Machine Learning Based Non-Enhanced CT Radiomics for the Identification of Orbital Cavernous Venous Malformations: An Innovative Tool.

Abstract

To evaluate the capability of non-enhanced computed tomography (CT) images for distinguishing between orbital cavernous venous malformations (OCVM) and non-OCVM, and to identify the optimal model from radiomics-based machine learning (ML) algorithms. A total of 215 cases of OCVM and 120 cases of non- OCVM were retrospectively analyzed in this study. A stratified random sample of 268 patients (80%) was used as the training set (172 OCVM and 96 non-OCVM); the remaining data were used as the testing set. Six feature selection techniques and thirteen ML models were evaluated to construct an optimal classification model. There were statistically significant differences between the OCVM and non-OCVM groups in the density and tumor location (P < 0.05), whereas other indicators were comparable (age, gender, sharp, P > 0.05). Linear regression (area under the curve [AUC] = 0.9351; accuracy = 0.8657) and Stochastic Gradient Descent (AUC = 0.9448; accuracy = 0.8806) classifiers, both of which coupled with the f test and L1-based feature selection method, achieved optimal performance. The support vector machine (AUC = 0.9186; accuracy = 0.8806), Random Forest (AUC = 0.9288; accuracy = 0.8507) and eXtreme Gradient Boosting (AUC = 0.9147; accuracy = 0.8507) classifier combined with f test method showed excellent average performance among our study, respectively. The effect of non-enhanced CT images in OCVM not only can help ophthalmologist to find and locate lesion, but also bring great help for the qualitative diagnosis value using radiomic- based ML algorithms.