Quantitative angiography prognosis of intracranial aneurysm treatment failure using parametric imaging and distal vessel analysis

Purpose: Data-driven methods based on x-ray angiographic parametric imaging (API) have been successfully used to provide prognosis for intracranial aneurysm (IA) treatment outcome. Previous studies have mainly focused on embolization devices where the flow pattern visualization is in the aneurysm dome; however, this is not possible in IAs treated with endovascular coils due to high x-ray attenuation of the devices. To circumvent this challenge, we propose to investigate whether flow changes in the parent artery distal to the coil-embolized IAs could be used to achieve the same accuracy of surgical outcome prognosis. Methods: Eighty digital subtraction angiography sequences were acquired from patients with IA embolized with coils. Five API parameters were recorded from a region of interest (ROI) placed distal to the IA neck in the main artery. Average API values were recorded and pre-treatment values. A supervised machine learning algorithm was trained to provide a six-month post procedure binary outcome (occluded/not occluded). Receiver operating characteristic (ROC) analysis was used to assess the accuracy of the method. Results: Use of API parameters with data driven methods yielded an area under the ROC curve of 0.77 ±0.11 and accuracy of 78.6%. Single parameter-based analysis yielded accuracies which were suboptimal for clinical acceptance. Conclusions: We determined that data-driven method based on API analysis of flow in the parent artery of IA treated with coils provide clinically acceptable accuracy for the prognosis of six months occlusion outcome.