Supplementary file 1_Prediction of functional outcome after mechanical thrombectomy using radiomics and deep learning features derived from intra-thrombus and peri-thrombus regions.docx

After feature selection, ten machine learning classifiers were employed to build prediction models. Combined models were developed based on two strategies including early feature-level fusion and late decision-level fusion. Model performance was evaluated using ROC analysis, DeLong test, NRI and IDI.

Subgroup analyses were also performed.</p>Results<p>A total of 191 patients (40.6%) achieved a favorable outcome (90-day mRS ≤ 2). In the test cohort, the late-fusion combined model achieved the highest discriminative performance (AUC = 0.836, 95% CI: 0.769–0.891). It significantly outperformed both the peri-thrombus model (AUC = 0.758, p = 0.005) and the early-fusion combined model (AUC = 0.763, p = 0.009).

Compared with the intra-thrombus model, the late-fusion combined model also demonstrated improved reclassification, with an NRI of 0.935 and an IDI of 0.278. Subgroup analyses showed consistent discriminative performance of the late-fusion combined model across the prespecified subgroups.</p>Conclusion<p>The late-fusion combined model, integrating CT-derived radiomic and DLFs from both intra-thrombus and peri-thrombus regions, may provide an effective approach for predicting 90-day functional outcome after mechanical thrombectomy.</p>