Deep-Learning Assessed Muscular Hypodensity Independently Predicts Mortality in DLBCL Patients Younger Than 60 Years.

ABSTRACT:

Background

Muscle depletion (MD) assessed by computed tomography (CT) has been shown to be a predictive marker in solid tumors, but has not been assessed in non-Hodgkin's lymphomas. Despite software improvements, MD measurement remains highly time-consuming and cannot be used in clinical practice.

Methods

This study reports the development of a Deep-Learning automatic segmentation algorithm (DLASA) to measure MD, and investigate its predictive value in a cohort of 656 diffuse large B cell lymphoma (DLBCL) patients included in the GAINED phase III prospective trial (NCT01659099).

Results

After training on a series of 190 patients, the DLASA achieved a Dice coefficient of 0.97 ± 0.03. In the cohort, the median skeletal muscle index was 50.2 cm²/m²

SUBMITTER: Jullien M 

PROVIDER: S-EPMC8466314 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature