AI-derived whole-body MRI metrics in patients with multiple myeloma reveal unique insights into body composition and...

Patients with multiple myeloma are frequently exposed to prolonged, multiline and multidrug treatments, factors that may substantially influence overall health, physical reserve, and physiological resilience. Imaging of organs that are captured by whole-body scans can be opportunistically interrogated to derive direct, quantitative measures of body composition. Advances and standardization of whole-body magnetic resonance imaging (WBMRI) together with recent improvements in artificial intelligence (AI) methodologies for automated organ and tissue segmentation presents an opportunity to complement accurate disease assessments with body composition metrics. We report, to our knowledge, for the first time, development of an AI-based pipeline to enable automated quantitative image-derived phenotypes in nondiseased tissue from WBMRI in patients with multiple myeloma. We have demonstrated that a deep-learning pipeline can derive body composition metrics from routinely acquired clinical WBMRI. Throughout treatment, significant longitudinal changes were observed (P< .001), characterized by a decrease in abdominal skeletal muscle (ASM) alongside transient increases in abdominal subcutaneous and visceral adipose tissue. We have also shown that greater reserves of ASM (hazard ratio [HR], 0.60; 95% confidence interval [CI], 0.40-0.89) and abdominal subcutaneous adipose tissue (HR, 0.67; 95% CI, 0.46-0.98) at baseline are associated with better progression-free survival. Conversely increases in visceral adipose tissue over time was associated with inferior progression-free survival (HR, 2.89; 95% CI, 1.65-5.09). These findings support opportunistic body composition phenotyping from diagnostic WBMRI as a scalable biomarker for risk stratification (concordance index, 0.725) and mechanistic study. This trial was registered at www.clinicaltrials.gov as NCT02403102.