Differentiation of Glioblastomas From Solitary Brain Metastases Using Radiomic Machine-Learning Classifiers

Abstract

Background: This study identifies optimal radiomic machine-learning classifiers to differentiate glioblastomas (GBM) from solitary brain metastases (MET), the most common neoplasms in adults, preoperatively. Methods: Four hundred and twelve patients with solitary brain tumors (242 with GBM and 170 with solitary brain MET) were divided into training (n =227) and validation (n =185) sets. Extraction of radiomic features from preoperative magnetic resonance images of each patient was accomplished with PyRadiomics software. Twelve feature selection methods and seven classification methods were evaluated to construct optimal radiomic machine-learning classifiers in the training set that were subsequently evaluated in a validation set. The role of the classifiers in differential diagnosis was evaluated using the mean area under the curve (AUC) and relative standard deviation in percentile (RSD). Findings:In the training set, thirteen classifiers had favorable predictive performances (AUC≥0.95 and RSD ≤6). In the validation set, receiver operating characteristic (ROC) curve analysis revealed that support vector machines (SVM) least absolute shrinkage and selection operator (LASSO) (AUC, 0.90) classifiers had the highest prediction efficacy, followed by Adaboost (ADa) LASSO (AUC, 0.89), Multi-Layer Perceptron (MLP) LASSO (AUC, 0.87), and random forest (RF) LASSO (AUC, 0.87). Furthermore, the clinical performance of these radiomic machine-learning classifiers were superior to neuroradiologists in accuracy, sensitivity, and specificity. Interpretation: By employing radiomic machine-learning technology, optimal machine-learning classifiers were identified for differentiating GBM from solitary brain MET preoperatively, which could significantly augment therapeutic strategies. Funding Statement: We acknowledge financial support from the National Natural Science Foundation of China (No. 81601452), and Key laboratory of functional and clinical translational medicine, Fujian province university(JNYLC1808) Declaration of Interests: The author declares no competing interest. Ethics Approval Statement: This study was approved by the ethics committee of Beijing Tiantan Hospital.