Grayscale Volume-Based Radiomic Analysis of Cone Beam Computed Tomography for Differentiation of Odontogenic Cysts and Tumors
DOI:
https://doi.org/10.15517/7es0h673Keywords:
Cone-beam computed tomography; Grayscale value; Jaw cysts; Jaw tumors; Radiomics.Abstract
To quantitatively characterize grayscale volume (GSV) behavior on cone beam computed tomography (CBCT) and evaluate its ability to differentiate odontogenic cysts from odontogenic tumors using first-order radiomic features. In this retrospective study, 100 histopathologically confirmed lesions, including 50 cysts and 50 tumors, were analyzed. CBCT datasets were standardized through resampling and gray-level discretization prior to three-dimensional manual segmentation. Radiomic extraction was performed using an IBSI-compliant pipeline, initially generating 107 features. Based on conceptual alignment with grayscale distribution, analysis was restricted to first-order intensity features. Correlation-based reduction yielded ten non-redundant GSV descriptors representing central tendency, dispersion, distribution shape, and randomness. Group differences were assessed using the Mann-Whitney U test. Principal component analysis (PCA) evaluated structural coherence of the GSV construct. Tumors demonstrated significantly higher variance (p=0.008), maximum intensity (p=0.017), and lower kurtosis (p=0.027) compared with cysts, indicating greater internal heterogeneity and broader intensity spread. Entropy was higher and uniformity lower in tumors, reflecting increased grayscale randomness. Central tendency measures showed limited discriminatory value. PCA revealed that the first three principal components explained 89.45% of total variance, confirming that grayscale volume behavior is structured and governed by dominant intensity-driven components.Grayscale volume analysis provides an objective quantitative framework for volumetric CBCT interpretation. Odontogenic cysts and tumors exhibit distinct intensity dispersion and distribution patterns, supporting GSV as a biologically meaningful quantitative imaging biomarker for lesion differentiation.
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