagnosis.
Abstract
Recent advances in computer-aided detection via deep learning (DL) now allow for prostate cancer to be detected automatically and recognized with extremely high accuracy, much like other medical diagnoses and prognoses. However, researchers are still limited by the Gleason scoring system. The histopathological analysis involved in assigning the appropriate score is a rigorous, time-consuming manual process that is constrained by the quality of the material and the pathologist’s level of expertise. In this research, we implemented a DL model using transfer learning on a set of histopathological images to segment cancerous and noncancerous areas in whole-slide images (WSIs). In this approach, the proposed Ensemble U-net model was applied for the segmentation of stroma, cancerous, and benign areas. The WSI dataset of prostate cancer was collected from the Kaggle repository, which is publicly available online. A total of 1000 WSIs were used for region segmentation. From this, 8100 patch images were used for training, and 900 for testing. The proposed model demonstrated an average dice coefficient (DC), intersection over union (IoU), and Hausdorff distance of 0.891, 0.811, and 15.9, respectively, on the test set, with corresponding masks of patch images. The manipulation of the proposed segmentation model improves the ability of the pathologist to predict disease outcomes, thus enhancing treatment efficacy by isolating the cancerous regions in WSIs.
... For the model architecture, we used EfficientNetB1 [42] starting with pre-trained weights on ImageNet. We used similar training methodology as [25,43]. For clarity, we highlight the main parts below. ...
... As for transfer learning, colon poorly differentiated adenocarcinoma classification model (Colon poorly ADC (x20, 512)) [55] was selected as an initial weight due to its highest ROC-AUC (0.889, CI: 0.861 -0.914) and lowest log-loss (0.415, CI: 0.378 -0.457) ( Table 2) on test set ( Table 1). The other existing deep learning models ( Table 2) we have used to compare ROC-AUC and logloss performances were described previously: Stomach ADC, AD (x10, 512) [24]; Stomach signet ring cell carcinoma (SRCC) (x10, 224) [54]; Stomach poorly ADC (x20, 224) [43]; Colon ADC, AD (x10, 512) [24]; Pancreas EUS-FNA ADC (x10, 224) [56]; Breast IDC, DCIS (x10, 224) [57]. As for FS pre-training, we have used manually drawing annotations by pathologists Fig. 2.For test set (Table 1), we computed the ROC-AUC, log loss, accuracy, sensitivity, and specificity and summarized in Table 3 and Fig. 3. ...
... In this study, we trained deep learning models for the classification of indolent and aggressive prostate adenocarcinoma in core needle biopsy WSIs to make an inference for patients' optimum clinical interventions (active surveillance or definitive therapy). We trained deep learning models using a combination of transfer learning [25,41,55], weakly supervised [53], and fully supervised [24,43,54] learning approaches. The evaluation results on the WSI level showed no significant differences between transfer learning and weakly supervised learning model (TL-Colon poorly ADC (x20, 512) and WS) and transfer learning, fully and weakly supervised learning model (TL-Colon poorly ADC (x20, 512) and FS+WS) ( Table 3). ...
Background
Prostate cancer is often a slowly progressive indolent disease. Unnecessary treatments from overdiagnosis are a significant concern, particularly low-grade disease. Active surveillance has being considered as a risk management strategy to avoid potential side effects by unnecessary radical treatment. In 2016, American Society of Clinical Oncology (ASCO) endorsed the Cancer Care Ontario (CCO) Clinical Practice Guideline on active surveillance for the management of localized prostate cancer.
Methods
Based on this guideline, we developed a deep learning model to classify prostate adenocarcinoma into indolent (applicable for active surveillance) and aggressive (necessary for definitive therapy) on core needle biopsy whole slide images (WSIs). In this study, we trained deep learning models using a combination of transfer, weakly supervised, and fully supervised learning approaches using a dataset of core needle biopsy WSIs (n=1300). In addition, we performed an inter-rater reliability evaluation on the WSI classification.
Results
We evaluated the models on a test set (n=645), achieving ROC-AUCs of 0.846 for indolent and 0.980 for aggressive. The inter-rater reliability evaluation showed s-scores in the range of 0.10 to 0.95, with the lowest being on the WSIs with both indolent and aggressive classification by the model, and the highest on benign WSIs.
