Total tumor apparent diffusion coefficient histogram analysis of whole-body DWI-MRI for prognostic stratification in patients with R-ISS stage II multiple myeloma

This study aimed to explore the utility of total tumor apparent diffusion coefficient (ttADC) histogram analysis based on whole-body diffusion-weighted magnetic resonance imaging (DWI-MRI) for prognostic stratification in patients with Revised International Staging System stage II (R-ISS II) multiple myeloma (MM). Patients with R-ISS II MM who underwent baseline whole-body MRI prior to treatment were retrospectively enrolled. The ttADC histogram parameters of the whole-body DWI-MRI were obtained using MR Total Tumor Load software (Siemens Healthcare, Erlangen, Germany). The overall survival (OS) and the progression-free survival (PFS) of the cohort was recorded. Cox regression analyses were used to evaluate the clinical features and ttADC histogram parameters for their association with OS and PFS. A total of 61 R-ISS II MM patients were retrospectively included. During a mean follow-up period of 80 months, 27 patients died, 4 patients lost follow-up for OS, and 4 patients lost follow-up for PFS. Multivariate analysis revealed that increased median ttADC (≥0.620 × 10 −3 mm 2 /s) [hazard ratio (HR) = 2.291, P = 0.046, 95 % confidence interval (CI): 1.014–5.175] was independently associated with OS in R-ISS II MM patients. No significant association was observed between the ttADC histogram parameters and PFS. The median ttADC of whole-body DWI-MRI is an independent predictor of OS in R-ISS II MM patients, suggesting its potential role in further prognostic stratification in this patients' subgroup. • The median ttADC of the whole-body DWI-MRI in Revised International Staging System II (R-ISS II) multiple myeloma patients. • The ttADC histogram parameters quantify whole-body tumor burden, addressing the representational shortcomings of single-site iliac biopsies in the R-ISS. • This imaging-based approach complements molecular staging systems and highlights the potential for multimodal prognostic models combining ADC metrics with cytogenetic data to refine risk stratification in multiple myeloma.