Keywords: Prostate, Machine Learning/Artificial Intelligence, Multicenter studyZonal segmentation is important in the management of prostatic diseases. Many studies have demonstrated the feasibility of training CNN models for zonal segmentation. However, they lack validation in non-public datasets and consideration of the patients’ characteristics. In this study, we validated the model’s utility for prostate zonal segmentation on T2WI in different external testing datasets. The model yielded good performance regardless of the variations in the patients’ clinicopathological characteristics. The model showed higher performance than the junior radiologist in PZ segmentation. Prostate morphology and MR scanner parameters, especially CG volume and vendor, impact zonal segmentation performance.
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