Radiomics-based prediction of hemorrhage expansion among patients with thrombolysis/thrombectomy related-hemorrhagic transformation using machine learning. (November 2021)

• Record Type: Journal Article
• Authors: Liu, Junfeng; Tao, Wendan; Wang, Zhetao; Chen, Xinyue; Wu, Bo; Liu, Ming
• Is Part Of: Therapeutic advances in neurological disorders. Volume 14(2021)
• Publication Date: 2021-11
• Subjects: hemorrhage expansion -- hemorrhagic transformation -- machine learning -- radiomic score -- thrombolysis/thrombectomy; Nervous system -- Diseases -- Periodicals; Nervous system -- Degeneration -- Periodicals; Nervous system -- Diseases -- Treatment -- Periodicals; Nervous System Diseases -- therapy -- Periodicals; Neurodegenerative Diseases -- Periodicals; Système nerveux -- Maladies -- Périodiques; Système nerveux -- Dégénérescence -- Périodiques; Système nerveux; Système nerveux -- Maladies -- Traitement -- Périodiques; 616.805
• DOI: 10.1177/17562864211060029
• Languages: English
• ISSNs: 1756-2856

Abstract

Introduction: Patients with hemorrhagic transformation (HT) were reported to have hemorrhage expansion. However, identification these patients with high risk of hemorrhage expansion has not been well studied. Objectives: We aimed to develop a radiomic score to predict hemorrhage expansion after HT among patients treated with thrombolysis/thrombectomy during acute phase of ischemic stroke. Methods: A total of 104 patients with HT after reperfusion treatment from the West China hospital, Sichuan University, were retrospectively included in this study between 1 January 2012 and 31 December 2020. The preprocessed initial non-contrast-enhanced computed tomography (NECT) imaging brain images were used for radiomic feature extraction. A synthetic minority oversampling technique (SMOTE) was applied to the original data set. The after-SMOTE data set was randomly split into training and testing cohorts with an 8:2 ratio by a stratified random sampling method. The least absolute shrinkage and selection operator (LASSO) regression were applied to identify candidate radiomic features and construct the radiomic score. The performance of the score was evaluated by receiver operating characteristic (ROC) analysis and a calibration curve. Decision curve analysis (DCA) was performed to evaluate the clinical value of the model. Results: Among the 104 patients, 17 patients were identified with hemorrhage expansion after HT detection. A total of 154 candidate predictors were extracted from NECT images and five optimal features were ultimately included in the development of the radiomic score by using logistic regression machine-learning approach. The radiomic score showed good performance with high area under the curves in both the training data set (0.91, sensitivity: 0.83; specificity: 0.89), test data set (0.87, sensitivity: 0.60; specificity: 0.85), and original data set (0.82, sensitivity: 0.77; specificity: 0.78). The calibration curve and DCA also indicated that there was a high accuracy and clinical usefulness of the radiomic score for hemorrhage expansion prediction after HT. Conclusions: The currently established NECT-based radiomic score is valuable in predicting hemorrhage expansion after HT among patients treated with reperfusion treatment after ischemic stroke, which may aid clinicians in determining patients with HT who are most likely to benefit from anti-expansion treatment.