A deep learning model for breast ductal carcinoma in situ classification in whole slide images

Kanavati, Fahdi; Ichihara, Shin

doi:10.1101/2022.01.14.22269329

Cited by 7 publications

References 63 publications

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Weakly supervised and transfer learning for adenocarcinoma classification in transurethral resection of the prostate whole slide images

Abe

Kanavati²

2022

Preprint

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The transurethral resection of the prostate (TUR-P) is generally considered 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 are diagnosed at the time of TUR-P for benign prostatic disease. Since diagnosing a large number of cases containing TUR-P specimens which are characterized by a very large volume of tissue fragments by pathologists using a conventional microscope is time-consuming and limited in terms of human resources. Thus, it is necessary to develop new techniques which can rapidly and accurately screen large numbers of TUR-P specimens. Computational pathology applications which can assist pathologists in detecting prostate adenocarcinoma from TUR-P whole slide images (WSIs) would be of great benefit for routine histopathological workflow. 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 high promising potential of deployment in a practical TUR-P histopathological diagnostic workflow system.

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Weakly supervised and transfer learning for adenocarcinoma classification in transurethral resection of the prostate whole slide images

Abe

Kanavati²

2022

Preprint

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Weakly supervised learning for poorly differentiated adenocarcinoma classification in gastric endoscopic submucosal dissection whole slide images

Ichihara

Kanavati²

2022

Preprint

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The 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 numbers 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. In this study, we trained the deep learning model to classify poorly differentiated adenocarcinoma in ESD WSIs by transfer and weakly supervised learning approaches. 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. 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

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Inference of core needle biopsy whole slide images requiring definitive therapy for prostate cancer

Abe

Ichihara

Kanavati³

2022

Preprint

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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.

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A deep learning model for breast ductal carcinoma in situ classification in whole slide images

Cited by 7 publications

References 63 publications

Weakly supervised and transfer learning for adenocarcinoma classification in transurethral resection of the prostate whole slide images

Weakly supervised and transfer learning for adenocarcinoma classification in transurethral resection of the prostate whole slide images

Weakly supervised learning for poorly differentiated adenocarcinoma classification in gastric endoscopic submucosal dissection whole slide images

Inference of core needle biopsy whole slide images requiring definitive therapy for prostate cancer

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