[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123716-en":3,"doc-seo-123716-105":30,"detail-sidebar-cat-0-en-105":95},{"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},123716,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","A machine learning-based radiomics model for prediction of tumor mutation burden in gastric cancer","A machine learning-based radiomics approach is evaluated for predicting tumor mutation burden (TMB) status in gastric cancer using contrast-enhanced CT data. Radiomics features are extracted from three imaging phases across 256 retrospectively collected patients, with LASSO used for feature screening and multiple ML algorithms compared for classifier selection. Logistic regression builds the final model, achieving strong discrimination (training and validation AUCs around 0.89 and 0.86). Spearman analysis shows a positive correlation with TMB (ρ≈0.62), and SHAP provides model explainability, supporting stable predictive performance across relevant mutation ranges.","TYPE Original Research PUBLISHED 06 November 2023 DOI 10.3389/fgene.2023.1283090  \nOPEN ACCESS  \nEDITED BY  \nXiaozhou Yu,  \nNorthwestern University, United States  \nREVIEWED BY  \nMa Xinxing,  \nThe First Affiliated Hospital of Soochow University, China  \nYuan Feng Gao,  \nCapital Medical University, China  \n*CORRESPONDENCE  \nZhaoxiang Ye,  \n [zye@tmu.edu.cn](zye@tmu.edu.cn)  \n†These authors have contributed equally to this work and share first authorship  \nRECEIVED 25 August 2023  \nACCEPTED 23 October 2023  \nPUBLISHED 06 November 2023  \nCITATION  \nMa T, Zhang Y, Zhao M, Wang L, Wang Hand Ye Z (2023), A machine learningbased radiomics model for prediction of tumor mutation burden in gastric cancer. Front. Genet. 14:1283090 .  \ndoi: 10.3389/fgene.2023.1283090  \nCOPYRIGHT  \n© 2023 Ma, Zhang, Zhao, Wang, Wang and Ye. This is an open-access article distributed under the terms of the  \nCreative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nA machine learning-based radiomics model for prediction of tumor mutation burden in gastric cancer  \nTingting Ma 1,2,3,4,5†, Yuwei Zhang 2,3,4,5†, Mengran Zhao 1,2,3,4,5, Lingwei Wang 2,3,4,5, Hua Wang 1,2,3,4,5 and Zhaoxiang Ye 2,3,4,5*  \n1Department of Radiology, Tianjin Cancer Hospital Airport Hospital, Tianjin, China, 2Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China, 3National Clinical Research Center for Cancer, Tianjin, China, 4Tianjin ’s Clinical Research Center for Cancer, Tianjin, China, 5The Key Laboratory of Cancer Prevention and Therapy, Tianjin, China  \nPurpose: To evaluate the potential of machine learning (ML)-based radiomics approach for predicting tumor mutation burden (TMB) in gastric cancer (GC) .  \nMethods: The contrast enhanced CT (CECT) images with corresponding clinical information of 256 GC patients were retrospectively collected. Patients were separated into training set (n = 180) and validation set (n = 76) . A total of 3,390 radiomics features were extracted from three phases images of CECT. The least absolute shrinkage and selection operator (LASSO) model was used for feature screening. Seven machine learning(ML)algorithms were employed tofind the optimal classifier. The predictive ability of radiomics model (RM) was evaluated with receiver operating characteristic. The correlation between RM and TMB values was evaluated using Spearman’s correlation coefficient. The explainability of RM was assessed by the Shapley Additive explanations (SHAP) method.  \nResults: Logistic regression algorithm was chosen for model construction. The RM showed good predictive ability of TMB status with AUCs of 0.89 [95% confidence interval (CI): 0.85–0.94] and 0.86 (95% CI: 0.74–0.98) in the training and validation sets. The correlation analysis revealed a good correlation between RM and TMB levels (correlation coefficient: 0 . 62, p \u003C 0. 001) . The RM also showed favorable and stable predictive accuracy within the cutoff value range 6–16 mut/Mb in both sets.  \nConclusion: The ML-based RM offered a promising image biomarker for predicting TMB status in GC patients.  \nKEYWORDS  \nradiomics, tumor mutation burden, machine learning, gastric cancer, computed tomograph  \nIntroduction  \nImmune checkpoint inhibitors (ICIs), represented by programmed cell death protein 1 (PD-1) and programmed death-ligand 1 (PD-L1) have revolutionized the treatment paradigm and shown exciting efficacy in a variety of solid tumors. Several clinical trials have highlighted the effectiveness and safety of PD-1 inhibitors in the management of gastric cancer (GC) patients (Kang et al., 2017; Fuchs et al., 2018; Taieb et al., 2018) . However, the  \nFrontiers ","cbCaitMGcbyGfjZr","https://ap.wps.com/l/cbCaitMGcbyGfjZr","pdf",1867405,1,9,"English","en",105,"# Purpose\n# Methods\n## Feature extraction and model building\n## Evaluation and explainability\n# Results\n# Conclusion","[{\"question\":\"What is the purpose of the study?\",\"answer\":\"To evaluate whether a machine learning-based radiomics approach can predict tumor mutation burden status in gastric cancer.\"},{\"question\":\"How was the predictive model built?\",\"answer\":\"Contrast-enhanced CT images from 256 patients were used to extract 3,390 radiomics features; LASSO performed feature screening, and multiple ML algorithms were tested, with logistic regression selected for the final model.\"},{\"question\":\"How well did the model predict TMB status?\",\"answer\":\"The model showed good discrimination, with AUCs of 0.89 in the training set and 0.86 in the validation set, and it correlated positively with TMB values (Spearman correlation ρ≈0.62, p\\u003c0.001).\"},{\"question\":\"Was model explainability assessed?\",\"answer\":\"Yes. Explainability was evaluated using SHAP (Shapley Additive explanations) to interpret the radiomics model’s contributions.\"}]","A machine learning-based radiomics model for prediction of tumor mutation burden in gastric cancer | PDF",1785818160,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":90,"head_meta":92,"extra_data":94,"updated_unix":28},"a-machine-learning-based-radiomics-model-for-prediction-of-tumor-mutation-burden-in-gastric-cancer","",{"@graph":36,"@context":89},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-machine-learning-based-radiomics-model-for-prediction-of-tumor-mutation-burden-in-gastric-cancer/123716/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"What is the purpose of the study?","Question",{"text":75,"@type":76},"To evaluate whether a machine learning-based radiomics approach can predict tumor mutation burden status in gastric cancer.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the predictive model built?",{"text":80,"@type":76},"Contrast-enhanced CT images from 256 patients were used to extract 3,390 radiomics features; LASSO performed feature screening, and multiple ML algorithms were tested, with logistic regression selected for the final model.",{"name":82,"@type":73,"acceptedAnswer":83},"How well did the model predict TMB status?",{"text":84,"@type":76},"The model showed good discrimination, with AUCs of 0.89 in the training set and 0.86 in the validation set, and it correlated positively with TMB values (Spearman correlation ρ≈0.62, p\u003C0.001).",{"name":86,"@type":73,"acceptedAnswer":87},"Was model explainability assessed?",{"text":88,"@type":76},"Yes. Explainability was evaluated using SHAP (Shapley Additive explanations) to interpret the radiomics model’s contributions.","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,119,124,127,131,134,138],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},6,"Technology",50,"technology",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":125,"slug":126},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":129,"slug":130},"Religion & Spirituality",20,"religion-spirituality",{"id":129,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":129,"slug":133},"World Cup","world-cup",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":135,"slug":137},10,"Lifestyle","lifestyle",{"id":139,"doc_module":4,"doc_module_name":46,"category_name":140,"show_sort_weight":110,"slug":141},19,"General","general"]