Conclusion
The results demonstrate the promising potential of deployment in a practical prostate adenocarcinoma histopathological diagnostic workflow system.
... Although there have been many studies on AI applications in the pathological diagnosis of GC in the recent past, there are few studies regarding tumor subclassification (Table 3). Yasuda et al [66] investigated the features and classification of GC tissues by using supervised ML algorithms. The results showed that this method reliably identifies morphological changes in tumors with different grades. ...
... In the field of computational pathology as a computer-aided detection (CADe) or computer-aided diagnosis (CADx), deep learning models have been widely applied in the histopathological cancer classification on whole-slide images (WSIs), cancer cell detection and segmentation, and the stratification of patient clinical outcomes. [11][12][13][14][15][16][17][18][19][20][21][22][23][24] Previous studies have looked into applying deep learning models for ADC classification in stomach, [24][25][26] and for gastric poorly differentiated ADC classification on WSIs. 25,26 However, the existing poorly differentiated ADC models did not classify poorly differentiated ADC well on gastric ESD WSIs. ...
... [11][12][13][14][15][16][17][18][19][20][21][22][23][24] Previous studies have looked into applying deep learning models for ADC classification in stomach, [24][25][26] and for gastric poorly differentiated ADC classification on WSIs. 25,26 However, the existing poorly differentiated ADC models did not classify poorly differentiated ADC well on gastric ESD WSIs. ...
... Based on the findings in this study and previous study, 25 we have summarized the possible application of our deep learning ...
Objective: Endoscopic submucosal dissection (ESD) is the preferred technique for treating early gastric cancers including poorly differentiated adenocarcinoma without ulcerative findings. The histopathological classification of poorly differentiated adenocarcinoma including signet ring cell carcinoma is of pivotal importance for determining further optimum cancer treatment(s) and clinical outcomes. Because conventional diagnosis by pathologists using microscopes is time-consuming and limited in terms of human resources, it is very important to develop computer-aided techniques that can rapidly and accurately inspect large number of histopathological specimen whole-slide images (WSIs). Computational pathology applications which can assist pathologists in detecting and classifying gastric poorly differentiated adenocarcinoma from ESD WSIs would be of great benefit for routine histopathological diagnostic workflow. Methods: In this study, we trained the deep learning model to classify poorly differentiated adenocarcinoma in ESD WSIs by transfer and weakly supervised learning approaches. Results: We evaluated the model on ESD, endoscopic biopsy, and surgical specimen WSI test sets, achieving and ROC-AUC up to 0.975 in gastric ESD test sets for poorly differentiated adenocarcinoma. Conclusion: The deep learning model developed in this study demonstrates the high promising potential of deployment in a routine practical gastric ESD histopathological diagnostic workflow as a computer-aided diagnosis system.
... In computational pathology, deep learning models have been widely applied in histopathological cancer classification on WSIs, cancer cell detection and segmentation, and the stratification of patient outcomes [15][16][17][18][19][20][21][22][23][24][25][26][27][28]. Previous studies have looked into applying deep learning models for adenocarcinoma classification in stomach [28][29][30], colon [28,31], lung [29,32], and breast [33,34] histopathological specimen WSIs. In a previous study, we trained a prostate adenocarcinoma classification model on needle biopsy WSIs [35] and evaluated the models on both needle biopsy and TUR-P WSI test sets to confirm their applications in different types of specimens, achieving an ROC-AUC of up to 0.978 in needle biopsy test sets; however, the model under-performed on TUR-P WSIs. ...
... In computational pathology, deep learning models have been widely applied in histopathological cancer classification on WSIs, cancer cell detection and segmentation, and the stratification of patient outcomes [15][16][17][18][19][20][21][22][23][24][25][26][27][28]. Previous studies have looked into applying deep learning models for adenocarcinoma classification in stomach [28][29][30], colon [28,31], lung [29,32], and breast [33,34] histopathological specimen WSIs. In a previous study, we trained a prostate adenocarcinoma classification model on needle biopsy WSIs [35] and evaluated the models on both needle biopsy and TUR-P WSI test sets to confirm their applications in different types of specimens, achieving an ROC-AUC of up to 0.978 in needle biopsy test sets; however, the model under-performed on TUR-P WSIs. ...
... Figure 1 shows an overview of the training method. The training methodology that we used in the present study was exactly the same as reported in our previous studies [29,35]. For the sake of completeness, we repeat the methodology here. ...
The transurethral resection of the prostate (TUR-P) is an option for benign prostatic diseases, especially nodular hyperplasia patients who have moderate to severe urinary problems that have not responded to medication. Importantly, incidental prostate cancer is diagnosed at the time of TUR-P for benign prostatic disease. TUR-P specimens contain a large number of fragmented prostate tissues; this makes them time consuming to examine for pathologists as they have to check each fragment one by one. In this study, we trained deep learning models to classify TUR-P WSIs into prostate adenocarcinoma and benign (non-neoplastic) lesions using transfer and weakly supervised learning. We evaluated the models on TUR-P, needle biopsy, and The Cancer Genome Atlas (TCGA) public dataset test sets, achieving an ROC-AUC up to 0.984 in TUR-P test sets for adenocarcinoma. The results demonstrate the promising potential of deployment in a practical TUR-P histopathological diagnostic workflow system to improve the efficiency of pathologists.
... For the model architecture, we used Efficient-NetB1Tan and Le (2019) starting with pre-trained weights on ImageNet. We used similar training methodology as Kanavati and Tsuneki (2021a); Tsuneki et al (2022). For clarity, we highlight the main parts below. ...
... As for transfer learning, colon poorly differentiated adenocarcinoma classification model (Colon poorly ADC (x20, 512)) Tsuneki and Kanavati (2021) was selected as an initial weight due to its highest ROC-AUC (0.889, CI: 0.861 -0.914) and lowest log-loss (0.415, CI: 0.378 -0.457) ( Table 2) on test set (Table 1). The other existing deep learning models (Table 2) we have used to compare ROC-AUC and log-loss performances were described previously: Stomach ADC, AD (x10, 512) Iizuka et al (2020); Stomach signet ring cell carcinoma (SRCC) (x10, 224) Kanavati et al (2021); Stomach poorly ADC (x20, 224) Kanavati and Tsuneki (2021a); Colon ADC, AD (x10, 512) Iizuka et al (2020); Pancreas EUS-FNA ADC (x10, 224) Naito et al (2021); Breast IDC, DCIS (x10, 224) Kanavati et al (2022). As for FS pre-training, we have used manually drawing annotations by pathologists 2.For test set (Table 1), we computed the ROC-AUC, log loss, accuracy, sensitivity, and specificity and summarized in Table 3 and Fig. 3. ...
... In this study, we trained deep learning models for the classification of indolent and aggressive prostate adenocarcinoma in core needle biopsy WSIs to make an inference for patients' optimum clinical interventions (active surveillance or definitive therapy). We trained deep learning models using a combination of transfer learning Kanavati and Tsuneki (2021b); Tsuneki and Kanavati (2021); Tsuneki et al (2022), weakly supervised Kanavati et al (2020), and fully supervised Iizuka et al (2020); Kanavati et al (2021); Kanavati and Tsuneki (2021a) learning approaches. The evaluation results on the WSI level showed no significant differences between transfer learning and weakly supervised learning model (TL-Colon poorly ADC (x20, 512) and WS) and transfer learning, fully and weakly supervised learning model (TL-Colon poorly ADC (x20, 512) and FS+WS) (Table 3). ...
Prostate cancer is often a slowly progressive indolent disease. Unnecessary treatments from overdiagnosis are a significant concern, particularly low-grade disease. Active surveillance has being considered as a risk management strategy to avoid potential side effects by unnecessary radical treatment. In 2016, American Society of Clinical Oncology (ASCO) endorsed the Cancer Care Ontario (CCO) Clinical Practice Guideline on active surveillance for the management of localized prostate cancer. Based on this guideline, we developed a deep learning model to classify prostate adenocarcinoma into indolent (applicable for active surveillance) and aggressive (necessary for definitive therapy) on core needle biopsy whole slide images (WSIs). In this study, we trained deep learning models using a combination of transfer, weakly supervised, and fully supervised learning approaches using a dataset of core needle biopsy WSIs (n=1300). We evaluated the models on a test set (n=645), achieving ROC-AUCs 0.846 (indolent) and 0.980 (aggressive). The results demonstrate the promising potential of deployment in a practical prostate adenocarcinoma histopathological diagnostic workflow system.
