Keywords: Diagnosis/Prediction, Machine Learning/Artificial Intelligence, Clinically significant prostate cancer, biparametric magnetic resonance imaging, deep learning model, logistic machine learning, risk calculator
Motivation: Early detection of clinically significant prostate cancer (csPCa) through AI models
may reduce unnecessary biopsies and improve screening efficiency.
Goal(s): This study aims to establish and evaluate a csPCa risk calculator by combining a deep
learning artificial intelligence model based on biparametric magnetic resonance imaging (bp-MRI)
with clinical parameters.
Approach: A deep learning model based on bp-MRI was used to predict Gleason score ranges
(Artificial intelligence-Gleason Score, AI-GS), and was combined with clinical indices to create a
csPCa risk calculator.
Results: The established csPCa risk calculator demonstrated excellent screening capabilities
through multicenter studies， outperforming traditional screening methods
Impact: The developed risk calculator offers valuable insights for personalized treatment strategies and enhances prognosis evaluation in clinical practice. Its utilization can lead to more targeted biopsies, reducing unnecessary procedures and improving patient care.
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