Functional Outcome Prediction Across Multiple Timescales Intracerebral Hemorrhage Using a Radiomics Model

Abstract Purpose To establish an outcome prediction model for multiple timescales itracerebral hemorrhage (ICH). Materials and methods ICH patients with an onset-to-imaging time (OIT) of less than 72 h were retrospectively collected. Patients were divided into three groups according to their OIT. Group 1–3: OIT < 6h, 6 ≤ OIT<24h, 24 ≤ OIT<72h. The first preoperative review in Group 1 within 72 h were recorded for Group 4. A binary logistic regression classifier was used to construct outcome prediction models for each group. The predictive performance of each group’s model was compared with the application of the Group 1 model across different groups to explore its accuracy and applicability in predicting the outcomes for patients in various groups. Results A total of 2,136 patients with ICH were included in the study. In the training set, the AUC value obtained by directly applying the group 1 radiomics model to group 2 patients was 0.773. The AUC value for the direct application of the Group 1 combined model to Group 3 patients was 0.811. The AUC for applying the Group 1 radiomics model directly to Group 4 patients was 0.779. The AUC of the radiomics model, constructed by combining the key radiomics features of Groups 1 and 4, was 0.788. The AUC of the independent radiomics model for Group 4 was 0.815. Conclusion The radiomics and combined models for predicting outcomes in ICH patients with OIT < 6 h can be applied to all patients with OIT < 72 h, allowing for early and accurate outcome prediction across multiple timescales.