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Methods: Using anonymized imaging and clinical data, the study applied a deep learning approach (Mask R-CNN) to localize lung cancer lesions on preoperative CT and generate recurrence classification scores. Results: Training and validation produced performance metrics from bounding box, classification, mask, and RPN components, enabling mapping of predicted recurrence sites back to chest CT. Conclusion: The model supports clinicians as decision support to focus on likely recurrence regions.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/enhanced-deep-learning-model-for-precise-nodule-localization-and-recurrence-risk-prediction-following-curative-intent-surgery-for-lung-cancer/332234/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/enhanced-deep-learning-model-for-precise-nodule-localization-and-recurrence-risk-prediction-following-curative-intent-surgery-for-lung-cancer/332234.png","ImageObject",300,407,{"name":92,"@type":93},"Aria","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-23","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":24},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the study address?","Question",{"text":112,"@type":113},"Recurrence after curative-intent lung cancer surgery is not uncommon, and preoperative identification of patients at high recurrence risk remains challenging.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What data and model were used?",{"text":117,"@type":113},"The study used anonymized imaging and clinical data and applied a deep learning Mask Region-based Convolutional Neural Network (MRCNN/Mask R-CNN) to predict cancer locations and recurrence classification scores.",{"name":119,"@type":110,"acceptedAnswer":120},"How were predictions evaluated and visualized?",{"text":121,"@type":113},"Model performance was assessed using validation results and metrics derived from bounding box, classification, mask, and RPN components, and identified recurrence sites were mapped onto chest CT images.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},332234,1790885710,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":24,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},2336464648322,"https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488","PLOS ONE  \nOPEN ACCESS  \nCitation: Park J, Rho MJ, Moon MH (2024) Enhanced deep learning model for precise nodule localization and recurrence risk prediction following curative-intent surgery for lung cancer. PLoS ONE 19(7): e0300442 . [https://doi.org/10.1371/journal](https://doi.org/10.1371/journal). pone.0300442  \nEditor: Xiaohui Zhang, University of Illinois UrbanaChampaign, UNITED STATES OF AMERICA  \nReceived: December 14, 2023  \nAccepted: February 27, 2024  \nPublished: July 12, 2024  \nCopyright: © 2024 Park et al. 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.  \nData Availability Statement: Owing to strict security regulations at our institution, researchers interested in accessing the data from this study are required to contact the Data Review Board of Seoul St. Mary’s Hospital. For inquiries, please use the following contact details: telephone number +82- 02-2258-5808, email [liveinstar@cmcnu.or.kr](liveinstar@cmcnu.or.kr), with Saesbyeol Yang as the designated point of contact.  \nFunding: The authors wish to acknowledge the financial support of the Catholic Medical Center Research Foundation made in the program year of  \nRESEARCH ARTICLE  \nEnhanced deep learning model for precise nodule localization and recurrence risk prediction following curative-intent surgery for lung cancer  \nJihwan Park1, Mi Jung Rho2, Mi Hyoung Moon3 *  \n1 College of Liberal Arts, Dankook University, Cheonan-si, Chungcheongnam-do, Republic of Korea,  \n2 College of Health Science, Dankook University, Cheonan-si, Chungcheongnam-do, Republic of Korea, 3 Department of Thoracic and Cardiovascular Surgery, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea  \n* [mihyoungsophia@gmail.com](mihyoungsophia@gmail.com), [sophiamoon@catholic.ac.kr](sophiamoon@catholic.ac.kr)  \nAbstract  \nPurpose  \nRadical surgery is the primary treatment for early-stage resectable lung cancer, yet recurrence after curative surgery is not uncommon. Identifying patients at high risk of recurrence using preoperative computed tomography (CT) images could enable more aggressive surgical approaches, shorter surveillance intervals, and intensified adjuvant treatments. This study aims to analyze lung cancer sites in CT images to predict potential recurrences in high-risk individuals.  \nMethods  \nWe retrieved anonymized imaging and clinical data from an institutional database, focusing on patients who underwent curative pulmonary resections for non-small cell lung cancers. Our study used a deep learning model, the Mask Region-based Convolutional Neural Network (MRCNN), to predict cancer locations and assign recurrence classification scores. To find optimized trained weighted values in the model, we developed preprocessing python codes, adjusted dynamic learning rate, and modifying hyper parameter in the model.  \nResults  \nThe model training completed; we performed classifications using the validation dataset. The results, including the confusion matrix, demonstrated performance metrics: bounding box (0 .390), classification (0 .034), mask (0 .266), Region Proposal Network (RPN) bounding box (0 .341), and RPN classification (0 .054) . The model successfully identified lung cancer recurrence sites, which were then accurately mapped onto chest CT images to highlight areas of primary concern.  \n2020 in the form of a grant to MHM [5-2020-B0001-00262] . The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.  \nCompeting interests: P and R, who are married, participated in the project as co-participants from D University. No other authors have any conflicts of interest to disclose.  \nConclusion  \nThe trained model allows clinicians to focus on lung regions where cancer recurrence i","cbCaidZnDpGXITNQ","https://ap.wps.com/l/cbCaidZnDpGXITNQ","pdf",2950451,14,"English","# Abstract\n## Purpose\n## Methods\n## Results\n## Conclusion\n# Introduction","[{\"question\":\"What problem does the study address?\",\"answer\":\"Recurrence after curative-intent lung cancer surgery is not uncommon, and preoperative identification of patients at high recurrence risk remains challenging.\"},{\"question\":\"What data and model were used?\",\"answer\":\"The study used anonymized imaging and clinical data and applied a deep learning Mask Region-based Convolutional Neural Network (MRCNN/Mask R-CNN) to predict cancer locations and recurrence classification scores.\"},{\"question\":\"How were predictions evaluated and visualized?\",\"answer\":\"Model performance was assessed using validation results and metrics derived from bounding box, classification, mask, and RPN components, and identified recurrence sites were mapped onto chest CT images.\"}]","Enhanced deep learning model for precise nodule localization and recurrence risk prediction following curative-intent surgery for lung cancer | PDF",1789997467,35]