ArticleBMC cancer2025
The prognostic value of POD24 for multiple myeloma: a comprehensive analysis based on traditional statistics and machine learning.
Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Comparative Molecular Insights and Computational Modeling of Multiple Myeloma and Osteosarcoma.International journal of molecular sciences · 2026Review
- Prognostic impact of early disease progression and early mortality in patients with multiple myeloma: a real-world cohort study.Frontiers in oncology · 2026Article
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Authors and funding
11 authors.
Funding
Abstract
backgroundIn multiple myeloma, progression within 24 months (POD24) is a strong adverse prognostic factor. However, its impact on overall survival (OS) remains underexplored through machine learning.
methodsWe retrospectively collected clinical data from 155 patients and divided them into POD24 and non-POD24 groups. Survival analysis was performed using Kaplan-Meier (KM) curves, univariate Cox regression, and multivariate Cox regression. We further evaluated the effect of these variables on overall survival using ten machine-learning algorithms.
resultsKM and Cox regression analyses revealed significantly poorer OS in the POD24 group (p < 0.001). Among machine learning models, ANN achieved the best performance in the test set. PCA-based visualization showed clear class separation and prediction consistency with original feature distributions. SHAP analysis identified POD24 as the strongest model-derived predictor in this cohort of mortality. Force plots further demonstrated that non-POD24 substantially contributed to lower predicted death risk.
conclusionThis study suggests that POD24 may be associated to the survival outcomes in multiple myeloma using both traditional statistical and machine learning approaches. Our findings highlight the potential value of POD24 in mortality risk prediction and demonstrate the utilization of ANN-based SHAP interpretation in enhancing model transparency.
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