Machine learning-based MRI imaging for prostate cancer diagnosis: systematic review and meta-analysis.
Authors
Affiliations (13)
Affiliations (13)
- Department of Medical Imaging, The Second People's Hospital of Lianyungang, Lianyungang city, China.
- Department of Medical Imaging, Cancer Hospital of Lianyungang, Lianyungang city, China.
- Department of Medical Imaging, The Second People's Hospital of Lianyungang Affiliated with Kangda College of Nanjing Medical University, Lianyungang city, China.
- Department of Information System, Lianyungang 149 Hospital, Lianyungang city, China.
- Department of Technology, , Jiangsu Jerry Technology Co. Ltd., Lianyungang city, China.
- Department of Technology, , CGN (Jiangsu) New Energy Resources Co. Ltd., Lianyungang city, China.
- Department of Integrated Management, Lianyungang Haizhou Bay Marine Fisheries Development Institute, Lianyungang city, China.
- Department of Medical Imaging, The Second People's Hospital of Lianyungang, Lianyungang city, China. [email protected].
- Department of Medical Imaging, Cancer Hospital of Lianyungang, Lianyungang city, China. [email protected].
- Department of Medical Imaging, The Second People's Hospital of Lianyungang Affiliated with Kangda College of Nanjing Medical University, Lianyungang city, China. [email protected].
- Department of Medical Imaging, The Second People's Hospital of Lianyungang, Lianyungang city, China. [email protected].
- Department of Medical Imaging, Cancer Hospital of Lianyungang, Lianyungang city, China. [email protected].
- Department of Medical Imaging, The Second People's Hospital of Lianyungang Affiliated with Kangda College of Nanjing Medical University, Lianyungang city, China. [email protected].
Abstract
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. 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. 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). 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.