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

BMC Medicine

Research Article

Shaohua Chen¹  Chengbang Wang¹  Chunmeng Wei¹  Huiyong Zhang¹  Fubo Wang¹  Wenhao Lu¹  Jiwen Cheng²  Wen Cheng³  Xiaodong Zhao³  Zhe Liu³  Zuheng Wang³  Yalong Xu⁴  Xi Chen⁴  Xing He⁴  Jin Ji⁵  Rui Chen⁶  Huan Xu⁷  Guopeng Yu⁸  Yue Gu⁸  Bin Xu⁸  Xuedong Wei⁹  Huiru Lu^1,10  Xingfa Chen^1,10  Chong Qian^1,11  Guijian Pang^1,11  Junyi Chen^1,12  Kangxian Jiang^1,12  Ming Chen^1,13 

[1] 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, 530021, Nanning, Guangxi, China;Department of Urology, Institute of Urology and Nephrology, First Affiliated Hospital of Guangxi Medical University, 530021, Nanning, Guangxi, China;Department of Urology, Institute of Urology and Nephrology, First Affiliated Hospital of Guangxi Medical University, 530021, Nanning, Guangxi, China;Department of Urology, Jinling Hospital, Medical School of Nanjing University, 210002, Nanjing, China;Department of Urology, Shanghai Changhai Hospital, Second Military Medical University, 200433, Shanghai, China;Department of Urology, Shanghai Changhai Hospital, Second Military Medical University, 200433, Shanghai, China;Department of Urology, Naval Medical Center, Naval Medical University, 200052, Shanghai, China;Department of Urology, Shanghai Changhai Hospital, Second Military Medical University, 200433, Shanghai, China;Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, 200127, Shanghai, China;Department of Urology, Shanghai Changhai Hospital, Second Military Medical University, 200433, Shanghai, China;Department of Urology, Shanghai Ninth People’s Hospital, Shanghai Jiaotong University School of Medicine, 200011, Shanghai, China;Department of Urology, Shanghai Ninth People’s Hospital, Shanghai Jiaotong University School of Medicine, 200011, Shanghai, China;Department of Urology, The First Affiliated Hospital of Soochow University, 215006, Suzhou, China;Department of Urology, The First Affiliated Hospital of Xi’an Jiaotong University, 710061, Xi’an, Shaanxi, China;Department of Urology, The First People’s Hospital of Yulin, 537000, Yulin, Guangxi, China;Department of Urology, The Second Affiliated Hospital of Fujian Medical University, 362000, Quanzhou, Fujian, China;Department of Urology, Zhongda Hospital, Southeast University, Nanjing, China;

关键词: Prostate cancer;    PCAIDS;    Artificial intelligence;    Machine learning;    Diagnosis;   

DOI  :  10.1186/s12916-023-02964-x

 received in 2023-03-21, accepted in 2023-06-27,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundThe 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).MethodsThis 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.ResultsThe 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 Changhai prospective cohort, and 0.850 in the Zhongda prospective cohort. DCA showed that the PCAIDS was superior to PSA or fPSA/tPSA for diagnosing csPCa with a higher net benefit for all threshold probabilities in all cohorts. Setting a fixed sensitivity of 95%, a total of 32.2%, 17.6%, and 26.3% of unnecessary biopsies could be avoided with less than 5% of csPCa missed in the validation cohort, Changhai and Zhongda prospective cohorts, respectively.ConclusionsThe PCAIDS was an effective tool to inform decision-making regarding the need for prostate biopsy and prebiopsy examinations such as mpMRI. Further prospective and international studies are warranted to validate the findings of this study.Trial registrationChinese Clinical Trial Registry ChiCTR2100048428. Registered on 06 July 2021.

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

Fig. 7

42490_2023_74_Article_IEq8.gif

Fig. 1

Fig. 6

Fig. 1

Fig. 6

42490_2023_74_Article_IEq3.gif

Fig. 8

Fig. 5

Fig. 2

Fig. 8