Roman D Price1,2, Mohammad Mahdi Shiraz Bhurwani1,2, Kelsey N Sommer1,2,3, Andrei Monteiro2,4, Ammad A Baig2,4, Jason M Davies2,4,3,5, Adnan H Siddiqui2,4,5, Ciprian N Ionita1,2,4,3. 1. Department of Biomedical Engineering, University at Buffalo, Buffalo NY 14228. 2. Canon Stroke and Vascular Research Center, Buffalo, NY 14203. 3. QAS.AI Incorporated, Buffalo NY 14203. 4. University at Buffalo Neurosurgery, University at Buffalo Jacobs School of Medicine, Buffalo NY 14228. 5. University Dept. of Biomedical Informatics, University at Buffalo, Buffalo, NY 14214.
Abstract
Purpose: Subarachnoid Hemorrhage (SAH) is a lethal hemorrhagic stroke that account for 25% of cerebrovascular deaths. As a result of the initial bleed, a chain of physiological events are initiated which may lead to Delayed Cerebral Ischemia (DCI). As of now we have no diagnostic capability to identify patients which may present DCI a few weeks after initial presentation. We propose to investigate whether a data driven approach using angiographic parametric imaging (API) may predict occurrence of the DCI. Materials and Methods: Digital Subtraction Angiographic (DSA) sequences from 125 SAH patients were used retrospectively to perform API assessment of the entire brain hemisphere where the hemorrhage was detected. Four Regions of Interests (ROIs) were placed to extract five average API biomarkers in the lateral and AP DSAs. Data driven analysis using Logistic Regression was performed for various API parameters and ROIs to find the optimal configuration to maximize the prognosis accuracy. Each model performance was evaluated using area under the curve of the receiver operator characteristic (AUROC). Results: Data driven approach with API has a 60% accuracy predicting DCI occurrence. We determined that location of the ROI for extraction of the API parameters is very important for the data driven model performance. Normalizing the values using the inlet velocities for each patient yield higher and more consistent results. Single API biomarkers models had poor prediction accuracies, barely better than chance. Conclusions: This effectiveness exploratory study demonstrates for the first time, that prognosis of the DCI in SAH patients, is feasible and warrants a more in-depth investigation.
Purpose: Subarachnoid Hemorrhage (SAH) is a lethal hemorrhagic stroke that account for 25% of cerebrovascular deaths. As a result of the initial bleed, a chain of physiological events are initiated which may lead to Delayed Cerebral Ischemia (DCI). As of now we have no diagnostic capability to identify patients which may present DCI a few weeks after initial presentation. We propose to investigate whether a data driven approach using angiographic parametric imaging (API) may predict occurrence of the DCI. Materials and Methods: Digital Subtraction Angiographic (DSA) sequences from 125 SAH patients were used retrospectively to perform API assessment of the entire brain hemisphere where the hemorrhage was detected. Four Regions of Interests (ROIs) were placed to extract five average API biomarkers in the lateral and AP DSAs. Data driven analysis using Logistic Regression was performed for various API parameters and ROIs to find the optimal configuration to maximize the prognosis accuracy. Each model performance was evaluated using area under the curve of the receiver operator characteristic (AUROC). Results: Data driven approach with API has a 60% accuracy predicting DCI occurrence. We determined that location of the ROI for extraction of the API parameters is very important for the data driven model performance. Normalizing the values using the inlet velocities for each patient yield higher and more consistent results. Single API biomarkers models had poor prediction accuracies, barely better than chance. Conclusions: This effectiveness exploratory study demonstrates for the first time, that prognosis of the DCI in SAH patients, is feasible and warrants a more in-depth investigation.
Authors: Alexander R Podgorsak; Ryan A Rava; Mohammad Mahdi Shiraz Bhurwani; Anusha R Chandra; Jason M Davies; Adnan H Siddiqui; Ciprian N Ionita Journal: J Neurointerv Surg Date: 2019-08-23 Impact factor: 5.836
Authors: Marc Buehler; Jordan M Slagowski; Charles A Mistretta; Charles M Strother; Michael A Speidel Journal: Proc SPIE Int Soc Opt Eng Date: 2017-03-09
Authors: G Boulouis; C Rodriguez-Régent; E C Rasolonjatovo; W Ben Hassen; D Trystram; M Edjlali-Goujon; J-F Meder; C Oppenheim; O Naggara Journal: Rev Neurol (Paris) Date: 2017-06-03 Impact factor: 2.607
Authors: Ryan A Rava; Maxim Mokin; Kenneth V Snyder; Muhammad Waqas; Adnan H Siddiqui; Jason M Davies; Elad I Levy; Ciprian N Ionita Journal: J Med Imaging (Bellingham) Date: 2020-02-11
Authors: Joseph R Geraghty; Melissa N Lara-Angulo; Milen Spegar; Jenna Reeh; Fernando D Testai Journal: J Stroke Cerebrovasc Dis Date: 2020-06-20 Impact factor: 2.136
Authors: Mohammad Mahdi Shiraz Bhurwani; Muhammad Waqas; Alexander R Podgorsak; Kyle A Williams; Jason M Davies; Kenneth Snyder; Elad Levy; Adnan Siddiqui; Ciprian N Ionita Journal: J Neurointerv Surg Date: 2019-12-10 Impact factor: 5.836