CT imaging-derived phenotypes for abdominal muscle and their association with age and sex in a medical biobank
Authors: Vu Phuong T. , Chahine Chantal , Chatterjee Neil , MacLean Matthew T. , Swago Sophia , Bhattaru Abhi , Thompson Elizabeth W. , Ikhlas Anooshey , Oteng Edith , Davidson Lauren , Tran Richard , Hazim Mohamad , Raghupathy Pavan , Verma Anurag , Duda Jeffrey , Gee James , Luks Valerie , Gershuni Victoria , Wu Gary , Rader Daniel , Sagreiya Hersh , Witschey Walter R. ,   The Penn Medicine Biobank,   PMBB Leadership Team, Rader Daniel J. , Ritchie Marylyn D. ,   Patient Recruitment and Regulatory Oversight, Weaver JoEllen , Naseer Nawar , Poindexter Afiya , Hu-Sain Khadijah , Ko Yi-An ,   Lab Operations, Weaver JoEllen , Livingstone Meghan , Vadivieso Fred , DerOhannessian Stephanie , Tran Teo , Stephanowski Julia , Zielinski Monica , Haubein Ned , Dunn Joseph ,   Clinical Informatics, Verma Anurag , Kripke Colleen Morse , Risman Marjorie , Judy Renae ,   Genome Informatics, Verma Anurag , Verma Shefali S. , Bradford Yuki , Dudek Scott , Drivas Theodore 
Published: June 2024

AbstractThe study of muscle mass as an imaging-derived phenotype (IDP) may yield new insights into determining the normal and pathologic variations in muscle mass in the population. This can be done by determining 3D abdominal muscle mass from 12 distinct abdominal muscle regions and groups using computed tomography (CT) in a racially diverse medical biobank. To develop a fully automatic technique for assessment of CT abdominal muscle IDPs and preliminarily determine abdominal muscle IDP variations with age and sex in a clinically and racially diverse medical biobank. This retrospective study was conducted using the Penn Medicine BioBank (PMBB), a research protocol that recruits adult participants during outpatient visits at hospitals in the Penn Medicine network. We developed a deep residual U-Net (ResUNet) to segment 12 abdominal muscle groups including the left and right psoas, quadratus lumborum, erector spinae, gluteus medius, rectus abdominis, and lateral abdominals. 110 CT studies were randomly selected for training, validation, and testing. 44 of the 110 CT studies were selected to enrich the dataset with representative cases of intra-abdominal and abdominal wall pathology. The studies were divided into non-overlapping training, validation and testing sets. Model performance was evaluated using the Sørensen–Dice coefficient. Volumes of individual muscle groups were plotted to distribution curves. To investigate associations between muscle IDPs, age, and sex, deep learning model segmentations were performed on a larger abdominal CT dataset from PMBB consisting of 295 studies. Multivariable models were used to determine relationships between muscle mass, age and sex. The model's performance (Dice scores) on the test data was the following: psoas: 0.85 ± 0.12, quadratus lumborum: 0.72 ± 0.14, erector spinae: 0.92 ± 0.07, gluteus medius: 0.90 ± 0.08, rectus abdominis: 0.85 ± 0.08, lateral abdominals: 0.85 ± 0.09. The average Dice score across all muscle groups was 0.86 ± 0.11. Average total muscle mass for females was 2041 ± 560.7 g with a high of 2256 ± 560.1 g (41–50 year old cohort) and a change of − 0.96 g/year, declining to an average mass of 1579 ± 408.8 g (81–100 year old cohort). Average total muscle mass for males was 3086 ± 769.1 g with a high of 3385 ± 819.3 g (51–60 year old cohort) and a change of − 1.73 g/year, declining to an average mass of 2629 ± 536.7 g (81–100 year old cohort). Quadratus lumborum was most highly correlated with age for both sexes (correlation coefficient of − 0.5). Gluteus medius mass in females was positively correlated with age with a coefficient of 0.22. These preliminary findings show that our CNN can automate detailed abdominal muscle volume measurement. Unlike prior efforts, this technique provides 3D muscle segmentations of individual muscles. This technique will dramatically impact sarcopenia diagnosis and research, elucidating its clinical and public health implications. Our results suggest a peak age range for muscle mass and an expected rate of decline, both of which vary between genders. Future goals are to investigate genetic variants for sarcopenia and malnutrition, while describing genotype–phenotype associations of muscle mass in healthy humans using imaging-derived phenotypes. It is feasible to obtain 3D abdominal muscle IDPs with high accuracy from patients in a medical biobank using fully automated machine learning methods. Abdominal muscle IDPs showed significant variations in lean mass by age and sex. In the future, this tool can be leveraged to perform a genome-wide association study across the medical biobank and determine genetic variants associated with early or accelerated muscle wasting.