Cone-beam computed tomography-based radiomic analysis of architectural phenotypes in jaw cysts and tumors using interpretable artificial intelligence models

Table 3 Final radiomic architectural phenotype signature identified using LASSO regression

Feature name	Domain	Architectural relevance	LASSO coefficient
Voxel volume	Shape	Represents overall volumetric architectural expansion and lesion growth pattern	1.284
Total energy	First-order	Reflects cumulative voxel intensity concentration within internal lesion architecture	1.116
Variance	First-order	Quantifies dispersion and variability of voxel intensity distribution	0.948
Difference Variance	GLCM	Captures local gray-level fluctuation and internal architectural variability	1.693
Dependence entropy	GLDM	Represents spatial dependency randomness and internal heterogeneity	1.558
Gray level non-uniformity	GLDM	Reflects irregularity of gray-level distribution across dependent voxel structures	1.402
Run length non-uniformity	GLRLM	Quantifies disruption and inconsistency of structural continuity within lesion architecture	1.341
Zone entropy	GLSZM	Represents disorder and heterogeneity of spatial zone organization	0.912
Complexity	NGTDM	Reflects overall architectural complexity and spatial organizational disarray	0.744

NGTDM: Neighborhood gray tone difference matrix; GLSZM: Gray level size zone matrix; GLRLM: Gray level run length matrix; GLDM: Gray level dependence matrix; GLCM: Gray level co-occurrence matrix.