Relevance of diffusion-weighted imaging with background body signal suppression for staging, prognosis, morphology, treatment response, and apparent diffusion coefficient in plasma-cell neoplasms: A single-center, retrospective study

Accurate staging and evaluation of therapeutic effects are important in managing plasma-cell neoplasms. Diffusion-weighted imaging with body signal suppression magnetic resonance imaging (DWIBS-MRI) allows for acquisition of whole-body volumetric data without radiation exposure. This study aimed to investigate the usefulness of DWIBS-MRI in plasma-cell neoplasms. We retrospectively analyzed 29 and 8 Japanese patients with multiple myeloma and monoclonal gammopathy of undetermined significance, respectively, who underwent DWIBS-MRI. We conducted a histogram analysis of apparent diffusion coefficient values. The correlations between each histogram parameter and staging, cell maturation, prognosis, and treatment response were evaluated. We found that the apparent diffusion coefficient values in patients with monoclonal gammopathy of undetermined significance were lower than those in patients with multiple myeloma. Pretreatment apparent diffusion coefficient values of immature myeloma were lower than those of mature myeloma. Moreover, these values decreased in proportion to stage progression in Durie-Salmon classification system but showed no significant correlation with other staging systems or prognosis. Patients were stratified as responder, stable, and non-responder based on the International Myeloma Working Group criteria. The magnitude of changes in apparent diffusion coefficients differed significantly between responders and non-responders (0.154 ± 0.386 ×10–3 mm2/s vs. -0.307 ± 0.424 ×10–3 mm2/s, p = 0.003). Although its usefulness has yet to be established, DWIBS-MRI combined with apparent diffusion coefficient measurement allowed for excellent response evaluation in patients with multiple myeloma. Furthermore, apparent diffusion coefficient analysis using DWIBS-MRI may be useful in predicting cell maturation and total tumor volume.