Deep learning-based image quality assessment: impact on detection accuracy of prostate cancer extraprostatic extension on MRI.

ABSTRACT:

Objective

To assess impact of image quality on prostate cancer extraprostatic extension (EPE) detection on MRI using a deep learning-based AI algorithm.

Materials and methods

This retrospective, single institution study included patients who were imaged with mpMRI and subsequently underwent radical prostatectomy from June 2007 to August 2022. One genitourinary radiologist prospectively evaluated each patient using the NCI EPE grading system. Each T2WI was classified as low- or high-quality by a previously developed AI algorithm. Fisher's exact tests were performed to compare EPE detection metrics between low- and high-quality images. Univariable and multivariable analyses were conducted to assess the predictive value of image quality for pathological EPE.

Results

A to

SUBMITTER: Lin Y 

PROVIDER: S-EPMC11300622 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature