Bridging the gap between prostate radiology and pathology through machine learning.

ABSTRACT:

Background

Prostate cancer remains the second deadliest cancer for American men despite clinical advancements. Currently, magnetic resonance imaging (MRI) is considered the most sensitive non-invasive imaging modality that enables visualization, detection, and localization of prostate cancer, and is increasingly used to guide targeted biopsies for prostate cancer diagnosis. However, its utility remains limited due to high rates of false positives and false negatives as well as low inter-reader agreements.

Purpose

Machine learning methods to detect and localize cancer on prostate MRI can help standardize radiologist interpretations. However, existing machine learning methods vary not only in model architecture, but also in the ground truth labeling strategies used for model t

SUBMITTER: Bhattacharya I 

PROVIDER: S-EPMC9543295 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature