[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126964-en":3,"doc-seo-126964-105":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},126964,687207024478,"Liam","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",7,"Healthcare","Machine learning-based radiomic analysis and growth visualization for ablation site recurrence diagnosis in follow-up CT","Objectives: Detecting ablation site recurrence (ASR) after thermal ablation is difficult because tumor recurrence closely resembles post-ablative changes on follow-up CT. This study assessed whether radiomic analysis combined with machine learning improves ASR detection and developed a visualization tool to highlight suspicious growth regions between two follow-up scans for individual patients. Methods: Radiomic features from reader-defined regions of interest were modeled using Lasso regression and XGBoost, with leave-one-out testing and difference heatmap visualization.","University of Groningen  \nMachine learning-based radiomic analysis and growth visualization for ablation site recurrence diagnosis in follow-up CT  \nYin, Yunchao; de Haas, Robbert J; Alves, Natalia; Pennings, Jan Pieter; Ruiter, Simeon J S;  \nKwee, Thomas C; Yakar, Derya Published in:  \nAbdominal radiology (New York)  \nDOI:  \n10.1007/s00261-023-04178-4  \nIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below.  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nPublication date: 2024  \nLink to publication in University of Groningen/UMCG research database  \nCitation for published version (APA):  \nYin, Y. , de Haas, R. J. , Alves, N. , Pennings, J. P. , Ruiter, S. J. S. , Kwee, T. C. , & Yakar, D. (2024) .  \nMachine learning-based radiomic analysis and growth visualization for ablation site recurrence diagnosis in follow-up CT. Abdominal radiology (New York), 49, 1122–1131 . [https://doi.org/10.1007/s00261-023-04178-](https://doi.org/10.1007/s00261-023-04178-)[ ](https://doi.org/10.1007/s00261-023-04178-)4  \nCopyright  \nOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons) .  \nThe publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license. More information can be found on the University of Groningen website: [https://www.rug.nl/library/open-access/self-archiving-pure/taverne](https://www.rug.nl/library/open-access/self-archiving-pure/taverne)amendment.  \nTake-down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nDownloaded from the University of Groningen/U MCG research database (Pure): [http://www.rug. nl/research/portal. For technical reasons the](http://www.rug. nl/research/portal. For technical reasons the)[ ](http://www.rug. nl/research/portal. For technical reasons the)[number of authors shown on this cover page is limited to 10 maximum.](number of authors shown on this cover page is limited to 10 maximum.)  \nDownload date: 01-01-2026  \nAbdominal Radiology (2024) 49:1122–1131  \n[https://doi.org/10.1007/s00261-023-04178-4](https://doi.org/10.1007/s00261-023-04178-4)  \nMachine learning‑based radiomic analysis and growth visualization for ablation site recurrence diagnosis in follow‑up CT  \nYunchao Yin1 · Robbert J. de Haas1 · Natalia Alves2 · Jan Pieter Pennings1 · Simeon J. S. Ruiter3 · Thomas C. Kwee1 · DeryaYakar1,4  \nReceived: 19 October 2023 / Revised: 22 December 2023 / Accepted: 27 December 2023 / Published online: 30 January 2024 © The Author(s) 2024  \nAbstract  \nObjectives Detecting ablation site recurrence (ASR) after thermal ablation remains a challenge for radiologists due to the similarity between tumor recurrence and post-ablative changes. Radiomic analysis and machine learning methods mayshow additional value in addressing this challenge. The present study primarily sought to determine the efficacy of radiomic analysis in detecting ASR on follow-up computed tomography (CT) scans. The second aim was to develop a visualization tool capable of emphasizing regions of ASR between follow-up scans in individual patients.  \nMaterials and methods Lasso regression and Extreme Gradient Boosting (XGBoost) classifiers were employed for modeling radiomic features extracted from regions of interest delineated by two radiologists. A leave-one-out test (LOOT) was utilized for performance evaluation. A visualization method, creating difference heatmaps (diff-maps) between two follow-up scans, was developed to emphasize regions of growth and thereby highlighting potential ASR.  \nResults A total of 55 patients, i","cbCainG455BCkTMS","https://ap.wps.com/l/cbCainG455BCkTMS","pdf",3054443,1,11,"English","en",105,"# Abstract\n# Introduction\n# Materials and methods\n## Radiomic feature modeling\n## Visualization method\n# Results\n# Conclusions","[{\"question\":\"What problem does the study target after thermal ablation?\",\"answer\":\"It targets ablation site recurrence detection, which is challenging because recurrent tumor appearance can be similar to post-ablative changes on follow-up CT.\"},{\"question\":\"Which machine learning models were used for radiomic analysis?\",\"answer\":\"Lasso regression and XGBoost classifiers were used to model radiomic features extracted from regions of interest.\"},{\"question\":\"How does the visualization component work?\",\"answer\":\"The study developed difference heatmaps (diff-maps) between two follow-up CT scans to emphasize regions of growth and highlight potential ASR.\"}]","Machine learning-based radiomic analysis and growth visualization for ablation site recurrence diagnosis in follow-up CT | 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