The application value of CT radiomics features in predicting pressure amplitude correlation index in patients with severe traumatic brain injury

Abstract

PurposeTo explore the application value of a machine learning model based on CT radiomics features in predicting the pressure amplitude correlation index (RAP) in patients with severe traumatic brain injury (sTBI).MethodsRetrospectively analyzed the clinical and imaging data in 36 patients with sTBI. All patients underwent surgical treatment, continuous ICP monitoring, and invasive arterial pressure monitoring. The pressure amplitude correlation index (RAP) was collected within 1 h after surgery. Three volume of interest (VOI) was selected from the craniocerebral CT images of patients 1 h after surgery, and a total of 93 radiomics features were extracted from each VOI. Three models were established to be used to evaluate the patients' RAP levels. The accuracy, precision, recall rate, F1 score, receiver operating characteristic (ROC) curve, and area under the curve (AUC) were used to evaluate the predictive performance of each model.ResultsThe optimal number of features for three predicting models of RAP was five, respectively. The accuracy of predicting the model of the hippocampus was 77.78%, precision was 88.24%, recall rate was 60%, the F1 score was 0.6, and AUC was 0.88. The accuracy of predicting the model of the brainstem was 63.64%, precision was 58.33%, the recall rate was 60%, the F1 score was 0.54, and AUC was 0.82. The accuracy of predicting the model of the thalamus was 81.82%, precision was 88.89%, recall rate was 75%, the F1 score was 0.77, and AUC was 0.96.ConclusionsCT radiomics can predict RAP levels in patients with sTBI, which has the potential to establish a method of non-invasive intracranial pressure (NI-ICP) monitoring.

Noninvasive prediction for intracranial pressure related parameters in traumatic brain injury patients using computed tomography radiomic features.

“…20 Liu also showed that the RAP levels could be judged by using a machine learning model based on texture features analysis. 21 Approach to measuring retinal arteriole and venule diameter ratio (A/V-ratio) on fundus photography also exhibit the good ability to predict ICP levels. 22 Limited by low accessibility and effectiveness, none of these methods could be widely applied in clinical practice.…”