医学
前列腺癌
接收机工作特性
前列腺
磁共振成像
放射科
核医学
特征(语言学)
临床实习
人工智能
癌症
计算机科学
内科学
语言学
哲学
家庭医学
作者
Christian Roest,Derya Yakar,Dorjan Ivan Rener Sitar,Joeran S. Bosma,Dennis B. Rouw,Stefan J. Fransen,Henkjan Huisman,Thomas C. Kwee
标识
DOI:10.1097/rli.0000000000001102
摘要
Deep learning (DL) studies for the detection of clinically significant prostate cancer (csPCa) on magnetic resonance imaging (MRI) often overlook potentially relevant clinical parameters such as prostate-specific antigen, prostate volume, and age. This study explored the integration of clinical parameters and MRI-based DL to enhance diagnostic accuracy for csPCa on MRI.
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