前列腺MRI深度学习模型通过PI-RADS指导的学习有效区分中危病例，在检测csPCa的AUC（0.73-0.88）显著优于放射科医生和临床模型 ...

通过预测模型指导对影像学检查呈阳性的男性（PI-RADS 3-5）进行临床显著前列腺癌（csPCa）的活检决策。我们旨在阐明前列腺区域（zone）对PI-RADS v2.1预测准确性的影响，确定一个疑似csPCa的量化表观扩散系数（ADC）值阈值，并创建一个逻辑回归模型，该模型独特之 ...

PI-RADS version 2.1 demonstrated improved detection of clinically significant prostate cancer in the transition zone. For detecting clinically significant prostate cancer (csPCa), prostate imaging ...

Men overestimate the risks for clinically significant prostate cancer based on prostate MRI PI-RADS findings, regardless of the context in which those risks are presented, suggesting that clinicians ...

Adding baseline MRI to conventional prostate cancer risk stratification could improve prognostic accuracy, potentially affecting active surveillance and treatment decisions for some patients, ...

Survival impact of initial local therapy selection for men under 60 with high risk prostate cancer. Prostate cancer (PCa) in 696 hypogonadal men with and without long-term testosterone therapy (TTh): ...