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Conduct a retrospective study of 101 patients with pathologically confirmed lymph node status, using MRI MAGiC (SyMRI) including K-means clustering of intra-tumoral VOIs into three spatial habitats. Interpret predictions with SHAP and build a radiomics nomogram integrating independent variables. Habitat radiomics with high T1 and moderately low T2 outperforms whole-tumor radiomics, improving AUC versus clinical diagnosis in both cohorts. ",{"@graph":69,"@context":123},[70,84,106],{"@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":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/an-interpretable-machine-learning-model-with-synthetic-mri-based-habitat-radiomics-for-predicting-lymph-node-metastasis-in-oral-cancer/355501/",{"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/an-interpretable-machine-learning-model-with-synthetic-mri-based-habitat-radiomics-for-predicting-lymph-node-metastasis-in-oral-cancer/355501.png","ImageObject",300,407,{"name":92,"@type":93},"Riley West","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-27","2026-09-22",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",16,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What is the study’s main goal?","Question",{"text":113,"@type":114},"To develop an interpretable habitat-based radiomics model using SyMRI-derived quantitative T1 and T2 maps for predicting lymph node metastasis in oral cancer.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How are the “habitats” defined for the radiomics model?",{"text":118,"@type":114},"Intra-tumoral VOIs are clustered into three spatial habitats using the K-means algorithm, and radiomics models are built for the whole tumor and for clustered regions.",{"name":120,"@type":111,"acceptedAnswer":121},"How is model interpretability achieved?",{"text":122,"@type":114},"The Shapley Additive Explanations (SHAP) method is used to interpret the predictions.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},355501,1790471810,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":145,"read_time":41},1099523885074,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","Cancer Medicine  \n|  RESEARCH ARTICLE  OPEN ACCESS \u003Cbr>An Interpretable Machine Learning Model With Synthetic MRI-Based Habitat Radiomics for Predicting Lymph Node Metastasis in Oral Cancer\u003Cbr>Rui Wang1  | Jiliang Ren1 | Yong Zhang2 | Jie Shi2 | Ying Yuan1 | Xiaofeng Tao1 \u003Cbr>1Department of Radiology, Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, Shanghai, China | 2MR Research, GE Healthcare, Shanghai, China\u003Cbr>Correspondence: Ying Yuan ([yuany83@163.com](yuany83@163.com)) | Xiaofeng Tao ([cjr.taoxiaofeng@vip.163.com](cjr.taoxiaofeng@vip.163.com))\u003Cbr>Received: 12 November 2025 | Revised: 6 March 2026 | Accepted: 30 March 2026\u003Cbr>Keywords: habitat | lymph node metastasis | radiomics | SyMRI |  |\n| --- | --- |\n| ABSTRACT\u003Cbr>Purpose: To develop an interpretable radiomics model based on habitat for predicting lymph node metastasis in oral cancer using Synthetic MRI (SyMRI) .\u003Cbr>Methods: A retrospective study of 101 oral cancer patients with pathologically confirmed lymph node status was performed, dividing them into a training cohort (N = 71) and a test cohort (N = 30) . All patients underwent MRI, including the MAGiC (SyMRI) sequence. Intra-tumoral volumes of interest (VOIs) were clustered into three spatial habitats using the K-means algorithm. Radiomics models were developed for the entire tumor and clustered intra-tumor regions, based on features from synthetic quantitative T1 and T2 maps. The Shapley Additive Explanations (SHAP) method was used to interpret predictions. A radiomics nomogram was constructed by integrating independent variables, including the radiomics score and MRI-reported status. Predictive performance was evaluated using AUC, DeLong's test, calibration curves, and decision curve analysis (DCA) . Results: The habitat with the highest T1 and a moderately low T2 values within the tumor showed the best predictive performance, compared to whole-tumor radiomics (training: 0.87 vs. 0.82; test: 0.74 vs. 0.62) . The radiomics nomogram, combining radiomics features and independent clinical variables, outperformed clinical diagnosis alone in both the training cohort (0.92 vs. 0.80; p = 0.0023) and test cohort (0.84 vs. 0.73; p = 0.0362) .\u003Cbr>Conclusion: The habitat-based radiomics signature, integrating SyMRI-derived quantitative T1 and T2 maps, provides enhanced predictive accuracy for preoperative lymph node metastasis in oral cancer compared to clinical diagnosis. |  |\n| 1 | Introduction | diagnoses and 14,1465 deaths in Asia in 2022 [1] . The presence of lymph node metastasis is significantly associated with a poor |\n| Oral cancers, accounting for approximately 3%–5% of all ma- | prognosis, leading to a 50% reduction in overall survival rates. |\n| lignancies, are widely acknowledged as one of the most prev- | Performing upfront elective neck dissection (END) can effec- |\n| alent global malignancies, with an estimated 25,8440 new | tively avoid missed diagnoses, whereas unnecessary END may |\n| Abbreviations: AUC, area under the curve; DCA, decision curve analysis; DOI, depth of invasion; END, elective neck dissection; ICC, interclass correlation coefficient; LASSO, least absolute shrinkage and selection operator; LightGBM, light gradient boosting machine; LR, logistic regression; MAGiC, magnetic resonance image compilation; NRI, categorical net reclassification improvement; RF, random forest; ROC, receiver operating characteristic; SHAP, Shapley Additive Explanations; SMOTE, synthetic minority over-sampling technique; SVM, support vector machine; SyMRI, synthetic MRI; VOIs, volumes of interest; XGBoost, eXtreme gradient boosting.\u003Cbr>Rui Wang and Jiliang Ren have contributed equally to this study and should be considered as co-first authors. |  |\n| This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercia","cbCaiam7g0pYiTIo","https://ap.wps.com/l/cbCaiam7g0pYiTIo","pdf",3682537,12,"English","# Abstract\n## Purpose\n## Methods\n## Results\n## Conclusion\n# Introduction\n## Background and clinical need\n## SyMRI and MAGiC\n## Quantitative T1/T2 and prior evidence\n## Radiomics and interpretability","[{\"question\":\"What is the study’s main goal?\",\"answer\":\"To develop an interpretable habitat-based radiomics model using SyMRI-derived quantitative T1 and T2 maps for predicting lymph node metastasis in oral cancer.\"},{\"question\":\"How are the “habitats” defined for the radiomics model?\",\"answer\":\"Intra-tumoral VOIs are clustered into three spatial habitats using the K-means algorithm, and radiomics models are built for the whole tumor and for clustered regions.\"},{\"question\":\"How is model interpretability achieved?\",\"answer\":\"The Shapley Additive Explanations (SHAP) method is used to interpret the predictions.\"}]","An Interpretable Machine Learning Model With Synthetic MRI-Based Habitat Radiomics for Predicting Lymph Node Metastasis in Oral Cancer | PDF",1790119430]