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

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机构： [1]Department of Urology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai 200433, China [2]Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China [3]Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China [4]State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China [5]Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China [6]Department of Clinical Laboratory, Nanjing Jinling Hospital, Nanjing University School of Medicine, Nanjing, China [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 610041, China [11]Department of Urology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi'an 710061, Shaanxi, China [12]Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou 215006, China.West China Hospital, Chengdu, China [13]Department of Urology, Zhongda Hospital, Southeast University, Nanjing, China [14]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 530021, Guangxi, China [15]Department of Urology, Institute of Urology and Nephrology, First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi, China

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关键词： Prostate cancer diagnosis prostate biopsy artificial intelligence risk prediction

摘要：

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, we 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.A total of 7476 patients with routine health checkup data who underwent prostate biopsies from January 2008 to December 2021 in eight referral centers 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-center 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.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-center 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-center validation cohort, respectively.The APCA score based on routine health checkups could reduce unnecessary prostate biopsies without additional examinations in Asian populations. Further prospective population-based studies are warranted to confirm these results.Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.

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出版当年[2023]版：

大类 | 2 区医学

小类 | 2 区外科

最新[2023]版：

大类 | 2 区医学

小类 | 2 区外科

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出版当年[2023]版：

Q1 SURGERY

最新[2023]版：

Q1 SURGERY

影响因子： 12.5 最新[2023版] 8.9 最新五年平均 12.5 出版当年[2023版] 8.9 出版当年五年平均 15.3 出版前一年[2022版]

第一作者：

第一作者机构： [1]Department of Urology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai 200433, China [2]Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China

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通讯作者：

通讯机构： [1]Department of Urology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai 200433, China [2]Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China [3]Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China [4]State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China [5]Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China [7]Department of Urology, University of Malaya Medical Centre, Kuala Lumpur, Malaysia [*1]No. 168 Changhai Road, Yangpu District, Shanghai, China. [*2]130 Meilong Road，Xuhui ，shanghai，200237. [*3]No. 22, Shuangyong Road, Qingxiu District, Nanning City, Guangxi Zhuang Autonomous Region

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

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