Radiomics-based quantitative assessment of the mandibular cortical index on panoramic radiographs

This study aimed to quantitatively evaluate mandibular cortical changes related to the Mandibular Cortical Index (MCI) using radiomic features extracted from panoramic radiographs in order to improve the objectivity and reproducibility of MCI assessment. A total of 159 panoramic radiographs (C1 = 53, C2 = 53, C3 = 53) were retrospectively analyzed. The right mandibular cortex was segmented following standardized anatomical boundaries, and radiomic features were extracted using PyRadiomics in 3D Slicer. Five feature classes-shape, first-order, GLCM, GLRLM, and GLSZM-were evaluated. Inter-observer reproducibility was assessed using intraclass correlation coefficients (ICC), and only features with ICC > 0.75 were included. Statistical significance was set at p < 0.05. Several first-order features, including Energy (p = 0.006), Entropy (p = 0.001), IQR (p = 0.002), MAD (p = 0.002), rMAD (p = 0.001), and Variance (p = 0.001), differ significantly across groups, consistently distinguishing C1 from resorbed cortices. Texture analysis revealed significant differences in multiple GLCM features, such as Correlation (p < 0.001), Cluster Shade (p = 0.010), and Joint Entropy (p = 0.006). Gray Level Variance (GLRLM) (p = 0.001) and Zone Entropy (GLSZM) (p = 0.002) also demonstrated significant group effects. However, after Benjamini-Hochberg false discovery rate (FDR) correction for multiple comparisons, no radiomic feature remained significantly different between the moderately (C2) and severely (C3) resorbed cortices. Radiomics enables objective quantification of morphometric and microtextural differences associated with MCI categories and may enhance the objectivity of mandibular cortical assessment on panoramic radiographs.