Simulated clinical deployment of fully automatic deep learning for clinical prostate MRI assessment.

ABSTRACT:

Objectives

To simulate clinical deployment, evaluate performance, and establish quality assurance of a deep learning algorithm (U-Net) for detection, localization, and segmentation of clinically significant prostate cancer (sPC), ISUP grade group ≥ 2, using bi-parametric MRI.

Methods

In 2017, 284 consecutive men in active surveillance, biopsy-naïve or pre-biopsied, received targeted and extended systematic MRI/transrectal US-fusion biopsy, after examination on a single MRI scanner (3 T). A prospective adjustment scheme was evaluated comparing the performance of the Prostate Imaging Reporting and Data System (PI-RADS) and U-Net using sensitivity, specificity, predictive values, and the Dice coefficient.

Results

In the 259 eligible men (median 64 [IQR 61-72] years), PI-

SUBMITTER: Schelb P 

PROVIDER: S-EPMC7755653 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature