AI derived Whole-Body MRI metrics in multiple myeloma patients reveal unique insights into body composition and outcomes.

Patients with multiple myeloma are frequently exposed to prolonged, multi-line and multi-drug treatment, 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 standardisation of whole-body MRI (WBMRI) together with recent improvements in AI methodologies for automated organ and tissue segmentation presents an opportunity to complement accurate disease assessments with body composition metrics. We report for the first time, development of an AI-based pipeline to enable automated quantitative image-derived phenotypes in non-diseased 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 < 0.001), characterised 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 (HR = 0.60; 95% 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. (NCT02403102).