[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126188-en":3,"doc-seo-126188-105":31,"detail-sidebar-cat-0-en-105":93},{"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},126188,549768072016,"River Wang","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques","Extremely aggressive prostate cancer, including small cell and neuroendocrine subtypes, is linked to poor outcomes and constrained therapies. This retrospective study builds an interpretable machine learning model to predict 1-, 3-, and 5-year survival for 1,620 patients from the SEER database (2000–2020). Boruta-based feature selection and nine survival algorithms were assessed with AUC, F1, confusion matrix, and decision curve analysis, while SHAP clarified clinically meaningful prognostic drivers and their implications.","OPEN ACCESS  \nEDITED BY  \nThomas F. Heston,  \nUniversity of Washington, United States  \nREVIEWED BY  \nWojciech Lesiński,  \nUniversity of Białystok, Poland Zuheng Wang,  \nGuangxi Medical University, China Bhumandeep Kour,  \nLovely Professional University, India  \n*CORRESPONDENCE  \nDuxian Liu  \n [ldx849756917@qq.com](ldx849756917@qq.com)  \nRECEIVED 17 October 2024  \nACCEPTED 21 April 2025  \nPUBLISHED 12 May 2025  \nCITATION  \nPeng C, Gong C, Zhang X and Liu D (2025) A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques.  \nFront. Med. 12:1512870 .  \ndoi: 10.3389/fmed.2025.1512870  \nCOPYRIGHT  \n© 2025 Peng, Gong, Zhang and Liu. This is an 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.  \nTYPE Original Research PUBLISHED 12 May 2025  \nDOI 10.3389/fmed.2025.1512870  \nA prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques  \nCong Peng, Cheng Gong, Xiaoya Zhang and Duxian Liu * Department of Pathology, The Second Hospital of Nanjing, Affiliated to Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China  \nBackground: Extremely aggressive prostate cancer, including subtypes like small cell carcinoma and neuroendocrine carcinoma, is associated with poor prognosis and limited treatment options. This study sought to create a robust, interpretable machine learning-based model that predicts 1-, 3-, and 5-year survival in patients with extremely aggressive prostate cancer. Additionally, we sought to pinpoint key prognostic factors and their clinical implications through an innovative method.  \nMaterials and methods: This study retrospectively analyzed data from 1,620 patients with extremely aggressive prostate cancer in the SEER database (2000– 2020) . Feature selection was performed using the Boruta algorithm, and survival predictions were made using nine machine learning algorithms, including XGBoost, logistic regression (LR), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), decision tree (DT), elastic network (Enet), multilayer perceptron (MLP) and lightGBM. Model performance was evaluated using metrics such as AUC, accuracy (F1 score), confusion matrix, and decision curve analysis. Additionally, Shapley Additive Explanations (SHAP) were applied to interpret feature importance within the model, revealing the clinical factors that influence survival predictions.  \nResults: Among the nine models, the lightGBM model exhibited the best performance, with an AUC and F1 score of (0 . 8, 0. 809) for 1-year survival prediction, (0 . 809, 0.751) for 3-year survival prediction, and (0 .773, 0.611) for 5-year survival prediction. SHAP analysis revealed that M stage was the most important feature for predicting 1-and 3-year survival, while PSA level had the greatest impact on 5-year survival predictions. The model demonstrated good clinical utility and predictive accuracy through decision curve analysis and confusion matrix.  \nConclusion: The lightGBM model has good predictive power for survival inpatients with extremely aggressive prostate cancer. By identifying key clinical factors and providing actionable predictions, the model has the potential to enhance prognostic accuracy and improve patient outcomes.  \nKEYWORDS  \nprostate cancer, survival, machine, predictive analytics, Boruta algorithm  \nFrontiers in Medicine 01 [frontiersin.org](frontiersin.org)  \nIntroduction  \nAccording to the Cancer Statistics 2024 published by the American Cancer Society, the United States is expected to diagnose approximately 299,010 new cases of prostate ","cbCaibliGOiaNhBz","https://ap.wps.com/l/cbCaibliGOiaNhBz","pdf",5097797,9,1,11,"English","en",105,"# Background\n## Materials and methods\n## Results\n## Conclusion","[{\"question\":\"What was the goal of the study?\",\"answer\":\"To develop a robust, interpretable machine learning model that predicts 1-, 3-, and 5-year survival for patients with extremely aggressive prostate cancer and identifies key prognostic factors.\"},{\"question\":\"Which data and feature/model methods were used?\",\"answer\":\"The study retrospectively analyzed 1,620 patients from the SEER database (2000–2020). Features were selected using the Boruta algorithm, and survival predictions were made using nine machine learning algorithms.\"},{\"question\":\"Which model performed best and what did SHAP reveal?\",\"answer\":\"The lightGBM model showed the best performance across the evaluated survival horizons. SHAP indicated that M stage was most important for 1- and 3-year survival, while PSA level most strongly influenced 5-year survival predictions.\"}]","A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques | PDF",1785903704,28,{"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":88,"head_meta":90,"extra_data":92,"updated_unix":29},"a-prognostic-model-for-highly-aggressive-prostate-cancer-using-interpretable-machine-learning-techniques","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/a-prognostic-model-for-highly-aggressive-prostate-cancer-using-interpretable-machine-learning-techniques/126188/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What was the goal of the study?","Question",{"text":77,"@type":78},"To develop a robust, interpretable machine learning model that predicts 1-, 3-, and 5-year survival for patients with extremely aggressive prostate cancer and identifies key prognostic factors.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Which data and feature/model methods were used?",{"text":82,"@type":78},"The study retrospectively analyzed 1,620 patients from the SEER database (2000–2020). Features were selected using the Boruta algorithm, and survival predictions were made using nine machine learning algorithms.",{"name":84,"@type":75,"acceptedAnswer":85},"Which model performed best and what did SHAP reveal?",{"text":86,"@type":78},"The lightGBM model showed the best performance across the evaluated survival horizons. 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