Initial CT-based radiomics nomogram for predicting in-hospital mortality in patients with traumatic brain injury: a multicenter development and validation study

Objective To develop and validate a radiomic prediction model using initial noncontrast computed tomography (CT) at admission to predict in-hospital mortality in patients with traumatic brain injury (TBI). Methods A total of 379 TBI patients from three cohorts were categorized into training, interna...

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Bibliographic Details
Published in:Neurological sciences 2022-07, Vol.43 (7), p.4363-4372
Main Authors: Zheng, Rui-zhe, Zhao, Zhi-jie, Yang, Xi-tao, Jiang, Shao-wei, Li, Yong-de, Li, Wen-jie, Li, Xiu-hui, Zhou, Yue, Gao, Cheng-jin, Ma, Yan-bin, Pan, Shu-ming, Wang, Yang
Format: Article
Language:English
Subjects:
Clinical significance
Computed tomography
Medicine
Medicine & Public Health
Mortality
Neurology
Neuroradiology
Neurosciences
Neurosurgery
Nomograms
Original Article
Patients
Prediction models
Psychiatry
Radiomics
Traumatic brain injury
Validation studies
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Summary:Objective To develop and validate a radiomic prediction model using initial noncontrast computed tomography (CT) at admission to predict in-hospital mortality in patients with traumatic brain injury (TBI). Methods A total of 379 TBI patients from three cohorts were categorized into training, internal validation, and external validation sets. After filtering the unstable features with the minimum redundancy maximum relevance approach, the CT-based radiomics signature was selected by using the least absolute shrinkage and selection operator (LASSO) approach. A personalized predictive nomogram incorporating the radiomic signature and clinical features was developed using a multivariate logistic model to predict in-hospital mortality in patients with TBI. The calibration, discrimination, and clinical usefulness of the radiomics signature and nomogram were evaluated. Results The radiomic signature consisting of 12 features had areas under the curve (AUCs) of 0.734, 0.716, and 0.706 in the prediction of in-hospital mortality in the internal and two external validation cohorts. The personalized predictive nomogram integrating the radiomic and clinical features demonstrated significant calibration and discrimination with AUCs of 0.843, 0.811, and 0.834 in the internal and two external validation cohorts. Based on decision curve analysis (DCA), both the radiomic features and nomogram were found to be clinically significant and useful. Conclusion This predictive nomogram incorporating the CT-based radiomic signature and clinical features had maximum accuracy and played an optimized role in the early prediction of in-hospital mortality. The results of this study provide vital insights for the early warning of death in TBI patients.
ISSN:1590-1874
1590-3478
DOI:10.1007/s10072-022-05954-8