Use of quantitative angiographic methods with a data-driven model to evaluate reperfusion status (mTICI) during thrombectomy. - Shiraz Bhurwani, Mohammad Mahdi; Snyder, Kenneth V.; Waqas, Muhammad; Mokin, Maxim; Rava, Ryan A.; Podgorsak, Alexander R.; Chin, Felix; Davies, Jason M.; Levy, Elad I.; Siddiqui, Adnan H.; Ionita, Ciprian N. (Neuroradiology, 2021)
Purpose: Intra-procedural assessment of reperfusion during mechanical thrombectomy (MT) for emergent large vessel occlusion (LVO) stroke is traditionally based on subjective evaluation of digital subtraction angiography (DSA). However, semi-quantitative diagnostic tools which encode hemodynamic properties in DSAs, such as angiographic parametric imaging (API), exist and may be used for evaluation of reperfusion during MT. The objective of this study was to use data-driven approaches, such as convolutional neural networks (CNNs) with API maps, to automatically assess reperfusion in the neuro-vasculature during MT procedures based on the modified thrombolysis in cerebral infarction (mTICI) scale. Methods: DSAs from patients undergoing MTs of anterior circulation LVOs were collected, temporally cropped to isolate late arterial and capillary phases, and quantified using API peak height (PH) maps. PH maps were normalized to reduce injection variability. A CNN was developed, trained, and tested to classify PH maps into 2 outcomes (mTICI 0,1,2a/mTICI 2b,2c,3) or 3 outcomes (mTICI 0,1,2a/mTICI 2b/mTICI 2c,3), respectively. Ensembled networks were used to combine information from multiple views (anteroposterior and lateral). Results: The study included 383 DSAs. For the 2-outcome classification, average accuracy was 81.0% (95% CI, 79.0–82.9%), and the area under the receiver operating characteristic curve (AUROC) was 0.86 (0.84–0.88). For the 3-outcome classification, average accuracy was 64.0% (62.0–66.0), and AUROC values were 0.85 (0.83–0.87), 0.74 (0.71–0.77), and 0.78 (0.76–0.81) for the mTICI 0,1,2a, mTICI 2b, and mTICI 2c,3 classes, respectively. Conclusion: This study demonstrated the feasibility of using hemodynamic information in API maps with data-driven models to autonomously assess intra-procedural reperfusion during MT.