AI plus CT tracks fatty tissue changes in people at risk of lung cancer

Adding deep learning to CT imaging to assess changes in subcutaneous adipose tissue (SAT) over time could help predict outcomes among individuals vulnerable to lung cancer, according to research presented at the recent RSNA meeting.The finding could improve risk assessment among those at high risk of the disease, particularly heavy smokers, said presenter Fabian Pallasch, MD, of University Medical Center Freiburg in Germany."Deep learning allows for opportunistic screening of subcutaneous adipose tissue in lung cancer screening chest CTs," he said.Studies suggest that body composition can help predict cancer and cardiovascular disease outcomes, the investigators noted. Most of these studies have focused on changes in the muscle; less is understood about the role assessment of subcutaneous adipose tissue (SAT) could play in a screening setting.The group developed a deep learning model for automatic 3D quantification of adipose tissue on low-dose chest CT and assessed any associations between this tissue and mortality among a population of heavy smokers at high risk of lung cancer who participated in screening.Included in the study were 26,144 patients who participated in the National Lung Cancer Screening Trial (NLST) at baseline and at one-year follow-up (total scans, 52,228); participants were between the ages of 55 and 70 and had at least 30 smoking pack years or more. The team tracked SAT volume and density as measures of the SAT quality in each patient. The primary outcom...
Source: AuntMinnie.com Headlines - Category: Radiology Authors: Tags: Subspecialties Chest Radiology Source Type: news