Detection of Lung Cancer Lymph Node Metastases from Whole-Slide Histopathological Images Using a Two-Step Deep Learning Approach

• Material Type: 博士論文
• Title: Detection of Lung Cancer Lymph Node Metastases from Whole-Slide Histopathological Images Using a Two-Step Deep Learning Approach
• Author/Editor: PHAM, HOANG NGOC HOA
• Author Heading: PHAM, HOANG NGOC HOA
• Publication, Distribution, etc.: Elsevier Inc.
• Publication Date: 2021-03-22
• Publication Date (W3CDTF): 2021-03-22
• Alternative Title: 病理組織学標本におけるディープラーニングを用いた2ステップアプローチによる肺癌リンパ節転移の検出
• Periodical title: The American Journal of Pathology
• No. or year of volume/issue: 189 12
• Volume: 189
• Issue: 12
• Pages: 2428-2439
• ISSN (Periodical Title): ISSN : 0002-9440
• Degree grantor/type: Nagasaki University (長崎大学)
• Date Granted: 2021-03-22
• Dissertation Number: 甲医歯薬第1296号
• Degree Type: 博士(医学)
• Text Language Code: eng
• Target Audience: 一般
• Note (General): The application of deep learning for the detection of lymph node metastases on histologic slides has attracted worldwide attention due to its potentially important role in patient treatment and prognosis. Despite this attention, false-positive predictions remain problematic, particularly in the case of reactive lymphoid follicles. In this study, a novel two-step deep learning algorithm was developed to address the issue of false-positive prediction while maintaining accurate cancer detection. Three-hundred and forty-nine whole-slide lung cancer lymph node images, including 233 slides for algorithm training, 10 slides for validation, and 106 slides for evaluation, were collected. In the first step, a deep learning algorithm was used to eliminate frequently misclassified noncancerous regions (lymphoid follicles). In the second step, a deep learning classifier was developed to detect cancer cells. Using this two-step approach, errors were reduced by 36.4% on average and up to 89% in slides with reactive lymphoid follicles. Furthermore, 100% sensitivity was reached in cases of macrometastases, micrometastases, and isolated tumor cells. To reduce the small number of remaining false positives, a receiver-operating characteristic curve was created using foci size thresholds of 0.6 mm and 0.7 mm, achieving sensitivity and specificity of 79.6% and 96.5%, and 75.5% and 98.2%, respectively. A two-step approach can be used to detect lung cancer metastases in lymph node tissue effectively and with few false positives.; 長崎大学学位論文 学位記番号:博(医歯薬)甲第1296号 学位授与年月日:令和3年3月22日; Author: Hoa Hoang Ngoc Pham, Mitsuru Futakuchi, Andrey Bychkov, Tomoi Furukawa, Kishio Kuroda, and Junya Fukuoka; Citation: The American Journal of Pathology, 189(12), pp.2428-2439; 2019; identifier:Nagasaki University (長崎大学), 博士(医学) (2021-03-22)
• Source: https://nagasaki-u.repo.nii.ac.jp/?action=repository_action_common_download&item_id=26367&item_no=1&attribute_id=70&file_no=1 （fulltext）; https://nagasaki-u.repo.nii.ac.jp/?action=repository_action_common_download&item_id=26367&item_no=1&attribute_id=70&file_no=1
• Access Restrictions: インターネット公開
• Rights (production): © 2019 American Society for Investigative Pathology. Published by Elsevier Inc.
• Related Material: http://hdl.handle.net/10069/00040561
• Related Material (DOI): 10.1016/j.ajpath.2019.08.014
• Data Provider (Database): 国立情報学研究所 : 学術機関リポジトリデータベース（IRDB）（機関リポジトリ）; https://irdb.nii.ac.jp
• Original Data Provider (Database): 長崎大学 : 長崎大学学術研究成果リポジトリ