Initial investigation of predicting hematoma expansion for intracerebral hemorrhage using imaging biomarkers and machine learning

Dennis Swetz; Samantha E. Seymour; Ryan A. Rava; Mohammad Mahdi Shiraz Bhurwani; Andre Monteiro; Ammad A. Baig; Muhammad Waqas; Kenneth V. Snyder; Elad I. Levy; Jason M. Davies; Adnan H. Siddiqui; Ciprian N. Ionita

doi:10.1117/12.2610672

4 April 2022 Initial investigation of predicting hematoma expansion for intracerebral hemorrhage using imaging biomarkers and machine learning

Dennis Swetz, Samantha E. Seymour, Ryan A. Rava, Mohammad Mahdi Shiraz Bhurwani, Andre Monteiro, Ammad A. Baig, Muhammad Waqas, Kenneth V. Snyder, Elad I. Levy, Jason M. Davies, Adnan H. Siddiqui, Ciprian N. Ionita

Author Affiliations +

Proceedings Volume 12036, Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging; 120360B (2022) https://doi.org/10.1117/12.2610672
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

Purpose: Intracerebral Hemorrhage (ICH) is one of the most devastating types of strokes with mortality and morbidity rates ranging from about 51%-65% one year after diagnosis. Early hematoma expansion (HE) is a known cause of worsening neurological status of ICH patients. The goal of this study was to investigate whether non-contrast computed tomography imaging biomarkers (NCCT-IB) acquired at initial presentation can predict ICH growth in the acute stage. Materials and Methods: We retrospectively collected NCCT data from 326 patients with acute (<6 hours) ICH. Four NCCT-IBs (blending region, dark hole, island, and edema) were identified for each hematoma, respectively. HE status was recorded based on the clinical observation reported in the patient chart. Supervised machine learning models were developed, trained, and tested for 15 different input combinations of the NCCT-IBs to predict HE. Model performance was assessed using area under the receiver operating characteristic curve and probability for accurate diagnosis (P_AD) was calculated. A 20-fold Monte-Carlo cross validation was implemented to ensure model reliability on a limited sample size of data, by running a myriad of random training/testing splits. Results: The developed algorithm was able to predict expansion utilizing all four inputs with an accuracy of 70.17%. Further testing of all biomarker combinations yielded P_AD ranging from 0.57, to 0.70. Conclusion: Specific attributes of ICHs may influence the likelihood of HE and can be evaluated via a machine learning algorithm. However, certain parameters may differ in importance to reach accurate conclusions about potential expansion.

Conference Presentation

Citation Download Citation

Dennis Swetz, Samantha E. Seymour, Ryan A. Rava, Mohammad Mahdi Shiraz Bhurwani, Andre Monteiro, Ammad A. Baig, Muhammad Waqas, Kenneth V. Snyder, Elad I. Levy, Jason M. Davies, Adnan H. Siddiqui, and Ciprian N. Ionita "Initial investigation of predicting hematoma expansion for intracerebral hemorrhage using imaging biomarkers and machine learning", Proc. SPIE 12036, Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging, 120360B (4 April 2022); https://doi.org/10.1117/12.2610672

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available