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Proceedings Paper

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

Paper Details

Date Published: 16 March 2020
PDF: 10 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113142O (16 March 2020); doi: 10.1117/12.2548635
Show Author Affiliations
Mohammad Mahdi Shiraz Bhurwani, Univ. at Buffalo (United States)
Canon Stroke and Vascular Research Ctr. (United States)
Mohammad Waqas, Canon Stroke and Vascular Research Ctr. (United States)
Univ. at Buffalo (United States)
Kyle A. Williams, Univ. at Buffalo (United States)
Canon Stroke and Vascular Research Ctr. (United States)
Ryan A. Rava, Univ. at Buffalo (United States)
Canon Stroke and Vascular Research Ctr. (United States)
Alexander R. Podgorsak, Univ. at Buffalo (United States)
Canon Stroke and Vascular Research Ctr. (United States)
Kenneth V. Snyder, Canon Stroke and Vascular Research Ctr. (United States)
Univ. at Buffalo (United States)
Elad I. Levy, Canon Stroke and Vascular Research Ctr. (United States)
Univ. at Buffalo (United States)
Jason M. Davies, Canon Stroke and Vascular Research Ctr. (United States)
Univ. at Buffalo (United States)
Adnan H. Siddiqui, Canon Stroke and Vascular Research Ctr. (United States)
Univ. at Buffalo (United States)
Ciprian N. Ionita, Univ. at Buffalo (United States)
Canon Stroke and Vascular Research Ctr. (United States)
(United States)


Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, Editor(s)

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