Computer tomography-based radiomics combined with machine learning for predicting the time since onset of epidural hematoma.

Abstract

Estimation of the age of epidural hematoma (EDH) is a challenge in clinical forensic medicine, and this issue has yet to be conclusively resolved. The advantages of objectivity and non-invasiveness make computing tomography (CT) imaging an potential diagnostic method for EDH in living individuals. Recently, radiomics, the extraction hidden information from medical images, has emerged as a promising method for constructing predictive models. The aim of this study is to explore the feasibility and applicability of CT-based radiomics in predicting the timing of EDH injuries in surviving victims. A cohort of 95 EDH cases with definite injured time (within 12 h since injury) was selected. Clinical characteristics (age, gender, injury time, bleeding location, bleeding volume, and fracture) were recorded. The datasets were divided randomly into training and test cohorts. LIFEx software was used to segment the hematoma area in the CT and extract radiomic features. Machine learning algorithms were applied for features selection and model building. Twenty-three features were selected to calculate the Radscore, a key metric in our analysis. Utilizing this Radscore in conjunction with the time since injury, we constructed an Ordinary Least Squared (OLS) model. Our validation study has shown that mean absolute error (MAE) of the test cohort was 2.42 h, indicating a high degree of accuracy. In order to enhance the accuracy of prediction, the dataset was divided into unstable phase, occurring within the first 5 h post injury, and the stable phases. The Random Forest algorithm presented a significant divergence in predictive performance between the two phases, achieving an area under the curve (AUC) of 0.79, with an accuracy of 75.86%. The MAE of the regression model was 1.05 h for the unstable phase, and 1.23 h for the stable phase. Our findings underscore the potential of CT-based radiomics to offer a novel, convenient, and efficient approach to dating EDH, promising to illuminate new avenues in the field of medical diagnostics.