Multi-task Nomogram Model for Predicting Rebleeding and Survival Outcomes in Cerebral Contusion

This study aimed to develop a multi-task nomogram model based on CT imaging to accurately predict rebleeding and survival prognosis in patients with cerebral contusion, thereby enhancing early risk assessment and personalized treatment strategies. A retrospective cohort of 427 patients with CT-confirmed cerebral contusions was analyzed. The study integrated clinical data (e.g., Glasgow Coma Scale scores), radiomics features extracted from CT images, and deep transfer learning (DTL) outputs using a DenseNet121 architecture. A multi-task nomogram model was constructed to concurrently predict rebleeding risk and survival outcomes. Model performance was evaluated using ROC curves, calibration plots, decision curve analysis (DCA), and Harrell's concordance index (C-index). Interpretability was examined using Gradient-weighted Class Activation Mapping (Grad-CAM). The nomogram model exhibited excellent predictive performance, achieving areas under the curve (AUC) values of 0.973 for the training cohort and 0.959 for the testing cohort regarding rebleeding prediction. It also showed the highest prognostic accuracy (C-index 0.857, p < 0.0001). The model highlighted the critical role of GCS scores, particularly in moderate TBI cases (GCS 6-8), where timely intervention improved survival rates by 20%. Grad-CAM visualization confirmed the model's ability to localize hemorrhage regions accurately. A multi-task nomogram model, combining clinical, radiomic, and DTL features, provides a robust tool for early prediction of rebleeding and survival in cerebral contusion patients. Its integration with GCS scores facilitates targeted interventions, especially for moderate cerebral contusion cases, underscoring its clinical utility in improving outcomes.