Weakly supervised learning for multi-organ adenocarcinoma classification in whole slide images
The primary screening by automated computational pathology algorithms of the presence or absence of adenocarcinoma in biopsy specimens (e.g., endoscopic biopsy, transbronchial lung biopsy, and needle biopsy) of possible primary organs (e.g., stomach, colon, lung, and breast) and radical lymph node d...
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| Veröffentlicht in: | PloS one 2022-11, Vol.17 (11), p.e0275378-e0275378 |
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| creator | Tsuneki, Masayuki Kanavati, Fahdi |
| description | The primary screening by automated computational pathology algorithms of the presence or absence of adenocarcinoma in biopsy specimens (e.g., endoscopic biopsy, transbronchial lung biopsy, and needle biopsy) of possible primary organs (e.g., stomach, colon, lung, and breast) and radical lymph node dissection specimen is very useful and should be a powerful tool to assist surgical pathologists in routine histopathological diagnostic workflow. In this paper, we trained multi-organ deep learning models to classify adenocarcinoma in biopsy and radical lymph node dissection specimens whole slide images (WSIs). We evaluated the models on five independent test sets (stomach, colon, lung, breast, lymph nodes) to demonstrate the feasibility in multi-organ and lymph nodes specimens from different medical institutions, achieving receiver operating characteristic areas under the curves (ROC-AUCs) in the range of 0.91 -0.98. |
| doi_str_mv | 10.1371/journal.pone.0275378 |
| format | Article |
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In this paper, we trained multi-organ deep learning models to classify adenocarcinoma in biopsy and radical lymph node dissection specimens whole slide images (WSIs). We evaluated the models on five independent test sets (stomach, colon, lung, breast, lymph nodes) to demonstrate the feasibility in multi-organ and lymph nodes specimens from different medical institutions, achieving receiver operating characteristic areas under the curves (ROC-AUCs) in the range of 0.91 -0.98.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0275378</identifier><identifier>PMID: 36417401</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adenocarcinoma ; Adenocarcinoma - pathology ; Algorithms ; Analysis ; Biology and Life Sciences ; Biopsy ; Breast ; Breast - pathology ; Cancer ; Care and treatment ; Classification ; Colon ; Computer applications ; Deep learning ; Diagnosis ; Dissection ; Health care facilities ; Humans ; Image classification ; Lung cancer ; Lungs ; Lymph nodes ; Lymph Nodes - pathology ; Lymphatic system ; Machine learning ; Medical imaging ; Medical screening ; Medicine and Health Sciences ; Metastasis ; Pathology ; Probability ; Stomach ; Supervised learning ; Supervised Machine Learning ; Surgical instruments ; Tumors ; Workflow</subject><ispartof>PloS one, 2022-11, Vol.17 (11), p.e0275378-e0275378</ispartof><rights>Copyright: © 2022 Tsuneki, Kanavati. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</rights><rights>COPYRIGHT 2022 Public Library of Science</rights><rights>2022 Tsuneki, Kanavati. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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| subjects | Adenocarcinoma Adenocarcinoma - pathology Algorithms Analysis Biology and Life Sciences Biopsy Breast Breast - pathology Cancer Care and treatment Classification Colon Computer applications Deep learning Diagnosis Dissection Health care facilities Humans Image classification Lung cancer Lungs Lymph nodes Lymph Nodes - pathology Lymphatic system Machine learning Medical imaging Medical screening Medicine and Health Sciences Metastasis Pathology Probability Stomach Supervised learning Supervised Machine Learning Surgical instruments Tumors Workflow |
| title | Weakly supervised learning for multi-organ adenocarcinoma classification in whole slide images |
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