The predictive value of CT radiomics features for intracranial pressure-related parameters in patients with severe traumatic brain injury
Objective To explore the application value of a machine learning model based on CT radiomics features in predicting the pressure amplitude correlation index(RAP)and pressure-reactivity index(PRx)in patients with severe traumatic brain injury(TBI).Methods The clinical and imaging data of 36 patients with severe TBI admitted to the Department of Neurosurgery of Shanghai General Hospital,Shanghai Jiao Tong University School of Medicine from January 2019 to December 2020 were retrospectively analyzed.The admission Glasgow Coma Scale(GCS)score[M(range)]of the included patients was 6(3 to 8)points.All patients underwent surgical treatment,continuous intracranial pressure monitoring,and invasive arterial pressure monitoring.The RAP and PRx were collected within 1 h after surgery.Then one volume of interest(VOI)was selected from the craniocerebral CT images of patients within 1 h after surgery,and a total of 93 radiomics features were extracted from the VOI for predicting RAP and PRx.The recursive feature elimination method was used for feature selection to obtain the optimal feature subset.The random forest algorithm was used to train the classifier to predict PRx and RAP respectively,and a prediction model was constructed based on CT radiomics features.The accuracy,precision,recall rate,F1 score,and receiver operating characteristic(ROC)curve and area under curve(AUC)of models were used to evaluate the predictive performance of CT radiomics features.Results The optimal number of features for predicting PRx and RAP was 12 and 15,respectively.The accuracy of predicting PRx by CT radiomics features was 72％,the precision was 85％,the recall rate was 68％,the F1 score was 0.61,and the AUC was 0.79.The accuracy of predicting RAP by CT radiomics features was 63％,the precision was 78％,the recall rate was 63％,the F1 score was 0.61,and the AUC was 0.80.Conclusion The prediction model based on CT radiomics features can effectively predict PRx and RAP in patients with severe TBI,which could help guide treatment and assess the patient prognosis.
Brain injuries,traumaticIntracranial pressureMachine learningComputed tomographyPressure amplitude correlation indexPressure reactivity index
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