Paola Di Giacomo

Background/Objectives. Radiomics is an emerging imaging-based tool that enhances lesion characterization beyond conventional diagnostic approaches. Its potential in evaluating osteolytic lesions of the jaws lies in improving discrimination between benign and malignant entities. This study aimed at developing a predictive model to identify radiomic features capable of distinguishing benign from malignant lesions. Methods. Subjects with preoperative CT or CBCT and histopathological confirmation were included. A pilot cohort was used for feature selection via LASSO regression, which ranked features by frequency and absolute coefficient. Malignancy was coded as class 1, benign lesions as class 0. Positive coefficients indicated association with malignancy, while negative coefficients with benign characteristics. The most stable features were initially trained on the pilot cohort and then validated on an independent test set through machine learning classifiers as LASSO, support vector machine, artificial neural network, random forest e XGboost. Results. The sample comprised 69 subjects (pilot cohort = 57, test cohort = 12). The predictors selected from LASSO regression were: DifferenceEntropy_GLCM (−0.768), CenterOfMassShift_MORPHOLOGICAL (−1.390), INTENSITY-HISTOGRAM_MaximumHistogramGradientGrayLevel (1.139), GLRLM_ShortRunLowGrayLevelEmphasis (−0.742), and Maximum3DDiameter_MORPHOLOGICAL (0.932). As for model performance on test, LASSO achieved the best performance (AUC 0.83), with perfect specificity and sensitivity of 0.71. SVM showed good AUC but poor sensitivity, while random forest and XGBoost performed poorly (AUC 0.57 and 0.37, respectively). Conclusions. The LASSO model proved to be a transparent and robust classifier, suitable for both feature selection and external validation. The selected features demonstrated strong discriminative ability, supporting the potential of radiomics in improving lesion assessment and guiding clinical decision-making.