Machine learning-based MRI imaging for prostate cancer diagnosis: systematic review and meta-analysis.

Journal: Prostate cancer and prostatic diseases
Published Date: Jul 28, 2025

Abstract

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.

Authors

  • Yusheng Zhao
    Leibniz Institute for Plant Genetics and Crop Plant Research, Seeland, Germany.
  • Lei Zhang
    Division of Gastroenterology, Union Hospital, Tongji Medical College Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Subo Zhang
    Department of Medical Imaging, The Second People's Hospital of Lianyungang, Lianyungang, China.
  • Jiajing Li
    Department of Medical Imaging, The Second People's Hospital of Lianyungang, Lianyungang city, China.
  • Kaimin Shi
    Department of Technology, , Jiangsu Jerry Technology Co. Ltd., Lianyungang city, China.
  • Di Yao
    School of Economics, Shandong University, Jinan, 250100, China. Electronic address: [email protected].
  • Qiuzi Li
    Department of Integrated Management, Lianyungang Haizhou Bay Marine Fisheries Development Institute, Lianyungang city, China.
  • Tao Zhang
    Department of Traumatology, Chongqing Emergency Medical Center, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing, 40044, People's Republic of China.
  • Lei Xu
    Key Laboratory of Biomedical Information Engineering of the Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.
  • Lei Geng
    Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems , Tianjin , China.
  • Yi Sun
    Department of Environmental and Occupational Health, Program in Public Health, University of California, Irvine, CA, USA.
  • Jinxin Wan
    Department of Medical Imaging, The Second People's Hospital of Lianyungang, Lianyungang, China.

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