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

Table 1 Segmentation reproducibility analysis using dice similarity coefficient

Reproducibility parameter	Minimum DSC	Maximum DSC	mean ± SD
Interobserver reproducibility	0.84	0.93	0.89 ± 0.03
Intraobserver reproducibility	0.87	0.95	0.91 ± 0.02

DSC: Dice similarity coefficient.

Table 2 Non-redundant radiomic features retained following correlation pruning

Feature name	Domain	P value
Shape features
Voxel volume	Shape	0.026942
First-order features
Total energy	First-order	0.025882
Root mean squared	First-order	0.029218
Variance	First-order	0.032481
Maximum	First-order	0.045327
GLCM features
Difference variance	GLCM	0.024381
GLDM features
Dependence entropy	GLDM	0.026331
Dependence non-uniformity	GLDM	0.030004
Gray level variance	GLDM	0.033192
Gray level non-uniformity	GLDM	0.039126
GLRLM features
Run length non-uniformity	GLRLM	0.028417
Gray level variance	GLRLM	0.028963
Run entropy	GLRLM	0.027496
High gray level run emphasis	GLRLM	0.043527
GLSZM features
Gray level non-uniformity	GLSZM	0.027105
Zone entropy	GLSZM	0.035674
Large area high gray level emphasis	GLSZM	0.038415
Gray level non-uniformity normalized	GLSZM	0.041118
Gray level variance	GLSZM	0.044186
NGTDM features
Coarseness	NGTDM	0.025114
Complexity	NGTDM	0.040283

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.

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.

Table 4 Artificial intelligence model performance for architectural phenotype classification, 95%CI

Model	AUC	Accuracy (%)	Sensitivity (%)	Specificity (%)	Precision	F1-score
Logistic regression	0.92 (0.87-0.96)	85.0 (78.2-90.4)	87.2 (80.1-92.5)	82.6 (74.8-88.9)	0.84 (0.77-0.90)	0.84 (0.77-0.90)
Random forest	0.87 (0.81-0.92)	81.0 (73.7-86.9)	83.1 (75.4-89.1)	78.4 (70.2-85.1)	0.80 (0.72-0.86)	0.80 (0.72-0.86)
Support vector machine	0.84 (0.77-0.89)	79.0 (71.5-85.2)	80.4 (72.5-86.8)	76.8 (68.4-83.7)	0.78 (0.70-0.85)	0.78 (0.70-0.85)

AUC: Area under the curve.

Table 5 Independent validation cohort performance, 95%CI

Model	AUC	Accuracy (%)	Sensitivity (%)	Specificity (%)	Precision
Logistic regression	0.89 (0.82-0.95)	83.3 (74.2-90.3)	84.7 (75.1-91.7)	81.5 (71.3-89.2)	0.82 (0.73-0.89)
Random forest	0.85 (0.77-0.91)	80.0 (70.5-87.5)	81.2 (71.2-88.8)	78.6 (68.1-86.8)	0.79 (0.69-0.87)
Support vector machine	0.81 (0.72-0.88)	76.7 (66.9-84.8)	78.4 (68.2-86.5)	74.1 (63.3-83.1)	0.76 (0.66-0.84)

AUC: Area under the curve.