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Establishment of a Human Multiple Myeloma Xenograft Model in the Chicken to Study Tumor Growth, Invasion and Angiogenesis
Published on: May 1, 2015
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Clinical Feature and Predictive Model for Transplanted Patients with Functional High-Risk Multiple Myeloma
Tongyong Yu1, Meilan Chen1, Beihui Huang1
1Department of Hematology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China.
Archives of Medical Research
|September 5, 2025
Summary
This study identifies key predictors for functional high-risk (FHR) multiple myeloma (MM) in transplanted patients, enabling early risk assessment. The developed prognostic model accurately predicts FHR-MM, aiding in personalized treatment strategies.
Area of Science:
- Hematology
- Oncology
- Medical Statistics
Background:
- A subset of multiple myeloma (MM) patients experience early relapse despite lacking high-risk features at diagnosis, termed functional high-risk (FHR) MM.
- FHR-MM is associated with an inferior prognosis, necessitating improved risk stratification methods.
Purpose of the Study:
- To compare FHR and standard risk (SR) MM cohorts to identify clinical risk factors for early relapse.
- To develop and validate a prognostic model for the early prediction of FHR in transplanted MM patients.
Main Methods:
- Retrospective cohort study analyzing clinical data of 357 MM patients.
- Univariate and multivariate analyses to identify independent risk factors for FHR-MM.
- Logistic regression analysis to develop a prognostic nomogram, internally validated.
Main Results:
- Independent predictors of FHR-MM identified: elevated LDH, high baseline PET-CT SUVmax, insufficient post-induction SUVmax reduction, low platelet count, elevated ferritin, M-protein decline pattern B, and failure to achieve CR post-ASCT.
- The predictive model showed strong discriminative capacity with AUC of 0.753 (training) and 0.857 (validation).
Conclusions:
- A validated prognostic nomogram for transplanted FHR-MM patients was established.
- The model demonstrates robust discriminative capacity for predicting early relapse in MM.
