Radiomics Features on Computed Tomography Combined With Clinical-Radiological Factors Predicting Progressive Hemorrhage of Cerebral Contusion

Qingning Yang; Jun Sun; Yi Guo; Ping Zeng; Ke Jin; Chencui Huang; Jingxu Xu; Liran Hou; Chuanming Li; Junbang Feng

doi:10.3389/fneur.2022.839784

Radiomics Features on Computed Tomography Combined With Clinical-Radiological Factors Predicting Progressive Hemorrhage of Cerebral Contusion

Front Neurol. 2022 Jun 14:13:839784. doi: 10.3389/fneur.2022.839784. eCollection 2022.

Authors

Qingning Yang¹, Jun Sun¹, Yi Guo¹, Ping Zeng¹, Ke Jin², Chencui Huang², Jingxu Xu², Liran Hou³, Chuanming Li¹, Junbang Feng¹

Affiliations

¹ Department of Radiology, Chongqing University Central Hospital, Chongqing, China.
² Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Beijing, China.
³ Department of Radiology, Panjiang Central Hospital, Guizhou, China.

Abstract

Background: Traumatic brain injury (TBI) is the main cause of death and severe disability in young adults worldwide. Progressive hemorrhage (PH) worsens the disease and can cause a poor neurological prognosis. Radiomics analysis has been used for hematoma expansion of hypertensive intracerebral hemorrhage. This study attempts to develop an optimal radiomics model based on non-contrast CT to predict PH by machine learning (ML) methods and compare its prediction performance with clinical-radiological models.

Methods: We retrospectively analyzed 165 TBI patients, including 89 patients with PH and 76 patients without PH, whose data were randomized into a training set and a testing set at a ratio of 7:3. A total of 10 different machine learning methods were used to predict PH. Univariate and multivariable logistic regression analyses were implemented to screen clinical-radiological factors and to establish a clinical-radiological model. Then, a combined model combining clinical-radiological factors with the radiomics score was constructed. The area under the receiver operating characteristic curve (AUC), accuracy and F1 score, sensitivity, and specificity were used to evaluate the models.

Results: Among the 10 various ML algorithms, the support vector machine (SVM) had the best prediction performance based on 12 radiomics features, including the AUC (training set: 0.918; testing set: 0.879) and accuracy (training set: 0.872; test set: 0.834). Among the clinical and radiological factors, the onset-to-baseline CT time, the scalp hematoma, and fibrinogen were associated with PH. The radiomics model's prediction performance was better than the clinical-radiological model, while the predictive nomogram combining the radiomics features with clinical-radiological characteristics performed best.

Conclusions: The radiomics model outperformed the traditional clinical-radiological model in predicting PH. The nomogram model of the combined radiomics features and clinical-radiological factors is a helpful tool for PH.

Keywords: machine learning; nomogram; progressive hemorrhage; radiomics; traumatic brain injury.