Tissue Outcome Prediction in Patients with Proximal Vessel Occlusion and Mechanical Thrombectomy Using Logistic...

Area of Science:

• Neuroimaging
• Radiology
• Stroke Medicine

Background:

• Perfusion CT aids patient selection for mechanical thrombectomy in acute stroke.
• Current selection often uses simple thresholding of perfusion maps, not fully utilizing data.
• Advanced modeling can potentially improve outcome prediction.

Purpose of the Study:

• To implement and evaluate a multiparametric logistic model for predicting tissue outcome in stroke patients.
• To compare the performance of this model against single-parameter thresholding methods.
• To assess the value of multimodal CT imaging data in outcome prediction.

Main Methods:

• A multiparametric mass-univariate logistic model was developed.
• Data from 405 stroke patients with anterior circulation occlusion undergoing thrombectomy were used.
• Model inputs included multimodal CT (perfusion, angiography, non-contrast), demographics, and clinical data, trained with recanalization and infarct data.

Main Results:

• Perfusion parameters (CBF, CBV, Tmax) were sufficient for tissue outcome prediction.
• The logistic model showed comparable volumetric accuracy but superior topographical accuracy (AUC) versus thresholding.
• Higher spatial accuracy (Dice index) was observed in internal cross-validation.

Conclusions:

• Multiparametric logistic prediction using perfusion CT data offers superior topographical accuracy compared to simple thresholding.
• Perfusion data is more valuable than non-contrast CT, CTA, or clinical information for outcome prediction.
• This approach holds potential for personalized biomarker development in mechanical thrombectomy for acute stroke.