Combined 18F-FDG PET/CT Radiomics and Sarcopenia Score in Predicting Relapse-Free Survival and Overall Survival in Patients With Esophagogastric Cancer. (11th August 2022)

• Record Type: Journal Article
• Authors: Anconina, Reut; Ortega, Claudia; Metser, Ur; Liu, Zhihui Amy; Elimova, Elena; Allen, Michael; Darling, Gail E.; Wong, Rebecca; Taylor, Kirsty; Yeung, Jonathan; Chen, Eric X.; Swallow, Carol J.; Jang, Raymond W.; Veit-Haibach, Patrick
• Is Part Of: Clinical nuclear medicine. Volume 47:Number 8(2022)
• Page Start: 684
• Page End: 691
• Publication Date: 2022-08-11
• Subjects: esophageal adenocarcinoma -- radiomics -- sarcopenia -- PET/CT -- outcome prediction; Nuclear medicine -- Periodicals; Radioisotope scanning -- Periodicals; Nuclear Medicine -- Periodicals; 616.07575
• DOI: 10.1097/RLU.0000000000004253
• Languages: English
• ISSNs: 0363-9762

Abstract

Abstract : Purpose: The aim of this study was to determine if radiomic features combined with sarcopenia measurements on pretreatment 18 F-FDG PET/CT can improve outcome prediction in surgically treated adenocarcinoma esophagogastric cancer patients. Patients and Methods: One hundred forty-five esophageal adenocarcinoma patients with curative therapeutic intent and available pretreatment 18 F-FDG PET/CT were included. Textural features from PET and CT images were evaluated using LIFEx software (lifexsoft.org ). Sarcopenia measurements were done by measuring the Skeletal Muscle Index at L3 level on the CT component. Univariable and multivariable analyses were conducted to create a model including the radiomic parameters, clinical features, and Skeletal Muscle Index score to predict patients' outcome. Results: In multivariable analysis, we combined clinicopathological parameters including ECOG, surgical T, and N staging along with imaging derived sarcopenia measurements and radiomic features to build a predictor model for relapse-free survival and overall survival. Overall, adding sarcopenic status to the model with clinical features only (likelihood ratio test P = 0.03) and CT feature ( P = 0.0037) improved the model fit for overall survival. Similarly, adding sarcopenic status ( P = 0.051), CT feature ( P = 0.042), and PET feature ( P = 0.011) improved the model fit for relapse-free survival. Conclusions: PET and CT radiomics derived from combined PET/CT integrated with clinicopathological parameters and sarcopenia measurement might improve outcome prediction in patients with nonmetastatic esophagogastric adenocarcinoma.