[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122265-en":3,"doc-seo-122265-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},122265,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",7,"Healthcare","Identifying Genetic Mutation Status in Patients with Colorectal Cancer Liver Metastases Using Radiomics-Based Machine-Learning Models","Genetic mutation status is critical for treatment selection and prognostication in colorectal cancer with liver metastases (CRLM), particularly for mutations such as KRAS. This multicenter study evaluates whether radiomics-derived CT imaging features can distinguish KRAS mutation versus no mutation and tests the approach using an external validation dataset. Pre-treatment CT scans were segmented and radiomics features computed, then machine-learning classifiers were trained and assessed on both discovery and external cohorts.","cancers   \nArticle  \nIdentifying Genetic Mutation Status in Patients with Colorectal Cancer Liver Metastases Using Radiomics-Based  \nMachine-Learning Models  \nNina Wesdorp 1,2,†, Michiel Zeeuw 1,2, *,†, Delanie van der Meulen 1,2, Iris van `t Erve 3, Zuhir Bodalal 4, Joran Roor 5, Jan Hein van Waesberghe 2,6, Shira Moos 2,6, Janneke van den Bergh 2,6, Irene Nota 2,6,  \nSusan van Dieren 2,7, Jaap Stoker 2,8, Gerrit Meijer 3, Rutger-Jan Swijnenburg 2,7, Cornelis Punt 9,10, Joost Huiskens 1,2, Regina Beets-Tan 4, Remond Fijneman 3, Henk Marquering 2,8,11, Geert Kazemier 1,2 and on behalf of the Dutch Colorectal Cancer Group Liver Expert Panel ‡  \nCitation: Wesdorp, N.; Zeeuw, M.; van der Meulen, D.; van `t Erve, I.; Bodalal, Z.; Roor, J.; van Waesberghe, J.H.; Moos, S.; van den Bergh, J.; Nota, I.; et al. Identifying Genetic Mutation Status in Patients with Colorectal Cancer Liver Metastases Using Radiomics-Based Machine-Learning Models. Cancers 2023, 15, 5648 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)cancers15235648  \nReceived: 20 September 2023  \nRevised: 16 November 2023  \nAccepted: 28 November 2023  \nPublished: 29 November 2023  \nCopyright: © 2023 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 Surgery, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands; [n.wesdorp@amsterdamumc.nl](n.wesdorp@amsterdamumc.nl) (N.W.)  \n2 Cancer Center Amsterdam, 1081 HV Amsterdam, The Netherlands  \n3 Department of Pathology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands  \n4 Department of Radiology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands  \n5 Department of Health, SAS Institute B.V., 1272 PC Huizen, The Netherlands  \n6 Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands  \n7 Department of Surgery, Amsterdam UMC, University of Amsterdam,  \n1105 AZ Amsterdam, The Netherlands  \n8 Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands  \n9 Department of Medical Oncology, Amsterdam UMC, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands  \n10 Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3584 CG Utrecht, The Netherlands  \n11 Department of Biomedical Engineering and Physics, Amsterdam UMC, University of Amsterdam, 1081 HV Amsterdam, The Netherlands  \n* [Correspondence: j.m.zeeuw@amsterdamumc.nl](Correspondence: j.m.zeeuw@amsterdamumc.nl)[ ](Correspondence: j.m.zeeuw@amsterdamumc.nl)† These authors contributed equally to this work.  \n‡ Dutch Colorectal Cancer Group Liver Expert Panel are listed in acknowledgments.  \nSimple Summary: For patients with colorectal cancer with liver metastases, it is important to determine the genetic mutations (e.g., KRAS mutations) of the liver metastases. Around 35–45% of patients with colorectal cancer liver metastases (CRLM) have a KRAS mutation, and genetic mutations are used in treatment planning and prognostication. The aim of this study was to assess if KRAS mutations could be identified on CT scans using radiomics. In the discovery cohort of 255 patients, KRAS mutations could be identified with a good accuracy. In the external validation cohort consisting of 129 patients, the radiomics model performed poorly. These results indicate that radiomics might be used to determine genetic mutations such as KRAS, but foremost emphasize the importance of the external validation of radiomics models. External validation is crucial for the assessment of clinical applicability and should be mand","cbCaioBrTcymPxxk","https://ap.wps.com/l/cbCaioBrTcymPxxk","pdf",3093471,1,12,"English","en",105,"# Introduction\n## Radiomics and genetic mutation status in CRLM\n## Study objective and dataset overview\n## Model building and internal testing\n## External validation and performance analysis\n## Clinical implications and limitations","[{\"question\":\"What clinical problem does the study address for CRLM patients?\",\"answer\":\"It targets determining genetic mutation status, especially KRAS mutations, in patients with colorectal cancer liver metastases to support treatment planning and prognosis.\"},{\"question\":\"How do the researchers use imaging data in the study?\",\"answer\":\"They segment all CRLM on pre-treatment CT scans semi-automatically and calculate radiomics features from those segmentations for model training.\"},{\"question\":\"What were the main findings for internal versus external validation performance?\",\"answer\":\"Machine-learning models showed good accuracy in the internal test set, but performance dropped substantially in the external validation dataset, with AUC values around 0.47–0.56.\"}]","Identifying Genetic Mutation Status in Patients with Colorectal Cancer Liver Metastases Using Radiomics-Based Machine-Learning Models | 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