Whole-body uptake classification and prostate cancer staging in ⁶⁸Ga-PSMA-11 PET/CT using dual-tracer learning.

ABSTRACT:

Purpose

In PSMA-ligand PET/CT imaging, standardized evaluation frameworks and image-derived parameters are increasingly used to support prostate cancer staging. Clinical applicability remains challenging wherever manual measurements of numerous suspected lesions are required. Deep learning methods are promising for automated image analysis, typically requiring extensive expert-annotated image datasets to reach sufficient accuracy. We developed a deep learning method to support image-based staging, investigating the use of training information from two radiotracers.

Methods

In 173 subjects imaged with ⁶⁸Ga-PSMA-11 PET/CT, divided into development (121) and test (52) sets, we trained and evaluated a convolutional neural network to both classify sites of elevated tra

SUBMITTER: Capobianco N 

PROVIDER: S-EPMC8803695 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature