Machine-Learning-Based Classification Model to Address Diagnostic Challenges in Transbronchial Lung Biopsy

• Material Type: 博士論文
• Title: Machine-Learning-Based Classification Model to Address Diagnostic Challenges in Transbronchial Lung Biopsy
• Author/Editor: 佐野, 寿郎
• Author Heading: 佐野, 寿郎
• Publication, Distribution, etc.: MDPI
• Publication Date: 2024-06-05
• Publication Date (W3CDTF): 2024-06-05
• Alternative Title: 気管支肺生検の診断上の課題に対処するための機械学習ベースの分類モデル
• Periodical title: Cancers
• No. or year of volume/issue: 16 4
• Volume: 16
• Issue: 4
• Pages: art. no. 731-
• ISSN (Periodical Title): EISSN : 2072-6694
• Degree grantor/type: Nagasaki University (長崎大学)
• Date Granted: 2024-06-05
• Dissertation Number: 甲医歯薬第1617号
• Degree Type: 博士(医学)
• Text Language Code: eng
• Subject Heading: transbronchial lung biopsy (TBLB); non-diagnostic samples; Delphi method; interface bronchitis/bronchiolitis (IB/B); decision-tree based classifiers
• Target Audience: 一般
• Note (General): 長崎大学学位論文 学位記番号:博(医歯薬)甲第1617号 学位授与年月日:令和6年6月5日; Hisao Sano, Ethan N. Okoshi, Yuri Tachibana,Tomonori Tanaka, Kris Lami, Wataru Uegami, Yoshio Ohta, Luka Brcic, Andrey Bychkov and Junya Fukuoka; Cancers, 16(4), art. no. 731; 2024; Nagasaki University (長崎大学), 博士(医学) (2024-06-05)
• Format (IMT): application/pdf
• Source: fulltext; https://nagasaki-u.repo.nii.ac.jp/record/2001472/files/ISYK1617_Sano.pdf
• Access Restrictions: インターネット公開
• Rights (production): © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
• Related Material (URI): HDL; http://hdl.handle.net/10069/0002001039
• Related Material (DOI): 10.3390/cancers16040731
• 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

• Summary, etc.: Background: When obtaining specimens from pulmonary nodules in TBLB, distinguishing between benign samples and mis-sampling from a tumor presents a ch...