Artificial intelligence for the diagnosis of clinically significant prostate cancer based on multimodal data: a multicenter study

作者：Huiyong Zhang, Jin Ji, Zhe Liu, Huiru Lu, Chong Qian, Chunmeng Wei, Shaohua Chen, Wenhao Lu, Chengbang Wang, Huan Xu, Yalong Xu, Xi Chen, Xing He, Zuheng Wang, Xiaodong Zhao, Wen Cheng, Xingfa Chen, Guijian Pang, Guopeng Yu, Yue Gu, Kangxian Jiang, Bin Xu, Junyi Chen, Bin Xu, Xuedong Wei, Ming Chen, Rui Chen, Jiwen Cheng, Fubo Wang · 发表于：BMC Medicine · 年份：2023 · DOI：10.1186/s12916-023-02964-x · 被引用次数：31 · 研究领域：Prostate Cancer Diagnosis and Treatment、AI in cancer detection、Prostate Cancer Treatment and Research

BACKGROUND: The introduction of multiparameter MRI and novel biomarkers has greatly improved the prediction of clinically significant prostate cancer (csPCa). However, decision-making regarding prostate biopsy and prebiopsy examinations is still difficult. We aimed to establish a quick and economic tool to improve the detection of csPCa based on routinely performed clinical examinations through an automated machine learning platform (AutoML). METHODS: This study included a multicenter retrospective cohort and two prospective cohorts with 4747 cases from 9 hospitals across China. The multimodal data, including demographics, clinical characteristics, laboratory tests, and ultrasound reports, of consecutive participants were retrieved using extract-transform-load tools. AutoML was applied to explore potential data processing patterns and the most suitable algorithm to build the Prostate Cancer Artificial Intelligence Diagnostic System (PCAIDS). The diagnostic performance was determined by the receiver operating characteristic curve (ROC) for discriminating csPCa from insignificant prostate cancer (PCa) and benign disease. The clinical utility was evaluated by decision curve analysis (DCA) and waterfall plots. RESULTS: The random forest algorithm was applied in the feature selection, and the AutoML algorithm was applied for model establishment. The area under the curve (AUC) value in identifying csPCa was 0.853 in the training cohort, 0.820 in the validation cohort, 0.807 in the ...