A web-based radiomics nomogram combining MRI features and clinical data for predicting 30-day progression of acute...

To develop and validate an integrated nomogram combining MRI radiomics features with clinical variables for predicting 30-day progression risk in patients with acute ischemic stroke (AIS), and to deploy a web-based visualization tool for clinical application.

This retrospective two-center study included 254 AIS patients. Radiomics features were extracted from DWI using MaZda, and key features were selected by LASSO logistic regression to build a radiomics signature. Clinical predictors were identified using logistic regression. Radiomics, clinical, and combined models were developed and evaluated using area under the curve (AUC), net reclassification index (NRI), and integrated discrimination improvement (IDI), calibration analysis, and decision curve analysis (DCA). Optimal cutoffs were determined in the training cohort using the Youden index and then directly applied to the external validation cohort without re-optimization. The corresponding training-derived thresholds were -1.2023 for the clinical model, -1.0368 for the radiomics model, and -2.3639 for the combined model. Bootstrap resampling (1,000 repetitions) was used for internal validation of the combined model.

Eight optimal radiomics features were selected from 300 extracted features. Multivariate analysis identified four independent predictors: Radiomics score (Radscore) (OR = 2.23, 95% CI: 1.28-5.11), National Institutes of Health Stroke Scale (NIHSS) score (OR = 1.57, 95% CI: 1.25-2.14), type 2 diabetes mellitus (OR = 9.28, 95% CI: 2.02-56.03), and monocytes (MO) (OR = 1.08, 95% CI: 1.02-1.16). The combined model showed favorable discriminative performance with AUCs of 0.945 (95%CI: 0.903-0.987) and 0.904 (95%CI: 0.854-0.954) in the training and validation cohorts, respectively. When the training-derived cutoff of -2.3639 was applied to the validation cohort, the combined model achieved 93.75% sensitivity, 73.95% specificity, and 83.85% balanced accuracy. Bootstrap internal validation yielded an optimism-corrected C-index of 0.912 and a corrected calibration slope of 0.769. The combined model outperformed the clinical-only and radiomics-only models, although the external validation should still be interpreted as an initial cross-center assessment.

The integrated radiomics-clinical nomogram incorporating Radscore, NIHSS score, type 2 diabetes mellitus (T2DM), and MO showed potential for predicting 30-day progression risk in patients with AIS. This web-based tool may support early risk stratification, but further large-scale prospective validation is required before routine clinical application.