Yusheng Zhao, Lei Zhang, Subo Zhang, Jiajing Li, Kaimin Shi, Di Yao, Qiuzi Li, Tao Zhang, Lei Xu, Lei Geng, Yi Sun, Jinxin Wan
{"title":"基于机器学习的磁共振成像前列腺癌诊断：系统回顾和荟萃分析。","authors":"Yusheng Zhao, Lei Zhang, Subo Zhang, Jiajing Li, Kaimin Shi, Di Yao, Qiuzi Li, Tao Zhang, Lei Xu, Lei Geng, Yi Sun, Jinxin Wan","doi":"10.1038/s41391-025-00997-2","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>This study aims to evaluate the diagnostic value of machine learning-based MRI imaging in differentiating benign and malignant prostate cancer and detecting clinically significant prostate cancer (csPCa, defined as Gleason score ≥7) using systematic review and meta-analysis methods.</p><p><strong>Methods: </strong>Electronic databases (PubMed, Web of Science, Cochrane Library, and Embase) were systematically searched for predictive studies using machine learning-based MRI imaging for prostate cancer diagnosis. Sensitivity, specificity, and area under the curve (AUC) were used to assess the diagnostic accuracy of machine learning-based MRI imaging for both benign/malignant prostate cancer and csPCa.</p><p><strong>Results: </strong>A total of 12 studies met the inclusion criteria, with 3474 patients included in the meta-analysis. Machine learning-based MRI imaging demonstrated good diagnostic value for both benign/malignant prostate cancer and csPCa. The pooled sensitivity and specificity for diagnosing benign/malignant prostate cancer were 0.92 (95% CI: 0.83-0.97) and 0.90 (95% CI: 0.68-0.97), respectively, with a combined AUC of 0.96 (95% CI: 0.94-0.98). For csPCa diagnosis, the pooled sensitivity and specificity were 0.83 (95% CI: 0.77-0.87) and 0.73 (95% CI: 0.65-0.81), respectively, with a combined AUC of 0.86 (95% CI: 0.83-0.89).</p><p><strong>Conclusion: </strong>Machine learning-based MRI imaging shows good diagnostic accuracy for both benign/malignant prostate cancer and csPCa. Further in-depth studies are needed to validate these findings.</p>","PeriodicalId":20727,"journal":{"name":"Prostate Cancer and Prostatic Diseases","volume":" ","pages":""},"PeriodicalIF":5.8000,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine learning-based MRI imaging for prostate cancer diagnosis: systematic review and meta-analysis.\",\"authors\":\"Yusheng Zhao, Lei Zhang, Subo Zhang, Jiajing Li, Kaimin Shi, Di Yao, Qiuzi Li, Tao Zhang, Lei Xu, Lei Geng, Yi Sun, Jinxin Wan\",\"doi\":\"10.1038/s41391-025-00997-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>This study aims to evaluate the diagnostic value of machine learning-based MRI imaging in differentiating benign and malignant prostate cancer and detecting clinically significant prostate cancer (csPCa, defined as Gleason score ≥7) using systematic review and meta-analysis methods.</p><p><strong>Methods: </strong>Electronic databases (PubMed, Web of Science, Cochrane Library, and Embase) were systematically searched for predictive studies using machine learning-based MRI imaging for prostate cancer diagnosis. Sensitivity, specificity, and area under the curve (AUC) were used to assess the diagnostic accuracy of machine learning-based MRI imaging for both benign/malignant prostate cancer and csPCa.</p><p><strong>Results: </strong>A total of 12 studies met the inclusion criteria, with 3474 patients included in the meta-analysis. Machine learning-based MRI imaging demonstrated good diagnostic value for both benign/malignant prostate cancer and csPCa. The pooled sensitivity and specificity for diagnosing benign/malignant prostate cancer were 0.92 (95% CI: 0.83-0.97) and 0.90 (95% CI: 0.68-0.97), respectively, with a combined AUC of 0.96 (95% CI: 0.94-0.98). For csPCa diagnosis, the pooled sensitivity and specificity were 0.83 (95% CI: 0.77-0.87) and 0.73 (95% CI: 0.65-0.81), respectively, with a combined AUC of 0.86 (95% CI: 0.83-0.89).</p><p><strong>Conclusion: </strong>Machine learning-based MRI imaging shows good diagnostic accuracy for both benign/malignant prostate cancer and csPCa. Further in-depth studies are needed to validate these findings.</p>\",\"PeriodicalId\":20727,\"journal\":{\"name\":\"Prostate Cancer and Prostatic Diseases\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":5.8000,\"publicationDate\":\"2025-07-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Prostate Cancer and Prostatic Diseases\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1038/s41391-025-00997-2\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Prostate Cancer and Prostatic Diseases","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1038/s41391-025-00997-2","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ONCOLOGY","Score":null,"Total":0}
Machine learning-based MRI imaging for prostate cancer diagnosis: systematic review and meta-analysis.
Objective: This study aims to evaluate the diagnostic value of machine learning-based MRI imaging in differentiating benign and malignant prostate cancer and detecting clinically significant prostate cancer (csPCa, defined as Gleason score ≥7) using systematic review and meta-analysis methods.
Methods: Electronic databases (PubMed, Web of Science, Cochrane Library, and Embase) were systematically searched for predictive studies using machine learning-based MRI imaging for prostate cancer diagnosis. Sensitivity, specificity, and area under the curve (AUC) were used to assess the diagnostic accuracy of machine learning-based MRI imaging for both benign/malignant prostate cancer and csPCa.
Results: A total of 12 studies met the inclusion criteria, with 3474 patients included in the meta-analysis. Machine learning-based MRI imaging demonstrated good diagnostic value for both benign/malignant prostate cancer and csPCa. The pooled sensitivity and specificity for diagnosing benign/malignant prostate cancer were 0.92 (95% CI: 0.83-0.97) and 0.90 (95% CI: 0.68-0.97), respectively, with a combined AUC of 0.96 (95% CI: 0.94-0.98). For csPCa diagnosis, the pooled sensitivity and specificity were 0.83 (95% CI: 0.77-0.87) and 0.73 (95% CI: 0.65-0.81), respectively, with a combined AUC of 0.86 (95% CI: 0.83-0.89).
Conclusion: Machine learning-based MRI imaging shows good diagnostic accuracy for both benign/malignant prostate cancer and csPCa. Further in-depth studies are needed to validate these findings.
期刊介绍：
Prostate Cancer and Prostatic Diseases covers all aspects of prostatic diseases, in particular prostate cancer, the subject of intensive basic and clinical research world-wide. The journal also reports on exciting new developments being made in diagnosis, surgery, radiotherapy, drug discovery and medical management.
Prostate Cancer and Prostatic Diseases is of interest to surgeons, oncologists and clinicians treating patients and to those involved in research into diseases of the prostate. The journal covers the three main areas - prostate cancer, male LUTS and prostatitis.
Prostate Cancer and Prostatic Diseases publishes original research articles, reviews, topical comment and critical appraisals of scientific meetings and the latest books. The journal also contains a calendar of forthcoming scientific meetings. The Editors and a distinguished Editorial Board ensure that submitted articles receive fast and efficient attention and are refereed to the highest possible scientific standard. A fast track system is available for topical articles of particular significance.
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