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Background
Accurately detecting adverse pathology (AP) presence in prostate cancer patients is important for personalized clinical decision‐making. Radiologists' assessment based on clinical characteristics showed poor performance for detecting AP presence.
Purpose
To develop deep learning models for detecting AP presence, and to compare the performance of these models with those of a clinical...
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