Predicting treatment outcome of intracranial aneurysms using angiographic parametric imaging and recurrent neural networks

Abstract

Angiographic Parametric Imaging (API) is a tool based on the parametrization of Time-Density Curves (TDCs) from Digital Subtraction Angiography (DSA). Parameters derived from the TDCs correlate moderately with hemodynamics, yet underuse the hemodynamic information encoded in a TDC. To determine whether better diagnoses can be made through a more complete utilization of the information in the TDCs, we implemented an analysis using Recurrent Neural Networks (RNNs). These are a class of neural networks that analyze and make predictions using time sequences such as the TDCs. We investigated the feasibility of using RNNs to make treatment outcome predictions using TDCs obtained from angiograms of Intracranial Aneurysms (IAs) treated with Pipeline Embolization Devices (PED). Six-month follow-up angiograms were collected to create binary labels regarding treatment outcome (occluded/un-occluded). API parameters obtained were Mean Transit Time, Time to Peak, Time to Arrival, and Peak Height. Parameters were used to simulate TDCs which were normalized to account for variability between interventions. An RNN was trained and tested to predict IA treatment outcome. A 20-fold Monte Carlo Cross Validation was conducted to evaluate robustness of the RNN. The RNN predicted occlusion outcome of IAs with an average accuracy of 74.4% (95% CI, 72.6%-76.1%) and 65.6% (63.4%- 67.2%) and average area under the receiver operating characteristic curve of 0.73 (0.70-0.76) and 0.56 (0.51-0.61) for normalized and un-normalized sub-groups respectively. This study proves the feasibility of using RNNs to predict treatment outcome of IAs treated with a PED using TDCs simulated from temporal features obtained through API.