[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121468-en":3,"doc-seo-121468-105":30,"detail-sidebar-cat-0-en-105":91},{"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},121468,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Evaluation of a PSA and transrectal prostate ultrasound video-based machine learning model as a tool for prostate cancer diagnosis","Developing an accurate, non-invasive approach for prostate cancer diagnosis motivates machine learning models that integrate serum prostatespecific antigen (PSA) with prostate ultrasound video information. A cohort of 928 participants was used to train and assess models, combining ultrasound-derived video clip features with PSA and clinical indicators. Six algorithms were compared using ROC-based performance, while SHAP was applied to interpret feature contributions. Results show strong test-set discriminative ability and highlight XGBoost with the most promising accuracy.","TYPE Original Research PUBLISHED 08 September 2025 DOI 10.3389/fonc.2025.1590396  \nOPEN ACCESS  \nEDITED BY  \nTaja Lozar,  \nInstitute of Oncology Ljubljana, Slovenia  \nREVIEWED BY  \nJincao Yao,  \nUniversity of Chinese Academy of Sciences, China  \nXinrui Huang,  \nPeking University, China  \n*CORRESPONDENCE  \nYanhong Du  \n [duyh520@outlook.com](duyh520@outlook.com)[ ](duyh520@outlook.com)Liyan Hu  \n [hlyhzh@outlook.com](hlyhzh@outlook.com)[ ](hlyhzh@outlook.com)Xiaoyang Qi  \n [qixiaoyang16@163.com](qixiaoyang16@163.com)  \nRECEIVED 09 March 2025  \nACCEPTED 19 August 2025  \nPUBLISHED 08 September 2025  \nCITATION  \nDu Y, Zhao A, Zhang M, Wang Z, Hu L and  \nQi X (2025) Evaluation of a PSA and transrectal prostate ultrasound video-based machine learning model as a tool for prostate cancer diagnosis.  \nFront. Oncol. 15:1590396 .  \ndoi: 10.3389/fonc.2025.1590396  \nCOPYRIGHT  \n© 2025 Du, Zhao, Zhang, Wang, Hu and Qi. 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.  \nEvaluation of a PSA and transrectal prostate ultrasound video-based machine learning model as a tool for prostate cancer diagnosis  \nYanhong Du*, Anli Zhao, Maoliang Zhang, Zhengping Wang, Liyan Hu* and Xiaoyang Qi*  \nDepartment of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China  \nObjective: To develop a machine learning-based model incorporating prostatespecific antigen (PSA) levels and prostate ultrasound video clips for diagnosing prostate cancer.  \nMethods: The study enrolled 928 participants, of whom 429 had prostate cancer and 499 other non-prostate cancers. Univariate and multivariate analyses of serological indices were conducted to detect significant variables. From this cohort, 742 patients were randomly chosen for model validation, while the other 186 were employed to evaluate the accuracy and reliability of the model. Seven features were extracted from ultrasound video clips and combined with PSA and other clinical indicators. Predictive models were established using six machine learning algorithms and receiver operating characteristic (ROC) curves were used to determine the optimal model. SHapley Additive exPlanations (SHAP) was utilized to visualize feature importance in the best-performing model.  \nResults: All six of the evaluated machine learning models performed favorably, with area under the ROC curve (AUC) values in the test set ranging from 0.800 to 0.881. Of these models, the XGBoost model achieved the most promising performance, significantly surpassing that of the other models (P \u003C 0 . 05) . SHAP visualization revealed that PSA, prostatic volume(PV), age, wavelet. LHL.firstorder. Median, wavelet. HLH.glszm.ZoneEntropy, and original.shape.MinorAxisLength were the most influential features in the XGBoost model.  \nConclusion: The developed machine learning models demonstrated significant potential for prostate cancer diagnosis. Among them, the XGBoost model outperformed the others, highlighting its superior predictive capability.  \nKEYWORDS  \nprostate cancer, PSA, machine learning, prostate ultrasound video, SHAP  \nFrontiers in Oncology 01 [frontiersin.org](frontiersin.org)  \n1 Introduction  \nProstate cancer(PCa) ranks second in global male malignancies, after only lung cancer (1) . In China, both diagnoses and deaths associated with prostate cancer have been increasing steadily in recent years (2) . Per the World Health Organization, in 2020, China reported an incidence rate of 15.6 per 100,000 individuals, with more than 110,000 new diagnoses and more than 50,000 deaths, making it a significant public health concer","cbCaif3eIVbKxyGc","https://ap.wps.com/l/cbCaif3eIVbKxyGc","pdf",3700448,1,15,"English","en",105,"# Introduction\n## Clinical background and screening limitations\n## Rationale for radiomics and machine learning\n# Methods\n## Participants and validation strategy\n## Feature extraction and model development\n## ROC evaluation and SHAP interpretation\n# Results\n## Comparative model performance (AUC)\n## Feature importance in the best model\n# Conclusion","[{\"question\":\"What is the main goal of the proposed model?\",\"answer\":\"To build a machine learning model that uses PSA levels together with prostate ultrasound video clips to support prostate cancer diagnosis.\"},{\"question\":\"How were the models trained and validated?\",\"answer\":\"The study enrolled 928 participants and randomly selected 742 for model validation, while the remaining 186 were used to evaluate accuracy and reliability.\"},{\"question\":\"Which algorithm performed best and how was interpretability assessed?\",\"answer\":\"The XGBoost model achieved the most promising performance. SHAP visualization was used to identify the most influential features driving predictions.\"}]","Evaluation of a PSA and transrectal prostate ultrasound video-based machine learning model as a tool for prostate cancer diagnosis | PDF",1785735796,38,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"evaluation-of-a-psa-and-transrectal-prostate-ultrasound-video-based-machine-learning-model-as-a-tool-for-prostate-cancer-diagnosis","",{"@graph":36,"@context":85},[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/evaluation-of-a-psa-and-transrectal-prostate-ultrasound-video-based-machine-learning-model-as-a-tool-for-prostate-cancer-diagnosis/121468/",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","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the proposed model?","Question",{"text":75,"@type":76},"To build a machine learning model that uses PSA levels together with prostate ultrasound video clips to support prostate cancer diagnosis.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were the models trained and validated?",{"text":80,"@type":76},"The study enrolled 928 participants and randomly selected 742 for model validation, while the remaining 186 were used to evaluate accuracy and reliability.",{"name":82,"@type":73,"acceptedAnswer":83},"Which algorithm performed best and how was interpretability assessed?",{"text":84,"@type":76},"The XGBoost model achieved the most promising performance. SHAP visualization was used to identify the most influential features driving predictions.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]