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Updated: Oct 25, 2025
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Published on: April 15, 2022
Evaluation of Diffuse Bone Marrow Infiltration Pattern in Monoclonal Plasma Cell Diseases by Quantitative Whole-body
Mengtian Sun1, Jingliang Cheng1, Cuiping Ren1
1Department of Magnetic Resonance, The First Affiliated Hospital of Zhengzhou University, 1st, Jianshe Dong Road, Zhengzhou, Henan Province, 450052, PR China.
Rationale And Objectives:
To analyze diffuse infiltration pattern in monoclonal plasma cell diseases by diffusion-weighted whole-body imaging with background body signal suppression (DWIBS) and quantitative chemical-shift encoded MRI.
Materials And Methods:
Ninety-nine patients with monoclonal plasma cell diseases and 15 healthy control subjects were retrospectively analyzed. All patients underwent whole-body MRI (including DWIBS and mDIXON Quant) and were divided into three groups: monoclonal gammopathy of undetermined significance (MGUS), smoldering multiple myeloma (SMM), and multiple myeloma (MM). Apparent diffusion coefficient (ADC), fat fraction (FF), and T2* values for each group were calculated then analyzed by one-way ANOVA and receiver operating characteristic curve. Correlations of ADC, FF, and T2* with clinical indices were analyzed with Spearman correlation test.
Results:
The ADC and T2* values of MM were significantly higher than those of the healthy control, MGUS and SMM (ADC: p = 0.003, p = 0.003, and p = 0.042; T2*: all with p < 0.001). The FF values of MM were significantly lower than those of the healthy control, MGUS and SMM (p < 0.001, p < 0.001 and p = 0.034). The ADC, FF, and T2* thresholds for recognizing MM and MGUS+SMM were 0.51 × 10-3 mm2/s, 31.14%, and 10.53 ms, respectively. The ADC, FF, and T2* values were identified to be significantly associated with bone marrow plasma cells and hemoglobin in patients (all with p < 0.001).
Conclusion:
ADC, FF, and T2* were significantly correlated with clinical indices related to monoclonal plasma cell diseases. MM with the diffuse infiltration pattern can be distinguished more objectively from MGUS and SMM by quantitative functional MRI parameters.
