The radiomics fingerprint of cartilage tumours: radiomics-based MRI differentiation of enchondroma and atypical cartilaginous tumour.

PURPOSE: This study aimed to develop and validate machine learning models based on quantitative radiomics parameters extracted from T1-weighted MRI to differentiate enchondromas from atypical cartilaginous tumours (ACTs). METHODS: A retrospective cohort comprising 66 patients (35 with histopathologically confirmed enchondroma and 31 with ACT) was included in the study. T1-weighted MRI images were used for 2D segmentation, performed independently by two experienced observers on all visible slices of each lesion. A comprehensive set of 107 radiomics features was extracted from these segmented regions of interest. LASSO regression was applied for dimensionality reduction. Four distinct machine learning algorithms-Support Vector Machine (SVM), Random Forest Classifier (RFC), Extreme Gradient Boosting (XGBoost), and Decision Tree Analysis-were trained and validated using a 70:30 data split. RESULTS: The radiomics features demonstrated high inter- and intra-observer reproducibility. All evaluated machine learning models exhibited strong diagnostic performance, with Area Under the Curve (AUC) values exceeding 0.90. Specifically, SVM achieved an AUC of 0.922 (95% CI 0.893-0.951), RFC yielded an AUC of 0.920 (95% CI 0.881-0.963), and Decision Tree Analysis showed an AUC of 0.949 (95% CI 0.927-0.972). Notably, the XGBoost model achieved the highest diagnostic efficacy, boasting an impressive AUC of 0.987 (95% CI 0.976-0.999), coupled with a sensitivity of 89.35% and a specificity of 96.55%. CONCLUSION: Our results indicate that the combination of MRI-based radiomics and machine learning algorithms, particularly XGBoost, offers a non-invasive and highly accurate method for distinguishing enchondroma from ACT.