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Development of a whole spinal MRI-based tumor burden scoring method in participants with multiple myeloma: a pilot study of prognostic significance.

Sha CuiYinnan GuoJianting LiWenjin BianWenqi WuWenjia ZhangQian ZhengHaonan GuanJun WangJinliang Niu
Published in: Annals of hematology (2024)
The aim of the study was to develop a new whole spinal MRI-based tumor burden scoring method in participants with newly diagnosed multiple myeloma (MM) and to explore its prognostic significance. We prospectively recruited participants with newly diagnosed MM; performed whole spinal MRI (sagittal FSE T 1 WI, sagittal IDEAL T 2 WI, and axial FLAIR T 2 WI) on them; and collected their clinical data, early treatment response, progression-free survival (PFS), and overall survival (OS). We developed a new tumor burden scoring method according to the extent of bone marrow infiltration in five MRI patterns. All participants were divided into good response and poor response groups after four treatment cycles. Univariate, multivariate analyses, and ROC were used to determine the performance of independent predictors. Thresholds for PFS and OS were calculated using X-tile, and their prognostic significance were assessed by Kaplan-Meier. The Kruskal-Wallis H test was used to compare the differences of tumor burden score between the revised International Staging System (R-ISS) stages. The new tumor burden scoring method was used in 62 participants (median score, 12; range, 0-18). The tumor burden score (OR 1.266, p = 0.002) was an independent predictor of poor response and the AUC was 0.838. Higher tumor burden scores were associated with shorter PFS (p = 0.002) and OS (p = 0.011). The tumor burden score was higher in R-ISS-III than in R-ISS-I and R-ISS-II (p = 0.016 and p = 0.006, respectively). The tumor burden score was an excellent predictor of prognosis and may serve as a supplemental marker for R-ISS.
Keyphrases
  • magnetic resonance imaging
  • bone marrow
  • multiple myeloma
  • risk factors
  • machine learning
  • contrast enhanced
  • mesenchymal stem cells
  • lymph node
  • magnetic resonance
  • deep learning
  • big data
  • replacement therapy