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This real-world cohort study evaluates whether adding the third lumbar vertebra centroid level subcutaneous fat volume (L3 CLSFV) improves a Cox regression prognostic prediction model. Two Cox models are built from clinical features and nutritional indicators, validated using time-dependent ROC, timeC-index, calibration, Kaplan-Meier, NRI, and IDI.",{"@graph":14,"@context":72},[15,34,55],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/healthcare/","Healthcare",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/lumbar-centroid-level-subcutaneous-fat-volume-increased-performance-of-prognostic-predictive-model-in-digestive-system-cancers-a-real-world-cohort-study/457189/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/lumbar-centroid-level-subcutaneous-fat-volume-increased-performance-of-prognostic-predictive-model-in-digestive-system-cancers-a-real-world-cohort-study/457189.png","ImageObject",300,407,{"name":42,"@type":43},"Fahsai","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-29","2026-09-30",true,{"@type":52,"interactionType":53,"userInteractionCount":30},"InteractionCounter",{"@type":54},"ViewAction",{"@type":56,"mainEntity":57},"FAQPage",[58,64,68],{"name":59,"@type":60,"acceptedAnswer":61},"What is the main purpose of this study?","Question",{"text":62,"@type":63},"To determine whether adding L3 CLSFV to a Cox regression prognostic model improves predictive performance for digestive system cancers.","Answer",{"name":65,"@type":60,"acceptedAnswer":66},"How were the two prognostic models constructed?",{"text":67,"@type":63},"Two Cox models (Coxm1 and Coxm2) were built using training cohort features; Coxm2 added the CT-measured L3 CLSFV.",{"name":69,"@type":60,"acceptedAnswer":70},"Which evaluation metrics were used to assess model performance?",{"text":71,"@type":63},"Performance was assessed in the validation cohort using time-dependent ROC, timeC-index, calibration curves, Kaplan-Meier curves, NRI, and IDI.","https://schema.org",{"og:url":32,"og:type":74,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":76,"canonical":32},"index,follow",{"doc_id":78,"site_id":7},457189,1793084969,{"code":4,"msg":81,"data":82},"success",[83,87,91,95,100,105,109,114,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":84,"show_sort_weight":85,"slug":86},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":88,"show_sort_weight":89,"slug":90},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":92,"show_sort_weight":93,"slug":94},"Exam",70,"exam",{"id":96,"doc_module":4,"doc_module_name":25,"category_name":97,"show_sort_weight":98,"slug":99},5,"Comic",60,"comic",{"id":101,"doc_module":4,"doc_module_name":25,"category_name":102,"show_sort_weight":103,"slug":104},6,"Technology",50,"technology",{"id":106,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":107,"slug":108},7,40,"healthcare",{"id":110,"doc_module":4,"doc_module_name":25,"category_name":111,"show_sort_weight":112,"slug":113},8,"Research & Report",30,"research-report",{"id":115,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":96,"slug":129},19,"General","general",{"code":4,"msg":81,"data":131},{"doc_id":78,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":106,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":30,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":139,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":144,"read_time":145,"preview_page_count":4},549768702563,"https://ap-avatar.wpscdn.com/avatar/8000c4aa63b76e948b?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786536092046926083","Lumbar centroid level subcutaneous fat volume increased performance of prognostic predictive model in digestive system cancers: a real-world cohort study  \nLin Zheng1\\#, Jun-Li Zhang2\\#, Li-Li Wu2\\#, Rong-Jun Shao1, Xiao-Xuan Ye3, Wei-Cheng Li4, Liang Zhang5, Yu-Yue Zhang6 , Feng-Ming Zhang 1 , Yu-Hang Ye 1 , Xiao-Wei Le 1 , Teng Zhang7 , En-Yu Wang8 , Rui-Zhi Ye1, Guang-Xian You1, Rong-Biao Ying9, Ru-Xuan Yan8, Zhi-Rui Zhou10  \n1Department of Radiation Oncology, Taizhou Cancer Hospital, Wenling, Taizhou, China; 2Department of Nursing, Taizhou Cancer Hospital, Wenling, Taizhou, China; 3Department of Medical Oncology, Taizhou Cancer Hospital, Wenling, Taizhou, China; 4Department of Cardiothoracic Surgery, School of Medicine, Shaoxing University, Shaoxing, China; 5Department of Cardiothoracic Surgery, Taizhou Central Hospital (Taizhou University Hospital), Taizhou, China; 6Department of Radiation Oncology, Taizhou Central Hospital, Taizhou, China; 7Department of Gastroenterology, Taizhou Cancer Hospital, Taizhou, China; 8Department of Radiology, Taizhou Cancer Hospital, Taizhou, China; 9Department of Surgical Oncology, Taizhou Cancer Hospital, Taizhou, China; 10Radiation Oncology Center, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China  \nContributions: (I) Conception and design: L Zheng, JL Zhang, RX Yan, ZR Zhou; (II) Administrative support: EY Wang, RZ Ye, GX You, RB Ying;  \n(III) Provision of study materials or patients: RJ Shao, XX Ye, WC Li, L Zhang, YY Zhang, FM Zhang, YH Ye, XW Le, T Zhang; (IV) Collection and assembly of data: All authors; (V) Data analysis and interpretation: All authors; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.  \n\\#These authors contributed equally to this work.  \nCorrespondence to: Ru-Xuan Yan, MBBS. Department of Radiology, Taizhou Cancer Hospital, No. 50 Zhenxin Road, Wenling, Taizhou 317502, China. Email: [ruxuango@gmail.com](ruxuango@gmail.com); Zhi-Rui Zhou, MD. Radiation Oncology Center, Huashan Hospital, Shanghai Medical College, Fudan  \nUniversity, No. 12 Wulumuqi Middle Road, Shanghai 200040, China. Email: [zzr3711@163.com](zzr3711@163.com).  \nBackground: Nutritional indicators play an important role in predicting the prognosis of digestive system cancers. Measures of adipose tissue distribution derived from computed tomography (CT), such as subcutaneous fat volume, are promising for assessing systemic inflammation and nutritional status.  \nHowever, their integration into standardized prognostic models is still limited. This study aimed to increase the performance of the Cox regression model (Coxm) by adding the third lumbar vertebra centroid level  \nsubcutaneous fat volume (L3 CLSFV) and to assess its influence on prognostic prediction model.  \nMethods: We constructed two Cox regression models, Coxm1 and Coxm2, using clinical features and nutritional indicators from the training cohort of patients with digestive system cancers. The Coxm1 model contained seven features, while Coxm2 incorporated an extra L3 CLSFV measured by CT. Performance was evaluated using multiple metrics in the validation cohort, including time-dependent receiver operating characteristic (timeROC), time-dependent concordance index (timeC-index), calibration curve, and Kaplan-Meier curve. The predictive accuracy of the model was further assessed using net reclassification  \nimprovement (NRI) and integrated discrimination improvement (IDI) .  \nResults: Both models had high area under the curve (AUC) (range, 0.78–0.89) and C-index values (range, 0.74–0.80) . The timeROC curve showed that the inclusion of L3 CLSFV in Coxm2 did not improve the model’s AUC, with similar values observed at 1-, 3-, and 5-year. Coxm2 did not improve time-dependent C-index in comparison with Coxm1 . Calibration curves showed good agreement between predicted and actual survival probability in both models, with slight improvements seen in Coxm2 versus Coxm1 (Brierscore, 0.166","cbCaigiKTLV4hu65","https://ap.wps.com/l/cbCaigiKTLV4hu65","pdf",2363372,17,"English","# Introduction\n# Methods\n# Results\n# Conclusions","[{\"question\":\"What is the main purpose of this study?\",\"answer\":\"To determine whether adding L3 CLSFV to a Cox regression prognostic model improves predictive performance for digestive system cancers.\"},{\"question\":\"How were the two prognostic models constructed?\",\"answer\":\"Two Cox models (Coxm1 and Coxm2) were built using training cohort features; Coxm2 added the CT-measured L3 CLSFV.\"},{\"question\":\"Which evaluation metrics were used to assess model performance?\",\"answer\":\"Performance was assessed in the validation cohort using time-dependent ROC, timeC-index, calibration curves, Kaplan-Meier curves, NRI, and IDI.\"}]","Lumbar Centroid Level Subcutaneous Fat Volume Increased Performance of Prognostic Predictive Model in Digestive System Cancers - A Real-World Cohort Study | PDF",1790748563,43]