Differentiating Radiation Necrosis and Metastatic Progression in Brain Tumors Using Radiomics and Machine Learning

Abstract

Objectives: Distinguishing between radiation necrosis (RN) and metastatic progression is extremely challenging due to their similarity in conventional imaging. This is crucial from a therapeutic point of view as this determines the outcome of the treatment. This study aims to establish an automated technique to differentiate RN from brain metastasis progression using radiomics with machine learning. Methods: Eighty-six patients with brain metastasis after they underwent stereotactic radiosurgery as primary treatment were selected. Discrete wavelets transform, Laplacian-of-Gaussian, Gradient, and Square were applied to magnetic resonance post-contrast T1-weighted images to extract radiomics features. After feature selection, dataset was randomly split into train/test (80%/20%) datasets. Random forest classification, logistic regression, and support vector classification were trained and subsequently validated using test set. The classification performance was measured by area under the curve (AUC) value of receiver operating characteristic curve, accuracy, sensitivity, and specificity. Results: The best performance was achieved using random forest classification with a Gradient filter (AUC=0.910±0.047, accuracy 0.8±0.071, sensitivity=0.796±0.055, specificity=0.922±0.059). For, support vector classification the best result obtains using wavelet HHH with a high AUC of 0.890±0.89, accuracy of 0.777±0.062, sensitivity=0.701±0.084, and specificity=0.85±0.112. Logistic regression using wavelet-HHH provides a poor result with AUC=0.882±0.051, accuracy of 0.753±0.08, sensitivity=0.717±0.208, and specificity=0.816±0.123. Conclusion: This type of machine-learning approach can help accurately distinguish RN from recurrence in magnetic resonance imaging, without the need for biopsy. This has the potential to improve the therapeutic outcome.