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Initial investigation of predicting hematoma expansion for intracerebral hemorrhage using imaging biomarkers and
Dennis Swetz1,2, Samantha E Seymour1,2, Ryan A Rava1,2
1Department of Biomedical Engineering, University at Buffalo, Buffalo NY 14228.
Insights
Machine learning models using non-contrast computed tomography imaging biomarkers can predict intracerebral hemorrhage growth. This aids in assessing the risk of hematoma expansion in acute stroke patients.
Area of Science:
- Neurology
- Radiology
- Artificial Intelligence
Background:
- Intracerebral Hemorrhage (ICH) is a severe stroke type with high mortality and morbidity.
- Early hematoma expansion (HE) significantly worsens patient outcomes.
- Predicting HE is crucial for timely intervention.
Purpose of the Study:
- To evaluate if non-contrast computed tomography imaging biomarkers (NCCT-IB) at initial presentation can predict acute-stage ICH growth.
- To identify key imaging features for predicting hematoma expansion.
Main Methods:
- Retrospective analysis of NCCT data from 200 acute ICH patients.
- Identification of four NCCT-IBs: blending region, dark hole, island, and edema.
- Development and testing of supervised machine learning models using various biomarker combinations.
Main Results:
- The algorithm predicted HE with 70.17% accuracy using all four biomarkers.
- Model performance, assessed by area under the ROC curve, ranged from 0.57 to 0.70.
- Cross-validation confirmed model reliability.
Conclusions:
- Specific ICH attributes, identifiable via machine learning, influence HE likelihood.
- NCCT-IBs show potential for predicting hematoma expansion.
- Further research may refine the importance of individual parameters for accurate prediction.
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 200 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 (PAD) 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 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.
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