A Radiomics Nomogram for Classifying Hematoma Entities in Acute Spontaneous Intracerebral Hemorrhage on Non-contrast-Enhanced Computed Tomography

作者：Jia Wang, Xing Xiong, Jing Ye, Yang Yang, Jie He, Juan Liu, Yi-Li Yin · 发表于：Frontiers in Neuroscience · 年份：2022 · DOI：10.3389/fnins.2022.837041 · 被引用次数：10 · 研究领域：Intracerebral and Subarachnoid Hemorrhage Research、Radiomics and Machine Learning in Medical Imaging、Vascular Malformations Diagnosis and Treatment

Aim: To develop and validate a radiomics nomogram on non-contrast-enhanced computed tomography (NECT) for classifying hematoma entities in patients with acute spontaneous intracerebral hemorrhage (ICH). Materials and Methods: One hundred and thirty-five patients with acute intraparenchymal hematomas and baseline NECT scans were retrospectively analyzed, i.e., 52 patients with vascular malformation-related hemorrhage (VMH) and 83 patients with primary intracerebral hemorrhage (PICH). The patients were divided into training and validation cohorts in a 7:3 ratio with a random seed. After extracting the radiomics features of hematomas from baseline NECT, the least absolute shrinkage and selection operator (LASSO) regression was applied to select features and construct the radiomics signature. Multivariate logistic regression analysis was used to determine the independent clinical-radiological risk factors, and a clinical model was constructed. A predictive radiomics nomogram was generated by incorporating radiomics signature and clinical-radiological risk factors. Nomogram performance was assessed in the training cohort and tested in the validation cohort. The capability of models was compared by calibration, discrimination, and clinical benefit. Results: LASSO regression. The clinical model was constructed with the combination of age [odds ratio (OR): 6.731; 95% confidence interval (CI): 2.209-20.508] and hemorrhage location (OR: 0.089; 95% CI: 0.028-0.281). Radiomics nomogram [...