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Updated: Sep 7, 2026
Constructing and Visualizing Models using Mime-based Machine-learning Framework
Published on: July 22, 2025
Construction and Validation of a Progression-Free Survival Prediction Model for Newly Diagnosed Multiple Myeloma
Yuexuan Rao1,2, Shiyou Li1,2, Aoyang Yu2
1Department of Hematology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu Province, China.
Objective:
To investigate the clinical characteristics of newly diagnosed multiple myeloma (NDMM) patients with 1q21 gain/amplification (1q21+), construct and validate a progression-free survival (PFS) prediction model, and explore the clinical implications of model-based risk stratification.
Methods:
We retrospectively analyzed 186 newly diagnosed multiple myeloma patients with 1q21+ treated between 2018 and 2024. Patients were randomly assigned at a 7:3 ratio to a training cohort (n = 131) and an internal split-sample validation cohort (n = 55). Independent prognostic factors for PFS were identified using multivariate Cox regression to construct a nomogram (the HLBP model). Model performance was evaluated through bootstrap resampling, time-dependent receiver operating characteristic (ROC) curves, calibration plots, and Kaplan-Meier survival analysis. Furthermore, the model's predictive accuracy was compared with conventional staging systems and established prognostic models. Finally, risk-stratified exploratory analyses were conducted to assess treatment-associated outcomes across different risk groups.
Results:
Higher hemoglobin was protective, whereas elevated lactate dehydrogenase, increased β2-microglobulin, and TP53 deletion were adverse prognostic factors. The HLBP model yielded AUCs of 0.805, 0.885, and 0.847 at 6, 12, and 24 months in the training cohort, and 0.747, 0.827, and 0.712 in the internal split-sample validation cohort. Patients stratified into high- and low-risk groups showed significantly different PFS outcomes. Risk-stratified analyses suggested heterogeneous prognostic patterns associated with induction treatment regimens and autologous stem cell transplantation across risk groups. Overall, the HLBP model demonstrated superior predictive performance compared with existing staging systems and previously reported prognostic models.
Conclusion:
The HLBP model showed promising preliminary performance for predicting PFS in patients with 1q21+ NDMM and may provide an exploratory framework for individualized risk stratification.
