[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122107-en":3,"doc-seo-122107-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122107,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",7,"Healthcare","Machine-Learning-Based Classification Model to Address Diagnostic Challenges in Transbronchial Lung Biopsy - Article","Background: When specimens are obtained from pulmonary nodules via transbronchial lung biopsy (TBLB), separating truly benign findings from tumor-related mis-sampling is difficult, particularly when tumor or atypical cells are absent. Methods: A machine-learning classifier was developed using micro-environmental histologic markers evaluated by three pathologists across 251 TBLB cases with clinical follow-up labels; an XGBoost gradient-boosted decision-tree model was trained and tested on split sets. Results: Interface bronchitis/bronchiolitis showed the strongest mild-to-moderate benign association, while fibroelastosis was the only mild malignant indicator; test AUC was 0.78. Conclusion: The model may improve diagnostic accuracy and reduce repeat sampling and treatment delays.","cancers   \nArticle  \nMachine-Learning-Based Classification Model to Address Diagnostic Challenges in Transbronchial Lung Biopsy  \nHisao Sano 1,2,3,†, Ethan N. Okoshi 1,†, Yuri Tachibana 1,3, Tomonori Tanaka 2,4, Kris Lami 1, Wataru Uegami 3, Yoshio Ohta 2, Luka Brcic 5, Andrey Bychkov 3 and Junya Fukuoka 1,3, *  \nCitation: Sano, H.; Okoshi, E.N.; Tachibana, Y.; Tanaka, T.; Lami, K.; Uegami, W.; Ohta, Y.; Brcic, L.; Bychkov, A.; Fukuoka, J.  \nMachine-Learning-Based Classification Model to Address Diagnostic Challenges in Transbronchial Lung Biopsy. Cancers 2024, 16, 731. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/cancers16040731](10.3390/cancers16040731)  \nAcademic Editor: Akiteru Goto  \nReceived: 5 January 2024  \nRevised: 29 January 2024  \nAccepted: 7 February 2024  \nPublished: 9 February 2024  \nCopyright: © 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://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Pathology Informatics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki 852-8588, Nagasaki, Japan; [sanohisao5584@gmail.com](sanohisao5584@gmail.com) (H.S.); [ethanokoshi@gmail.com](ethanokoshi@gmail.com) (E.N.O.); [tachibana.yuri@kameda.jp](tachibana.yuri@kameda.jp) (Y.T.); [krislami839@gmail.com](krislami839@gmail.com) (K.L.)  \n2 Department of Diagnostic Pathology, Izumi City General Hospital, Izumi 594-0073, Osaka, Japan; [tanaka.t.tomonori@gmail.com](tanaka.t.tomonori@gmail.com) (T.T.); [yoshio.oota@tokushukai.jp](yoshio.oota@tokushukai.jp) (Y.O.)  \n3 Department of Pathology, Kameda Medical Center, Kamogawa 296-8602, Chiba, Japan; [uegami.wataru@kameda.jp](uegami.wataru@kameda.jp) (W.U.); [bychkov.andrey@kameda.jp](bychkov.andrey@kameda.jp) (A.B.)  \n4 Department of Pathology, Kobe University Graduate School of Medicine, Kobe 650-0017, Hyogo, Japan  \n5 Diagnostic and Research Institute of Pathology, Medical University of Graz, 8010 Graz, Austria; [luka.brcic@medunigraz.at](luka.brcic@medunigraz.at)  \n* Correspondence: [fukuokaj@nagasaki-u.ac.jp](fukuokaj@nagasaki-u.ac.jp)  \n† These authors contributed equally to this work.  \nSimple Summary: The distinction between entirely benign and potentially mis-sampled cases presents a notable challenge in the histological examination of transbronchial lung biopsy (TBLB) specimens derived from pulmonary nodules that lack tumor or atypical cells. Such cases are often categorized as non-diagnostic. This study aims to develop a machine learning-based classifier for TBLB specimens, with a specific focus on analyzing the micro-environmental histological reactions present in TBLB and correlating these changes to either benign or malignant status, and to avoid unnecessary sampling procedures and lead to prompt treatment initiation.  \nAbstract: Background: When obtaining specimens from pulmonary nodules in TBLB, distinguishing between benign samples and mis-sampling from a tumor presents a challenge. Our objective is to develop a machine-learning-based classifier for TBLB specimens. Methods: Three pathologists assessed six pathological findings, including interface bronchitis/bronchiolitis (IB/B), plasma cell infiltration (PLC), eosinophil infiltration (Eo), lymphoid aggregation (Ly), fibroelastosis (FE), and organizing pneumonia (OP), as potential histologic markers to distinguish between benign and malignant conditions. A total of 251 TBLB cases with defined benign and malignant outcomes based on clinical follow-up were collected and a gradient-boosted decision-tree-based machine learning model (XGBoost) was trained and tested on randomly split training and test sets. Results: Five pathological changes showed independent, mild-to-moderate associations (AUC ranging from 0.58 to 0.75) with b","cbCaiusXBgA2olhu","https://ap.wps.com/l/cbCaiusXBgA2olhu","pdf",2731371,1,14,"English","en",105,"# Introduction\n## Clinical and diagnostic challenge in TBLB\n# Abstract\n## Background\n## Methods\n## Results\n## Conclusion","[{\"question\":\"What diagnostic challenge does this study target in transbronchial lung biopsy (TBLB)?\",\"answer\":\"It addresses the difficulty of distinguishing benign specimens from tumor mis-sampling when tumor or atypical cells are absent, and only inflammatory reactions or fibrosis are observed.\"},{\"question\":\"Which histological findings were used as potential markers in the model?\",\"answer\":\"Three pathologists evaluated interface bronchitis/bronchiolitis (IB/B), plasma cell infiltration (PLC), eosinophil infiltration (Eo), lymphoid aggregation (Ly), fibroelastosis (FE), and organizing pneumonia (OP) as candidate markers.\"},{\"question\":\"What were the main performance results of the machine-learning approach?\",\"answer\":\"The model achieved an AUC of 0.78 for binary classification of benign versus malignant on the test set, with IB/B as the strongest benign predictor and FE as the sole malignant indicator.\"}]","Machine-Learning-Based Classification Model to Address Diagnostic Challenges in Transbronchial Lung Biopsy - 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