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High-probability grants
According to our matching algorithm, Peng R. Chen is the likely recipient of the following grants.
| Years |
Recipients |
Code |
Title / Keywords |
Matching
score |
| 2020 |
Chen, Peng Roc
Lee, Tom |
R41Activity Code Description:
To support cooperative R&D projects between small business concerns and research institutions, limited in time and amount, to establish the technical merit and feasibility of ideas that have potential for commercialization. Awards are made to small business concerns only. |
Non-Invasive Detection of Cerebral Aneurysm Recurrence After Endovascular Treatment Using Automated Image Processing @ Medical Innovators Company, Llc
PROJECT SUMMARY 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. CONFIDENTIAL- UTHEALTH
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