A Follow-up Study On Prospective Para-Clinical Use by Residents of a Re-calibrating Automated Deep Learning System for Prostate Cancer Detection

Abstract

Previously validated fully-automatic detection of prostate cancer by CNNs requires further prospective validation. Para-clinical case-by-case prospective prostate MRI assessment by residents was performed both before and after review of CNN probability maps superimposed on T2w images. A previously and retrospectively validated self-parametrizing nnUNet-architecture CNN trained on more than 1000 voxel-wise annotated prostate MRIs achieved ROC AUC of 0.89. Residents did not substantially change their assessment both at PI-RADS>=3 and >=4 decisions, however achieved excellent working points, indicating success of high reading capability conveyed at an expert center.

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