The added value of an AI-based body composition analysis in a lung cancer screening population: preliminary results

Body composition has been linked with clinical and prognostic outcomes in patients with cancer and cardiovascular diseases. Body composition analysis in lung cancer screening (LCS) is very limited. This study aimed at assessing the association of subcutaneous fat volume (SFV) and subcutaneous fat de...

Full description

Saved in:
Bibliographic Details
Published in:Nutrition, metabolism, and cardiovascular diseases p. 103696
Main Authors: Ledda, Roberta Eufrasia, Sabia, Federica, Valsecchi, Camilla, Suatoni, Paola, Milanese, Gianluca, Rolli, Luigi, Marchianò, Alfonso Vittorio, Pastorino, Ugo
Format: Journal Article
Language:English
Published: Netherlands Elsevier B.V 22-07-2024
Subjects:
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Body composition has been linked with clinical and prognostic outcomes in patients with cancer and cardiovascular diseases. Body composition analysis in lung cancer screening (LCS) is very limited. This study aimed at assessing the association of subcutaneous fat volume (SFV) and subcutaneous fat density (SFD), measured on chest ultra-low dose computed tomography (ultra-LDCT) images by a fully automated artificial intelligence (AI)-based software, with clinical and anthropometric characteristics in a LCS population. Demographic, clinical, and dietary data were obtained from the written questionnaire completed by each participant at the first visit, when anthropometric measurements, blood sample collection and chest ultra-LDCT were performed. Images were analyzed for automated 3D segmentation of subcutaneous fat and muscle. The analysis included 938 volunteers (372 females); men with a smoking history of ≥40 pack-years had higher SFV (p = 0.0009), while former smokers had lower SFD (p = 0.0019). In female participants, SFV and SFD differed significantly according to age. SFV increased with rising BMI, waist circumference, waist-hip ratio, and CRP levels ≥2 mg/L (p < 0.0001), whereas SFD decreased with rising BMI, waist circumference, waist-hip ratio, and CRP levels ≥2 mg/L (p < 0.001) in both sexes. SFV was associated with glycemia and triglycerides levels (p = 0.0067 and p=<0.0001 in males, p = 0.0074 and p < 0.0001 in females, respectively), while SFD with triglycerides levels (p < 0.0001). We observed different associations of SFV and SFD with age and smoking history between men and women, whereas the association with anthropometric data, CRP, glycemia and triglycerides levels was similar in the two sexes. •Fully automated AI-based body composition analysis is feasible on chest ultra-low dose CT imaging.•Subcutaneous fat volume (SFV) was significantly higher in men with pack-years ≥40 and in women aged ≥65.•Median SFV increased significantly with rising BMI, waist circumference and waist-hip ratio in both sexes.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0939-4753
1590-3729
1590-3729
DOI:10.1016/j.numecd.2024.07.013