ECR: AI algorithm quantifies fatty tissue on chest CT for lung cancer prognosis

VIENNA - A deep-learning algorithm used with chest CT can help clinicians quantify patients' subcutaneous fat tissue levels on lung cancer screening -- and thus better predict disease outcomes, according to a presentation delivered on 29 February at ECR 2024. The findings could help clinicians tailor patient care, said presenter Dr. Fabian Pallasch of University Medical Center Freiburg in Germany. "[We found that] subcutaneous adipose tissue density at baseline and a decrease in subcutaneous adipose tissue volume and density within one year [were] associated with mortality beyond clinical risk factors, which may help to improve personalized risk assessment," he told session attendees. There's "increasing evidence that body composition is important for personalized prognostication in cardiovascular disease and lung cancer," Pallasch said, but most studies have focused on muscle, and little is known about the role adipose tissue can play. To address this knowledge gap, the investigators developed and tested a deep-learning model for quantifying 3D subcutaneous adipose tissue (SAT) on low-dose CT (LDCT) and investigated any associations between SAT measures and mortality among 26,144 participants in the National Lung Screening Trial (NSLT). Medical students under resident supervision used data from manual segmentations to develop the model, which consisted of 50 testing and 150 training samples. Pallasch and colleagues analyzed CT exam results from study participants at base...
Source: AuntMinnie.com Headlines - Category: Radiology Authors: Tags: Clinical News CT Source Type: news