Prediction of functional outcome after mechanical thrombectomy using radiomics and deep learning features derived
Kai Shang1, Xiaoxiao Xu1, Lifang Ye1
1Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Purpose:
Although mechanical thrombectomy (MT) achieves high recanalization rates in acute ischemic stroke (AIS), functional outcomes remain highly inconsistent. We aimed to evaluate the effectiveness of radiomics and deep learning features (DLFs) extracted from both intra-thrombus and peri-thrombus regions on baseline CT in predicting prognosis following MT.
Materials And Methods:
This retrospective, multicenter study included 471 AIS patients who underwent MT. Patients from Center A (n = 315) constituted the training cohort, while those from Center B and C (n = 156) served as an external test cohort. Thrombus regions were manually segmented, with a subsequent 1 mm expansion to define the peri-thrombus regions. A total of 428 radiomics features and 128 DLFs were extracted from non-contrast CT (NCCT) and CTA images. 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.
Results:
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.
Conclusion:
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.