[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122350-en":3,"doc-seo-122350-105":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},122350,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Diagnosing prostate cancer in the PSA gray zone through machine learning and transrectal ultrasound video","A machine learning-based predictive framework targets prostate cancer detection in the PSA gray zone by using transrectal ultrasound video clips. Patients with suspected prostate cancer and PSA levels between 4 and 10 ng/mL were included, generating 851 ultrasound-derived features, reduced to 25 via LASSO regression. Four radiomics models were built with SVM, random forest, adaptive boosting, and gradient boosting and evaluated using ROC, AUC, sensitivity, specificity, accuracy, PPV, and F1 score. The random forest model achieved the strongest validation performance, demonstrating superior diagnostic utility for clinical decision support.","O R I G I NA L  \nR E S EA R C H  \nDiagnosing prostate cancer in the PSA gray zone through machine learning and transrectal ultrasound video  \nQin Wu 1 ,†, Chengyi Wu2 ,†, Maoliang Zhang 1 ,†, Jie Yang 1 , Junxiang Zhang 1 , Yun Jin 1 , Yanhong Du 1 , Xingbo Sun 1 , Liyuan Jin 1 , Kai Wang 1 , Zhengbiao Hu 1 , Xiaoyang Qi 1 , Jincao Yao3 ,4 ,5 , *, Zhengping Wang 1 , *, Dong Xu3 ,4 ,5 , *  \n1 Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, 322100 Dongyang, Zhejiang, China  \n2 Medical College of Zhejiang University, 310033 Hangzhou, Zhejiang, China  \n3 Department of Ultrasound, Zhejiang Cancer Hospital, 310022 Hangzhou, Zhejiang, China  \n4 Interventional Medicine and Engineering Research Center, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, 310006 Hangzhou, Zhejiang, China  \n5 Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, 310022 Hangzhou, Zhejiang, China  \n*Correspondence  \n[yaojc@zjcc.org.cn](yaojc@zjcc.org.cn)  \n(Jincao Yao);  \n[wangzp0203@163.com](wangzp0203@163.com)  \n(Zhengping Wang);  \n[xudong@zjcc.org.cn](xudong@zjcc.org.cn)  \n(Dong Xu)  \n† These authors contributed equally.  \nAbstract  \nBackground: We developed a machine learning-based predictive model for diagnosing prostate cancer within the gray zone of prostate-specific antigen (PSA) levels, leveraging transrectal prostate ultrasound video clips. Methods: Data were collected for patients with suspected prostate cancer, characterized by intermediate PSA levels between 4 and 10 ng/mL, who visited the Department of Urology, Dongyang People’s Hospital, which is affiliated with Wenzhou Medical University, from 20 August 2021 to 30 September 2023 . Among the final selection of 508 patients, a total of 851 features were extracted from the ultrasound video clips, reduced the dimensionality using least absolute shrinkage and selection operator regression, and finally selected 25 features. The selected features were employed to construct radiomics models based on four machine learning algorithms support vector machine (SVM), random forest (RF), adaptive boosting (ADB) and gradient boosting machine (GBM) . The performance of the model was comprehensively assessed using receiver operating characteristic (ROC) curve analysis, with diagnostic effectiveness measured through metrics such as the area under the curve (AUC), sensitivity, specificity and overall accuracy. Results: The RF model demonstrated an AUC of 0.89, accuracy of 0.81, sensitivity of 0.81, specificity of 0.79, positive predictive value of 0.91 and F1 score of 0.77 . As compared to the RF model, the SVM, ADB and GBM models showed similar values for AUC (range 0.80–0.86), accuracy (range 0.75–0.79), sensitivity (range 0.80–0.81), specificity (range 0.65–0.75), positive predictive value (range 0.83–0.89) and F1 score (range 0.72–0.76) . In the validation set, following comprehensive evaluation, the RF model exhibited the best performance among the four models. Conclusions: The four machine learning models each had diagnostic value for detecting prostate cancer in patients within the PSA “gray zone”, with the RF model demonstrating the highest predictive performance.  \nKeywords  \nProstate cancer; PSA; Gray zone; Machine Learning; Ultrasound  \n1. Introduction  \nProstate cancer (PCa) remains one of the most common malignancies affecting men worldwide, characterized by a high incidence rate and varying degrees of aggressiveness [1] . Clinically significant prostate tumors pose a significant health risk due to their potential for progression and metastasis [2] . The diagnostic approaches for PCa have traditionally relied on a combination of digital rectal examinations (DRE), prostatespecific antigen (PSA) testing [3], and advanced imaging techniques such as transrectal ultrasound (TRUS) and magnetic resonance imaging (MRI) [4] .  \nPS","cbCailquKTqxZLsx","https://ap.wps.com/l/cbCailquKTqxZLsx","pdf",6498178,1,10,"English","en",105,"# Abstract\n# Keywords\n# Introduction","[{\"question\":\"What PSA range defines the PSA gray zone in this study?\",\"answer\":\"The PSA gray zone is defined as PSA levels between 4 and 10 ng/mL for patients with suspected prostate cancer.\"},{\"question\":\"How were ultrasound features created and selected for the models?\",\"answer\":\"Transrectal ultrasound video clips were used to extract 851 features, which were reduced using least absolute shrinkage and selection operator (LASSO) regression, resulting in 25 selected features.\"},{\"question\":\"Which machine learning model performed best for detecting prostate cancer?\",\"answer\":\"In validation, the random forest model showed the highest diagnostic performance among the four models.\"}]","Diagnosing prostate cancer in the PSA gray zone through machine learning and transrectal ultrasound video | 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PSA range defines the PSA gray zone in this study?","Question",{"text":75,"@type":76},"The PSA gray zone is defined as PSA levels between 4 and 10 ng/mL for patients with suspected prostate cancer.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were ultrasound features created and selected for the models?",{"text":80,"@type":76},"Transrectal ultrasound video clips were used to extract 851 features, which were reduced using least absolute shrinkage and selection operator (LASSO) regression, resulting in 25 selected features.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning model performed best for detecting prostate cancer?",{"text":84,"@type":76},"In validation, the random forest model showed the highest diagnostic performance among the four 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