Cancer Medicine (Apr 2024)

The quantitative parameters based on marrow metabolism derived from synthetic MRI: A pilot study of prognostic value in participants with newly diagnosed multiple myeloma

  • Sha Cui,
  • Yinnan Guo,
  • Weiran Niu,
  • Jianting Li,
  • Wenjin Bian,
  • Wenqi Wu,
  • Wenjia Zhang,
  • Qian Zheng,
  • Jun Wang,
  • Jinliang Niu

DOI
https://doi.org/10.1002/cam4.7109
Journal volume & issue
Vol. 13, no. 7
pp. n/a – n/a

Abstract

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Abstract Background The value of SyMRI‐derived parameters from lumbar marrow for predicting early treatment response and optimizing the risk stratification of the Revised International Staging System (R‐ISS) in participants with multiple myeloma (MM) is unknown. Methods We prospectively enrolled participants with newly diagnosed MM before treatment. The SyMRI of lumbar marrow was used to calculate T1, T2, and PD values and the clinical features were collected. All participants were divided into good response (≥VGPR) and poor response (<VGPR) groups after four treatment cycles. Univariate, multivariate analyses and ROC were used to identify prognostic significance. Mann–Whitney U‐tests were used to compare the SyMRI parameters between genders. The value of optimizing the risk stratification was analyzed by Fisher's exact tests at each R‐ISS stage. Results Fifty‐nine participants (good response, n = 33; poor response, n = 26) were evaluated. The bone marrow plasma cell percentage, β2‐microglobulin, T1 and T2 value were difference between two groups (all p < 0.05). The T1 (odds ratio 1.003, p = 0.005) and T2 values (odds ratio 0.910, p = 0.002) were independent predictors and the AUC and cut‐off values were 0.787, 967.2 ms and 0.784, 75.9 ms, respectively. There were no significant differences in SyMRI parameters between genders. Participants with both T1 value ≥967.2 ms and T2 value ≤75.9 ms in the R‐ISS II stage were potentially to get poor response. Conclusions Synthetic MRI is a promising tool for predicting early treatment response to MM and promoting R‐ISS II stage risk stratification.

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