Key result
Clot radiomics fusion model predicts post-thrombectomy neurological improvement with AUC ~0.89.
Why the study?
Does a fusion model integrating venous-phase clot radiomics and arterial-level collateral scores predict neurological improvement after mechanical thrombectomy in LVO-AIS patients?
Observational (n=110)
Does a fusion model integrating venous-phase clot radiomics and arterial-level collateral scores predict neurological improvement after mechanical thrombectomy in LVO-AIS patients?
Effect estimate: AUC 0.889
Combining venous-phase clot radiomics with arterial-level collateral scores and clinical parameters yields high predictive accuracy for neurological improvement after mechanical thrombectomy in acute ischemic stroke.
BACKGROUND: Mechanical thrombectomy (MT) has been recognized as a groundbreaking intervention for acute ischemic stroke (AIS) resulting from large vessel occlusion (LVO). Traditional imaging parameters frequently fall short in synthetically encapsulating the heterogeneity of thrombi and the dynamics of collateral circulation. AIM: This study seeks to investigate the integration of venous-phase clot radiomics features with arterial-level collateral scores obtained from color-coded multi-phase CT angiography (mCTA) to predict neurological improvement (NI) following MT in LVO-AIS patients. METHODS: A retrospective analysis was conducted on a series of adult patients with LVO-AIS who underwent mCTA followed by MT. Radiomic features were extracted from the peak-venous and late-venous phases of the mCTA. Subsequently, a machine learning algorithm was employed to develop radiomic models. The regional leptomeningeal collateral (rLMC) score, derived from color-coded mCTA maps, was documented to assess arterial-level collateral status. Another fusion model integrating clinical, collateral, and radiomics data was constructed using logistic regression to predict NI status. RESULTS: The study included 110 AIS patients in which the rLMC score was significantly higher in the NI group compared to the non-NI group (P<0.001). The clot-based radiomics model exhibited good predictive performance, with AUC values of 0.986 (training set) and 0.831 (test set) for the peak-venous phase. The fusion model based on peak venous phase data, incorporating clinical parameters, rLMC score, and radiomics features, showed superior predictive accuracy (AUC: 0.992 in training set, 0.889 in test set). Corresponding DCA indicate that the combined model demonstrates the optimal potential clinical benefits. CONCLUSION: The integration of venous-phase clot radiomics features with arterial-level collateral scores and clinical parameters effectively predicts NI after MT in AIS patients.
No takes yet. Share an insight, caveat, or question.
Zhang et al. (2025) conducted an observational in Acute ischemic stroke (AIS) resulting from large vessel occlusion (LVO) (n=110). Fusion model integrating venous-phase clot radiomics, arterial-level collateral scores, and clinical parameters was evaluated on Neurological improvement following mechanical thrombectomy (AUC 0.889). A fusion model integrating venous-phase clot radiomics, arterial-level collateral scores, and clinical parameters accurately predicted neurological improvement after thrombectomy (test set AUC 0.889).
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: