MRI-based prostate cancer classification using 3D efficient capsule network.

ABSTRACT:

Background

Prostate cancer (PCa) is the most common cancer in men and the second leading cause of male cancer-related death. Gleason score (GS) is the primary driver of PCa risk-stratification and medical decision-making, but can only be assessed at present via biopsy under anesthesia. Magnetic resonance imaging (MRI) is a promising non-invasive method to further characterize PCa, providing additional anatomical and functional information. Meanwhile, the diagnostic power of MRI is limited by qualitative or, at best, semi-quantitative interpretation criteria, leading to inter-reader variability.

Purposes

Computer-aided diagnosis employing quantitative MRI analysis has yielded promising results in non-invasive prediction of GS. However, convolutional neural networks (CNNs) do

SUBMITTER: Li Y 

PROVIDER: S-EPMC12739645 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature