[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128045-en":3,"doc-seo-128045-105":31,"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128045,5909887256941,"Levi","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Machine learning based predictive model and genetic mutation landscape for high-grade colorectal neuroendocrine carcinoma - SEER database analysis with external validation","High-grade colorectal neuroendocrine carcinoma (HCNEC) is rare yet highly aggressive, and clinical decision-making is limited by diagnostic challenges and unclear prognostic stratification. A retrospective SEER-based analysis (2000–2019) was combined with an external validation cohort from two tertiary hospitals in Southwest China. LASSO, Random Forest, and XGBoost identified key predictors of overall and cancer-specific survival, while COSMIC-derived mutation data characterized common genomic alterations. Among 714 patients, the model demonstrated reliable prognostic performance and highlighted TP53, KRAS, and APC mutations as frequent changes.","TYPE Original Research PUBLISHED 29 January 2025 DOI 10.3389/fonc.2025.1509170  \nOPEN ACCESS  \nEDITED BY  \nSharon R. Pine,  \nUniversity of Colorado Anschutz Medical Campus, United States  \nREVIEWED BY  \nRahul Gupta,  \nSynergy Institute of Medical Sciences, India Rui Wang,  \nThe First Affiliated Hospital of Xi’an Jiaotong University, China  \n*CORRESPONDENCE  \nSong Mu  \n [musong1129@126.com](musong1129@126.com)[ ](musong1129@126.com)Aishun Jin  \n [aishunjin@cqmu.edu.cn](aishunjin@cqmu.edu.cn)  \n†These authors have contributed equally to this work and share first authorship  \nRECEIVED 10 October 2024  \nACCEPTED 13 January 2025  \nPUBLISHED 29 January 2025  \nCITATION  \nWu R, Chen S, He Y, Li Y, Mu S and Jin A (2025) Machine learning based predictive model and genetic mutation landscape for high-grade colorectal neuroendocrine carcinoma: a SEER database analysis with external validation.  \nFront. Oncol. 15:1509170 .  \ndoi: 10.3389/fonc.2025.1509170  \nCOPYRIGHT  \n© 2025 Wu, Chen, He, Li, Mu and Jin. This isan open-access article distributed under the terms of the Creative 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.  \nMachine learning based predictive model and genetic mutation landscape for highgrade colorectal neuroendocrine carcinoma: a SEER database analysis with external validation  \nRuixin Wu 1,2†, Sihao Chen 1,2†, Yi He 1,2, Ya Li 3, Song Mu 4* and Aishun Jin 1,2*  \n1 Department of Immunology, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, China, 2Chongqing Key Laboratory of Tumor Immune Regulation and Immune Intervention, Chongqing, China, 3 Department of Gastrointestinal Surgery, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China, 4 Department of Colorectal Surgery, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, China  \nBackground: High-grade colorectal neuroendocrine carcinoma (HCNEC) is a rare but aggressive subset of neuroendocrine tumors. This study was designed to construct a risk model based on comprehensive clinical and mutational genomics data to facilitate clinical decision making.  \nMethods: A retrospective analysis was conducted using data from the Surveillance, Epidemiology, and End Results (SEER) database, spanning 2000 to 2019 . The external validation cohort was sourced from two tertiary hospitals in Southwest China. Independent factors influencing both overall survival (OS) and cancer-specific survival (CSS) were identified using LASSO, Random Forest, and XGBoost regression techniques. Molecular data with the most common mutations in CNEC were extracted from the Catalogue of Somatic Mutations in Cancer (COSMIC) database.  \nResults: In this prognostic analysis, the data from 714 participants with HCNEC were evaluated. The median OS for the cohort was 10 months, whereas CSS was 11 months. Six variables (M stage, LODDS, Nodes positive, Surgery, Radiotherapy, and Chemotherapy) were screened as key prognostic indicators. The machine learning model showed reliable performance across multiple evaluation dimensions. The most common mutations of CNEC identified in the COSMIC database were TP53, KRAS, and APC.  \nConclusions: In this study, a refined machine learning predictive model was developed to assess the prognosis of HCNEC accurately and we briefly analyzed its genomic features, which might offer a valuable tool to address existing clinical challenges.  \nKEYWORDS  \nhigh-grade colorectal neuroendocrine carcinoma (HCNEC), machine learning, prognosis, SEER, COSMIC, genetic mutation landscape  \nFrontiers in Oncology 01 [frontiersin.org](frontiersin.org)  \nIntroduction  \nNeuroendocrine tumors, which are rare malignancies, ","cbCaibhDzWW0kMhF","https://ap.wps.com/l/cbCaibhDzWW0kMhF","pdf",4643813,2,1,14,"English","en",105,"# Introduction\n## Background and clinical challenges\n## Classification and treatment landscape\n# Methods\n## SEER retrospective analysis and external validation\n## Feature selection and survival modeling\n## Genomic mutation extraction from COSMIC\n# Results\n## Patient cohort and survival outcomes\n## Key prognostic indicators\n## Model performance and mutation landscape\n# Conclusions","[{\"question\":\"What is the main goal of the study on HCNEC?\",\"answer\":\"To construct and validate a machine-learning risk model using clinical and mutational genomics data to support prognosis-based clinical decision-making for high-grade colorectal neuroendocrine carcinoma.\"},{\"question\":\"Which databases were used to derive clinical cases and genomic mutations?\",\"answer\":\"Clinical and survival data were analyzed from the SEER database (2000–2019), while common somatic mutations were extracted from the COSMIC database.\"},{\"question\":\"Which factors and mutations were identified as important for prognosis and genomic features?\",\"answer\":\"Six prognostic variables were screened as key indicators: M stage, LODDS, nodes positive, surgery, radiotherapy, and chemotherapy; common mutations included TP53, KRAS, and APC.\"}]","Machine learning based predictive model and genetic mutation landscape for high-grade colorectal neuroendocrine carcinoma - SEER database analysis with external validation | PDF",1785944424,35,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"machine-learning-based-predictive-model-and-genetic-mutation-landscape-for-high-grade-colorectal-neuroendocrine-carcinoma-seer-database-analysis-with-external-validation","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/machine-learning-based-predictive-model-and-genetic-mutation-landscape-for-high-grade-colorectal-neuroendocrine-carcinoma-seer-database-analysis-with-external-validation/128045/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the main goal of the study on HCNEC?","Question",{"text":76,"@type":77},"To construct and validate a machine-learning risk model using clinical and mutational genomics data to support prognosis-based clinical decision-making for high-grade colorectal neuroendocrine carcinoma.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which databases were used to derive clinical cases and genomic mutations?",{"text":81,"@type":77},"Clinical and survival data were analyzed from the SEER database (2000–2019), while common somatic mutations were extracted from the COSMIC database.",{"name":83,"@type":74,"acceptedAnswer":84},"Which factors and mutations were identified as important for prognosis and genomic features?",{"text":85,"@type":77},"Six prognostic variables were screened as key indicators: M stage, LODDS, nodes positive, surgery, radiotherapy, and chemotherapy; 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