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Noninvasive assessment of intracranial hypertension in patients with traumatic brain injury using CT radiomic features：a pilot study.

This study aimed to assess intracranial hypertension in patients with traumatic brain injury noninvasively using computed tomography (CT) radiomic features. Fifty patients from the primary cohort were enrolled in this study. The clinical data, preoperative cranial CT images, and initial intracranial pressure readings were collected and used to develop a prediction model. Data of 20 patients from another hospital was used to validate the model. Clinical features including age, gender, midline shift, basilar cistern status, and ventriculocranial ratio were measured. Radiomic features, i.e., 18 first-order and 40 second-order features were extracted from the CT images. Lasso method was used for features filtration. Multivariate logistic regression was used to develop three prediction models with clinical (CF model), first-order (FO model), and second-order features (SO model). The SO model achieved the most robust ability to predict intracranial hypertension. Internal validation showed that the C-statistic of the model was 0.811 (95% confidence interval [CI]: 0.691-0.931) with the bootstrapping method. The Hosmer Lemeshow test and calibration curve also showed that the SO model had excellent performance. The external validation results showed a good discrimination with an area under the curve of 0.725 (95% CI: 0.500-0.951). Although the FO model was inferior to the SO model, it had better prediction ability than the CF model. The study shows that the radiomic features analysis, especially second-order features can be used to evaluate intracranial hypertension noninvasively compared to conventional clinical features, given its potential for clinical practice and further research.