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  • Feasibility study for use of angiographic parametric imaging and deep neural networks for intracranial aneurysm occlusion prediction

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Basic Science
Original research
Feasibility study for use of angiographic parametric imaging and deep neural networks for intracranial aneurysm occlusion prediction
  1. Mohammad Mahdi Shiraz Bhurwani1,2,
  2. Muhammad Waqas1,3,
  3. Alexander R Podgorsak1,2,
  4. Kyle A Williams1,2,
  5. Jason M Davies1,3,4,
  6. Kenneth Snyder1,3,
  7. Elad Levy1,3,
  8. Adnan Siddiqui1,3,
  9. Ciprian N Ionita1,2,3
  1. 1 Canon Stroke and Vascular Research Center, Buffalo, New York, USA
  2. 2 Biomedical Engineering, University at Buffalo - The State University of New York, Buffalo, New York, USA
  3. 3 Neurosurgery, University at Buffalo School of Medicine and Biomedical Sciences, Buffalo, New York, USA
  4. 4 Biomedical Informatics, University at Buffalo,The State University of New York, Buffalo, New York, United States
  1. Correspondence to Dr Ciprian N Ionita, Biomedical Engineering, University at Buffalo, State University of New York, Buffalo, NY 14203, USA; cnionita{at}buffalo.edu

Abstract

Background Angiographic parametric imaging (API), based on digital subtraction angiography (DSA), is a quantitative imaging tool that may be used to extract contrast flow parameters related to hemodynamic conditions in abnormal pathologies such as intracranial aneurysms (IAs).

Objective To investigate the feasibility of using deep neural networks (DNNs) and API to predict IA occlusion using pre- and post-intervention DSAs.

Methods We analyzed DSA images of IAs pre- and post-treatment to extract API parameters in the IA dome and the corresponding main artery (un-normalized data). We implemented a two-step correction to account for injection variability (normalized data) and projection foreshortening (relative data). A DNN was trained to predict a binary IA occlusion outcome: occluded/unoccluded. Network performance was assessed with area under the receiver operating characteristic curve (AUROC) and classification accuracy. To evaluate the effect of the proposed corrections, prediction accuracy analysis was performed after each normalization step.

Results The study included 190 IAs. The mean and median duration between treatment and follow-up was 9.8 and 8.0 months, respectively. For the un-normalized, normalized, and relative subgroups, the DNN average prediction accuracies for IA occlusion were 62.5% (95% CI 60.5% to 64.4%), 70.8% (95% CI 68.2% to 73.4%), and 77.9% (95% CI 76.2% to 79.6%). The average AUROCs for the same subgroups were 0.48 (0.44–0.52), 0.67 (0.61–0.73), and 0.77 (0.74–0.80).

Conclusions The study demonstrated the feasibility of using API and DNNs to predict IA occlusion using only pre- and post-intervention angiographic information.

  • aneurysm
  • flow diverter
  • angiography
  • intervention
  • blood flow

https://doi.org/10.1136/neurintsurg-2019-015544

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    Footnotes

    • Contributors MMSB, ARP, and CNI conceived and designed the research. JMD, EL, KS, and AS performed all the clinical procedures.MMSB, MW and CNI collected the data. MMSB, CNI, and KAW analyzed the data. MMSB performed the statistical analysis. CNI handled funding and supervision. MMSB drafted the manuscript. All authors made critical revisions to the manuscript and reviewed the final version.

    • Funding This project was partially supported by Canon Medical Systems and the James H. Cummings Foundation.

    • Competing interests CNI: Equipment grant from Canon Medical Systems, support from the Cummings Foundation, NIH R21 grant. JMD: Research grant: National Center for Advancing Translational Sciences of the National Institutes of Health under award number KL2TR001413 to the University at Buffalo. Speakers’ bureau: Penumbra; Honoraria: Neurotrauma Science. Shareholder/ownership interests: RIST Neurovascular. KS: Consulting and teaching for Canon Medical Systems Corporation, Penumbra, Medtronic, and Jacobs Institute. Co-Founder: Neurovascular Diagnostics. EL: Shareholder/Ownership interests: NeXtGen Biologics, RAPID Medical, Claret Medical, Cognition Medical, Imperative Care (formerly the Stroke Project), Rebound Therapeutics, StimMed, Three Rivers Medical. National Principal Investigator/Steering Committees: Medtronic (merged with Covidien Neurovascular) SWIFT Prime and SWIFT Direct Trials. Honoraria: Medtronic (training and lectures). Consultant: Claret Medical, GLG Consulting, Guidepoint Global, Imperative Care, Medtronic, Rebound, StimMed. Advisory Board: Stryker (AIS Clinical Advisory Board), NeXtGen Biologics, MEDX, Cognition Medical, Endostream Medical. Site Principal Investigator: CONFIDENCE study (MicroVention), STRATIS Study—Sub I (Medtronic). AS: Research grant: NIH/NINDS 1R01NS091075 as a co-investigator for “Virtual Intervention of Intracranial Aneurysms”. Financial interest/investor/stock options/ownership: Amnis Therapeutics, Apama Medical, Blink TBI, Buffalo Technology Partners, Cardinal Consultants, Cerebrotech Medical Systems, Cognition Medical, Endostream Medical, Imperative Care, International Medical Distribution Partners, Neurovascular Diagnostics, Q’Apel Medical, Rebound Therapeutics Corp, Rist Neurovascular, Serenity Medical, Silk Road Medical, StimMed, Synchron, Three Rivers Medical, Viseon Spine. Consultant/advisory board: Amnis Therapeutics, Boston Scientific, Canon Medical Systems USA, Cerebrotech Medical Systems, Cerenovus, Corindus, Endostream Medical, Guidepoint Global Consulting, Imperative Care, Integra LifeSciences Corp, Medtronic, MicroVention, Northwest University–DSMB Chair for HEAT Trial, Penumbra, Q’Apel Medical, Rapid Medical, Rebound Therapeutics Corp., Serenity Medical, Silk Road Medical, StimMed, Stryker, Three Rivers Medical, VasSol, W.L. Gore & Associates. Principal investigator/steering comment of the following trials: Cerenovus LARGE and ARISE II; Medtronic SWIFT PRIME and SWIFT DIRECT; MicroVention FRED & CONFIDENCE; MUSC POSITIVE; and Penumbra 3D Separator, COMPASS, and INVEST.

    • Patient consent for publication Not required.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Data availability statement Data are available upon reasonable request.