Dento maxillo facial radiology · 2026
Evaluation of Differential Diagnosis of Odontogenic Lesions in Cone Beam Computed Tomography Images Using Radiomics-Based Machine Learning.
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Hammudıoglu Zarif Ece, Kucuk Kurtgoz Merve, Erol Elif Çeçen, Belgın Ceren Aktuna, Orhan Kaan
Аннотация
OBJECTIVE: This study aimed to evaluate the structural characteristics of mandibular alveolar bone in patients with Type 1 diabetes mellitus (T1DM), Type 2 diabetes mellitus (T2DM), and systemically healthy controls using panoramic radiography-based radiomic analysis combined with machine learning algorithms.
MATERIALS AND METHODS: A total of 225 panoramic radiographs (75 T1DM, 75 T2DM, 75 healthy controls) were retrospectively analyzed. ROIs were segmented from eight anatomical mandibular segments per subject, and 107 radiomic features were extracted using PyRadiomics. Interobserver reliability was confirmed by two-way random-effects ICC (≥0.85). A leakage-free pipeline was applied. Four machine learning algorithms were evaluated: Random Forest, ExtraTrees, SVM-RBF, and Logistic Regression.
RESULTS: Significant differences were identified in age (Kruskal-Wallis p = 0.0003) and sex (χ²=17.857, p = 0.0001). The best single-segment performance was achieved in the left mandibular corpus with Logistic Regression (Accuracy=0.833, F1=0.832, AUC=0.958). All segments showed significant radiomic differences (FDR q < 0.001). Sensitivity of 1.000 was achieved for T1DM and AUC=1.000 for T2DM. The feature glszm_SizeZoneNonUniformityNormalized showed the strongest discriminative power (H = 86.928, ε²=0.578).
CONCLUSION: Panoramic radiography-based radiomic analysis demonstrates high diagnostic performance in non-invasively distinguishing mandibular bone alterations among T1DM, T2DM, and healthy individuals, with potential as a clinical bone monitoring tool.
MATERIALS AND METHODS: A total of 225 panoramic radiographs (75 T1DM, 75 T2DM, 75 healthy controls) were retrospectively analyzed. ROIs were segmented from eight anatomical mandibular segments per subject, and 107 radiomic features were extracted using PyRadiomics. Interobserver reliability was confirmed by two-way random-effects ICC (≥0.85). A leakage-free pipeline was applied. Four machine learning algorithms were evaluated: Random Forest, ExtraTrees, SVM-RBF, and Logistic Regression.
RESULTS: Significant differences were identified in age (Kruskal-Wallis p = 0.0003) and sex (χ²=17.857, p = 0.0001). The best single-segment performance was achieved in the left mandibular corpus with Logistic Regression (Accuracy=0.833, F1=0.832, AUC=0.958). All segments showed significant radiomic differences (FDR q < 0.001). Sensitivity of 1.000 was achieved for T1DM and AUC=1.000 for T2DM. The feature glszm_SizeZoneNonUniformityNormalized showed the strongest discriminative power (H = 86.928, ε²=0.578).
CONCLUSION: Panoramic radiography-based radiomic analysis demonstrates high diagnostic performance in non-invasively distinguishing mandibular bone alterations among T1DM, T2DM, and healthy individuals, with potential as a clinical bone monitoring tool.