Song, Zijian and Zhang, Wei and Jiang, Qingchao and Deng, Longxin and Du, Le and Mou, Weiming and Lai, Yancheng and Zhang, Wenhui and Yang, Yang and Lim, Jasmine and Liu, Kang and Park, Jae Young and Ng, Chi-Fai and Ong, Teng Aik and Wei, Qiang and Li, Lei and Wei, Xuedong and Chen, Ming and Cao, Zhixing and Wang, Fubo and Chen, Rui (2023) Artificial intelligence-aided detection for prostate cancer with multimodal routine health check-up data: An Asian multi-center study. International Journal of Surgery, 109 (12). pp. 3848-3860. ISSN 1743-9191, DOI https://doi.org/10.1097/JS9.0000000000000862.
Full text not available from this repository.Abstract
Background:The early detection of high-grade prostate cancer (HGPCa) is of great importance. However, the current detection strategies result in a high rate of negative biopsies and high medical costs. In this study, the authors aimed to establish an Asian Prostate Cancer Artificial intelligence (APCA) score with no extra cost other than routine health check-ups to predict the risk of HGPCa.Patients and methods:A total of 7476 patients with routine health check-up data who underwent prostate biopsies from January 2008 to December 2021 in eight referral centres in Asia were screened. After data pre-processing and cleaning, 5037 patients and 117 features were analyzed. Seven AI-based algorithms were tested for feature selection and seven AI-based algorithms were tested for classification, with the best combination applied for model construction. The APAC score was established in the CH cohort and validated in a multi-centre cohort and in each validation cohort to evaluate its generalizability in different Asian regions. The performance of the models was evaluated using area under the receiver operating characteristic curve (ROC), calibration plot, and decision curve analyses.Results:Eighteen features were involved in the APCA score predicting HGPCa, with some of these markers not previously used in prostate cancer diagnosis. The area under the curve (AUC) was 0.76 (95% CI:0.74-0.78) in the multi-centre validation cohort and the increment of AUC (APCA vs. PSA) was 0.16 (95% CI:0.13-0.20). The calibration plots yielded a high degree of coherence and the decision curve analysis yielded a higher net clinical benefit. Applying the APCA score could reduce unnecessary biopsies by 20.2% and 38.4%, at the risk of missing 5.0% and 10.0% of HGPCa cases in the multi-centre validation cohort, respectively.Conclusions:The APCA score based on routine health check-ups could reduce unnecessary prostate biopsies without additional examinations in Asian populations. Further prospective population-based studies are warranted to confirm these results.
| Item Type: | Article |
|---|---|
| Funders: | National Natural Science Foundation of China (NSFC) [Grant no. 82272905], Rising-Star Program of the Science and Technology Commission of Shanghai Municipality [Grant no. 21QA1411500], Shanghai Action Plan for Technological Innovation Grant [Grant no. 22ZR1478000, 22ZR1415300, 22511104000 & 23S41900500] |
| Uncontrolled Keywords: | Artificial intelligence; Diagnosis; Prostate biopsy; Prostate cancer; Risk prediction |
| Subjects: | R Medicine > RD Surgery |
| Divisions: | Faculty of Medicine > Surgery Department |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 20 Oct 2025 14:15 |
| Last Modified: | 20 Oct 2025 14:15 |
| URI: | http://eprints.um.edu.my/id/eprint/48076 |
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