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Non-invasive Detection of Cerebral Aneurysm Recurrence after Endovascular Treatment Using Automated Image Processing

Award Information
Agency: Department of Health and Human Services
Branch: National Institutes of Health
Contract: 1R41NS115253-01
Agency Tracking Number: R41NS115253
Amount: $224,981.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: 101
Solicitation Number: PA18-575
Solicitation Year: 2018
Award Year: 2020
Award Start Date (Proposal Award Date): 2020-01-01
Award End Date (Contract End Date): 2020-12-31
Small Business Information
Houston, TX 77042-1411
United States
DUNS: 080479676
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 (713) 248-0311
Business Contact
Phone: (713) 248-0311
Research Institution
HOUSTON, TX 77030-5400
United States

 Nonprofit college or university

Hemorrhage due to cerebral aneurysm rupture is a devastating condition with high mortality. For the more than
30,000 patients in the US who are diagnosed annually with an aneurysm, treatment consists of preventing
rupture, and increasingly relies of endovascular techniques. However, treatment durability is unknown with
recurrence estimated at 16-40% and the re-treatment of 10-20%. The current gold standard to ensure aneurysm
obliteration is catheter-based digital subtraction angiography (DSA), an invasive method with significant side
effects. Here, we propose an alternative that uses simple skull x-rays and automated image processing to identify
patients who are high likelihood of recurrence and select them for further investigation. Development of this
technique is the result of a collaboration between the Medical Innovations Company (MIC) and the UTHealth
and Memorial Hermann Hospital (UTH/MHH). We plan to test the hypothesis that aneurysm recurrence can
be detected using standard skull x-rays. In Aim 1, we will develop an automated computer algorithm that
detects aneurysm recurrence after coiling. Data from an established cohort of patients treated at UTH/MHH.
Automated computer analysis of the x-rays (at initial treatment and 6-month follow) will predict aneurysm
recurrence using coil morphometry (size, shape, orientation). The algorithm will be trained by comparing it to the
gold standard for follow up (DSA). In Aim 2, preliminary validation of algorithm performance will be tested in a
novel dataset. A validation dataset (n=150) of similar patients treated with the same protocol as the training
dataset will be processed using the automated algorithm. The performance of the algorithm will be assessed
using receiver operator characteristics to determine optimal sensitivity/specificity. If successful, such an
approach could stratify risk in patients and determine which should undergo angiography. Reducing utilization
of angiography will significantly reduce complications and medical cost at an immense benefit to the public. This
Phase I STTR grant will allow for algorithm development and testing prior to a Phase II application and broader
clinical trials. The partnership between MIC and UTH/MHH combines experience commercializing medical
software with clinical neurosurgery.
Cerebral aneurysm rupture is a dreaded medical condition with high mortality; and while significant advances
have been made in endovascular treatment and coiling of aneuryms, there is no consensus on long term clinical
follow up. The academic partner has developed an innovative and low cost alternative to current strategies to
monitor for aneurysm recurrence in patients treated with endovascular coiling using automated analysis of skull
x-rays. The small business partner has assisted with optimization and validation of this algorithm and will provide
support for a subsequent clinical trial.

* Information listed above is at the time of submission. *

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