Deep learning and radiomics framework for PSMA-RADS classification of prostate cancer on PSMA PET.

ABSTRACT:

Background

Accurate classification of sites of interest on prostate-specific membrane antigen (PSMA) positron emission tomography (PET) images is an important diagnostic requirement for the differentiation of prostate cancer (PCa) from foci of physiologic uptake. We developed a deep learning and radiomics framework to perform lesion-level and patient-level classification on PSMA PET images of patients with PCa.

Methods

This was an IRB-approved, HIPAA-compliant, retrospective study. Lesions on [¹⁸F]DCFPyL PET/CT scans were assigned to PSMA reporting and data system (PSMA-RADS) categories and randomly partitioned into training, validation, and test sets. The framework extracted image features, radiomic features, and tissue type information from a cropped PET image s

SUBMITTER: Leung KH 

PROVIDER: S-EPMC9800682 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature