Artificial intelligence-aided detection for prostate cancer with multimodal routine health check-up data: an Asian multi-center study

Author:

Song Zijian12,Zhang Wei1,Jiang Qingchao34,Deng Longxin1,Du Le34,Mou Weiming15,Lai Yancheng1,Zhang Wenhui1,Yang Yang6,Lim Jasmine7,Liu Kang8,Park Jae Young9,Ng Chi-Fai8,Ong Teng Aik7,Wei Qiang10,Li Lei11,Wei Xuedong12,Chen Ming13,Cao Zhixing34,Wang Fubo141516,Chen Rui12

Affiliation:

1. Department of Urology, Shanghai Changhai Hospital, Second Military Medical University

2. Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine

3. Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education

4. State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai

5. Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine

6. Department of Clinical Laboratory, Nanjing Jinling Hospital, Nanjing University School of Medicine

7. Department of Urology, University of Malaya Medical Centre, Kuala Lumpur, Malaysia

8. SH Ho Urology Centre, Department of Surgery, The Chinese University of Hong Kong, Hong Kong SAR, China

9. Department of Urology, Korea University Ansan Hospital, Soule, Korea

10. Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, Sichuan

11. Department of Urology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an Shaanxi

12. Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou

13. Department of Urology, Zhongda Hospital, Southeast University, Nanjing

14. School of Life Sciences, Guangxi Medical University, Nanning, Guangxi

15. Center for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi

16. Department of Urology, the First Affiliated Hospital of Guangxi Medical University, Guangxi Medical University, Guangxi China

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.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

General Medicine,Surgery

Reference34 articles.

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