Open AccessPrognostic prediction of hypertensive intracerebral hemorrhage using CT radiomics and machine learning
Author(s) -
Xu Xinghua,
Zhang Jiashu,
Yang Kai,
Wang Qun,
Chen Xiaolei,
Xu Bainan
Publication year - 2021
Publication title -
brain and behavior
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.915
H-Index - 41
ISSN - 2162-3279
DOI - 10.1002/brb3.2085
Subject(s) - random forest , artificial intelligence , lasso (programming language) , intracerebral hemorrhage , logistic regression , radiomics , machine learning , medicine , decision tree , feature selection , support vector machine , dimensionality reduction , computer science , subarachnoid hemorrhage , world wide web
Objectives Spontaneous intracerebral hemorrhage remains a major cause of death and disability throughout the world. We tried to establish accurate long‐term outcome prediction models for hypertensive intracerebral hemorrhage (HICH) using CT radiomics and machine learning. Methods In a retrospective study of 270 patients with HICH between June 2013 and June 2018, CT images and patients' 6‐month outcome based on the modified Rankin Scale were collected. Hematomas on CT images were selected as volumes of interests (VOIs), and 1,029 radiomics features of the VOIs were extracted. Based on correlations with patients' outcome, radiomics features underwent dimensionality reduction analyses. Then, the support vector machine (SVM), k‐nearest neighbor (KNN), logistic regression (LR), decision tree (DT), random forest (RF), and XGBoost algorithms were applied with the screened features to establish prognostic prediction models of HICH. Accuracies of all models were compared. Results Eighteen radiomics features were screened as prognosis‐associated radiomics signature of HICH based on the variance threshold, SelectKBest, and least absolute shrinkage and selection operator (LASSO) regression models. Patients were randomly allocated into training ( n = 215) and validation ( n = 55) sets. Accuracies of all 6 machine learning algorithms in the validation set exceeded 80%. The sensitivity, specificity, and accuracy in the validation set were 93.3%, 92.5%, and 92.7% for the RF model and 92.3%, 88.1%, and 89.1% for the XGBoost model, respectively, which were the best two among all models. Conclusions Taking advantage of radiomics and machine learning, we established accurate prognostic prediction models of HICH. The RF model and XGBoost model returned the best accuracies.