Full automation of total metabolic tumor volume from FDG-PET/CT in DLBCL for baseline risk assessments.

ABSTRACT:

Background

Current radiological assessments of ¹⁸fluorodeoxyglucose-positron emission tomography (FDG-PET) imaging data in diffuse large B-cell lymphoma (DLBCL) can be time consuming, do not yield real-time information regarding disease burden and organ involvement, and hinder the use of FDG-PET to potentially limit the reliance on invasive procedures (e.g. bone marrow biopsy) for risk assessment.

Methods

Our aim is to enable real-time assessment of imaging-based risk factors at a large scale and we propose a fully automatic artificial intelligence (AI)-based tool to rapidly extract FDG-PET imaging metrics in DLBCL. On availability of a scan, in combination with clinical data, our approach generates clinically informative risk scores with minimal resource requirem

SUBMITTER: Jemaa S 

PROVIDER: S-EPMC9373298 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature