Abstract
OBJECTIVES: This study evaluated the radiologic and radiomic features extracted from magnetic resonance imaging (MRI) in meningioma after radiation therapy and investigated the impact of radiation therapy in treating meningioma based on routine brain MRI. METHODS: Observation (n = 100) and radiation therapy (n = 62) patients with meningioma who underwent MRI were randomly divided (7:3 ratio) into training (n = 118) and validation (n = 44) groups. Radiologic findings were analyzed. Radiomic features (filter types: original, square, logarithm, exponential, wavelet; feature types: first order, texture, shape) were extracted from the MRI. The most significant radiomic features were selected and applied to quantify the imaging phenotype using random forest machine learning algorithms. Area under the curve (AUC), sensitivity, and specificity for predicting both the training and validation sets were computed with multiple-hypothesis correction. RESULTS: The radiologic difference in the maximum area and diameter of meningiomas between two groups was statistically significant. The tumor decreased in the treatment group. A total of 241 series and 1691 radiomic features were extracted from the training set. In univariate analysis, 24 radiomic features were significantly different (P < 0.05) between both groups. Best subsets were one original, three first-order, and six wavelet-based features, with an AUC of 0.87, showing significant differences (P < 0.05) in multivariate analysis. When applying the model, AUC was 0.76 and 0.79 for the training and validation set, respectively. CONCLUSION: In meningioma cases, better size reduction can be expected after radiation treatment. The radiomic model using MRI showed significant changes in radiomic features after radiation treatment.