Abstract 351: Novel Quantitative Imaging Biomarkers From Acute Brain CT Using Artificial Intelligence for Predicting Neurological Prognosis in Cardiac Arrest Patients: A Pilot Study

Abstract

Background: Effective use of information extracted from medical images has attracted attention for decision support in primary care of patients with cardiac arrest (CA). Hypothesis and Aims: This study aimed to predict the neurological prognosis of patients with cardiac arrest by quantitative imaging biomarkers (QIBs) extracted from acute CT brain images before admission using artificial intelligence. Methods: Eighty-six CA patients (good prognosis; Glasgow-Pittsburgh cerebral performance categories [CPC]=1 or 2 at 90 days after the CA : 32 patients, poor prognosis; CPC=3, 4, or 5 at 90 days after the CA : 54 patients) treated at three hospitals between 2017 and 2019 were retrospectively analyzed. None of the CA patients caused by trauma or brain damage were included in the database. The data were divided into training and test data, and the training data were used to select informative QIBs for classification between the good and poor neurological prognosis in this analysis. Results: Seven CA patients’ data were excluded from the study due to severe metal and bone artifacts broadly spreading on the brain image, and 1170 QIBs were extracted from whole brain and regional brain interest volumes on CT images of each patient. The QIBs were composed of first-order histogram and texture features and informative QIBs for the classification were selected by feature selection algorithms using the training dataset. The feature corresponding to the gray level having a maximum gradient in the image histogram from the brain region of interest on CT images showed the strongest significant difference between the good and poor neurological prognosis of the test data set with p=0.009 and AUC=0.775 (95% CI; 0.590-0.960). Conclusions: We have shown that QIB can be used to predict neurological prognosis in patients with CA. Because this is a pilot study for methodological development and there are limitations in sample size and outcomes, new studies on a larger number of patients are required.