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

ABSTRACT:

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 wa

SUBMITTER: Zhang H 

PROVIDER: S-EPMC10367399 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature