Mortality Prediction in Diffuse Large B-Cell Lymphoma Using Supervised Machine Learning Models-A Retrospective Study

Background/Objectives: Diffuse large B-cell lymphoma (DLBCL) is a biologically and clinically heterogeneous malignancy with variable outcomes. Accurate risk prediction at diagnosis remains essential to guide treatment and follow-up strategies. In this retrospective study we aimed to assess the performance of multiple modeling approaches to predict death by 26 months of follow-up in patients with DLBCL using data available in the diagnostic stage. Methods: In this study we included 412 patients with DLBCL who were evaluated, treated, and followed-up at the Regional Institute of Oncology in Iasi, Romania, between 2015 and 2023. Clinical and paraclinical data determined at baseline examination was used to train and test six machine learning models (logistic regression, random forest-RF, support vector machine with a radial-basis kernel-SVM-RBF, multilayer perceptron neural network-MLP, random survival forest-RSF, and extreme gradient boosting-XGBoost) and to compare their performance to the Cox proportional hazards model. Results: Among the models, RF achieved the highest discrimination (AUC = 0.9060), with balanced performance (accuracy = 0.833; F1 = 0.902), followed by XGBoost (AUC = 0.8335) and MLP (AUC = 0.7861; accuracy = 0.849). RF and logistic regression demonstrated the best calibration (Brier = 0.360 and 0.377). The Cox model achieved moderate discrimination (time-dependent AUC = 0.5561; C-index = 0.55). Conclusions: Our findings align with contemporary reports showing that machine learning frameworks can outperform classical prediction approaches.