Related Experiment Video
Updated: Jun 17, 2025
Author Spotlight: A Unique Mouse Model of Asphyxia-Induced Cardiac Arrest
Published on: April 14, 2023
Neurological prognosis prediction for cardiac arrest patients using quantitative imaging biomarkers from brain
Takahiro Nakamoto1, Kanabu Nawa2, Kei Nishiyama3
1Department of Biological Science and Engineering, Faculty of Health Sciences, Hokkaido University, N12-W5, Kita-ku, Sapporo, Hokkaido 060-0812, Japan; Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
Insights
Quantitative imaging biomarkers from brain CT scans can predict neurological outcomes in cardiac arrest (CA) patients. This aids in acute care decision-making for improved patient prognosis.
Area of Science:
- Neurology
- Radiology
- Medical Imaging
Background:
- Predicting neurological prognosis after cardiac arrest (CA) is crucial for patient management.
- Current methods may lack precision in early prognostication.
- Quantitative imaging offers a novel approach to assess brain injury post-CA.
Purpose of the Study:
- To evaluate the efficacy of quantitative imaging biomarkers from brain computed tomography (CT) scans in predicting neurological prognosis for CA patients.
- To identify specific imaging features that correlate with patient outcomes.
Main Methods:
- Retrospective analysis of 86 CA patients' brain CT images.
- Extraction of 1131 quantitative imaging biomarkers.
- Machine learning models (univariate and multivariate) were trained and validated using AUC.
- Feature selection based on statistical significance and predictive performance.
Main Results:
- Multivariate analysis achieved an AUC of 0.813, outperforming univariate analysis (AUC 0.775).
- The gray level with the maximum gradient in a filtered CT image histogram emerged as a key predictive biomarker.
- Statistical significance (p=0.009) was achieved in univariate analysis.
Conclusions:
- Quantitative imaging biomarkers derived from CT scans are effective for predicting neurological prognosis in CA patients.
- These biomarkers can enhance clinical decision support systems in acute care settings.
- Standardized CT acquisition protocols may improve the reliability of these biomarkers.
Purpose:
We aimed to predict the neurological prognosis of cardiac arrest (CA) patients using quantitative imaging biomarkers extracted from brain computed tomography images.
Methods:
We retrospectively enrolled 86 CA patients (good prognosis, 32; poor prognosis, 54) who were treated at three hospitals between 2017 and 2019. We then extracted 1131 quantitative imaging biomarkers from whole-brain and local volumes of interest in the computed tomography images of the patients. The data were split into training and test sets containing 60 and 26 samples, respectively, and the training set was used to select representative quantitative imaging biomarkers for classification. In univariate analysis, the classification was evaluated using the p-value of the Brunner-Munzel test and area under the receiver operating characteristic curve (AUC) for the test set. In multivariate analysis, machine learning models reflecting nonlinear and complex relations were trained, and they were evaluated using the AUC on the test set.
Results:
The best performance provided p = 0.009 (<0.01) and an AUC of 0.775 (95% confidence interval, 0.590-0.960) for the univariate analysis and an AUCof0.813 (95% confidence interval, 0.640-0.985) for the multivariate analysis. Overall, the gray level with the maximum gradient in the histogram of the three-dimensionally low-pass-filtered image was an important feature for prediction across the analyses.
Conclusions:
Quantitative imaging biomarkers can be used in neurological prognosis prediction for CA patients. Relevant biomarkers may contribute to protocolized computed tomography image acquisition to ensure proper decision support in acute care.
![Semi-quantitative Assessment Using [18F]FDG Tracer in Patients with Severe Brain Injury](/_next/image?url=https%3A%2F%2Fcloudfront.jove.com%2FCDNSource%2Fteasers%2F58641.jpg&w=3840&q=50)
