JMIR medical informatics · 2026
Integrated Clinical-Molecular Risk Stratification in Diffuse Large B-Cell Lymphoma: Machine Learning Survival Analysis.
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Zhao Jin, Wen Xiaolian, Ma Li, Su Liping
Аннотация
BACKGROUND: The clinical outcomes of diffuse large B-cell lymphoma (DLBCL) are highly heterogeneous. While clinical indices like the international prognostic index (IPI) are widely used, their predictive accuracy remains limited. The integration of molecular features with clinical characteristics holds promise for developing more precise prognostic models to improve risk stratification and personalize treatment strategies.
OBJECTIVE: This study aimed to systematically identify key factors influencing overall survival (OS) and relapse in patients with DLBCL by leveraging publicly available transcriptomic data and clinical information. The goal was to construct and validate a high-precision risk-prediction model by using machine learning methods to aid in individualized clinical decision-making.
METHODS: We curated clinical and transcriptomic data from the GSE31312 cohort. A baseline clinical model was first constructed using multivariate Cox regression. Key genes associated with prognosis were identified through univariate Cox and survival analyses. Subsequently, 3 machine learning survival models, namely, fast survival support vector machine (FastSurvivalSVM), gradient boosting survival analysis (GBSurvival), and random survival forest (RSF), were trained and evaluated using 5-fold cross-validation. The interpretability of the optimal model was further elucidated using Shapley Additive Explanations (SHAP) methodology.
RESULTS: The baseline clinical model confirmed age, elevated lactate dehydrogenase, Eastern Cooperative Oncology Group score, Ann Arbor stage, and B symptoms as independent risk factors for OS and relapse-free survival, with a C-index of 0.65-0.67. At the molecular level, genes such as PSMG4 and CRY1 were significantly associated with poor OS, while TMEM182 and SPIRE1 were prominent in relapse prediction. Among the machine learning models, FastSurvivalSVM demonstrated the best overall performance, achieving an area under the curve of 0.791 for 1-year OS prediction and 0.774 for 1-year relapse prediction. SHAP analysis revealed that both clinical (eg, IPI and age) and molecular (eg, PSMG4 and SPIRE1) features were critical drivers of the model's predictions.
CONCLUSIONS: This study successfully developed a multidimensional risk prediction model that integrates clinical and molecular characteristics for DLBCL. The FastSurvivalSVM model showed superior performance in predicting mortality and relapse risks. The interpretability analysis uncovered key prognostic factors, providing a valuable tool for personalized risk management and new theoretical insights for future mechanistic research.
OBJECTIVE: This study aimed to systematically identify key factors influencing overall survival (OS) and relapse in patients with DLBCL by leveraging publicly available transcriptomic data and clinical information. The goal was to construct and validate a high-precision risk-prediction model by using machine learning methods to aid in individualized clinical decision-making.
METHODS: We curated clinical and transcriptomic data from the GSE31312 cohort. A baseline clinical model was first constructed using multivariate Cox regression. Key genes associated with prognosis were identified through univariate Cox and survival analyses. Subsequently, 3 machine learning survival models, namely, fast survival support vector machine (FastSurvivalSVM), gradient boosting survival analysis (GBSurvival), and random survival forest (RSF), were trained and evaluated using 5-fold cross-validation. The interpretability of the optimal model was further elucidated using Shapley Additive Explanations (SHAP) methodology.
RESULTS: The baseline clinical model confirmed age, elevated lactate dehydrogenase, Eastern Cooperative Oncology Group score, Ann Arbor stage, and B symptoms as independent risk factors for OS and relapse-free survival, with a C-index of 0.65-0.67. At the molecular level, genes such as PSMG4 and CRY1 were significantly associated with poor OS, while TMEM182 and SPIRE1 were prominent in relapse prediction. Among the machine learning models, FastSurvivalSVM demonstrated the best overall performance, achieving an area under the curve of 0.791 for 1-year OS prediction and 0.774 for 1-year relapse prediction. SHAP analysis revealed that both clinical (eg, IPI and age) and molecular (eg, PSMG4 and SPIRE1) features were critical drivers of the model's predictions.
CONCLUSIONS: This study successfully developed a multidimensional risk prediction model that integrates clinical and molecular characteristics for DLBCL. The FastSurvivalSVM model showed superior performance in predicting mortality and relapse risks. The interpretability analysis uncovered key prognostic factors, providing a valuable tool for personalized risk management and new theoretical insights for future mechanistic research.