Table 3.

Selected radiomic features for predicting tumor doubling time from generalized estimating equations in growth pattern II lung adenocarcinomas

	Radiomic features	Simple			Multiple ‡
		Coefficient †	SE	P‐value	Coefficient †	SE	P‐value
Shape features	Roundness factor	−3.06	1.15	0.00786
	Solidity ‡	−4.58	1.53	0.00279	−11.119	1.65	1.60E‐11
	Surface area	0.000956	0.000406	0.0187
	Max3D diameter	0.0527	0.0168	0.00176
Local features (texture‐based)	Auto correlation (GLCM)	−0.0000321	0.0000147	0.0295
	Cluster tendency (GLCM)	−0.00000812	0.00000369	0.0277
	Dissimilarity (GLCM)	−0.0428	0.0238	0.0721
	Entropy (GLCM)	0.285	0.149	0.0552
	Energy (GLCM) ‡	−243	86.3	0.0048	297.929	62.323	1.70E‐06
	Homogeneity (GLCM)	16.4	5.6	0.00345
	Max probability (GLCM)	−100	34.5	0.0037
	Variance (GLCM)	−0.0000329	0.0000148	0.0262
	Intensity variability (ISZM)	0.0156	0.00479	0.00111
	Size zone variability (ISZM)	0.0721	0.0283	0.0108
	Contrast (NGTDM)	−1.68	0.858	0.0504
	Busyness (NGTDM) ‡				−54.661	15.476	0.00041
Filter‐based features	LoG entropy (σ = 1.5)	0.403	0.232	0.0829
	LoG entropy (σ = 2)	0.505	0.298	0.0906
	LoG uniformity (σ = 1.5)	−38.5	18.2	0.0351
	LoG uniformity (σ = 2)	−29.5	13.6	0.0303
	LoG uniformity (σ = 3)	−7.46	3.23	0.0208
	LoG uniformity (σ = 3.5)	−4.53	2.28	0.0465
	LoG kurtosis (σ = 0.5)	0.835	0.257	0.00113
Fractal model‐based features	Lacunarity	0.336	0.146	0.0213
	Fractal signature dissimilarity	−0.536	0.187	0.00409

^†Coefficient estimated by generalized estimating equation.

^‡Variables were selected using a backward stepwise variable selection method.

Variables in bold are those that had clinical significance without redundancy within the radiomic information as well as a P‐value < 0.01 after multiple generalized estimating equation analysis.

GLCM, gray level co‐occurrence matrix‐based features; ISZM, intensity size zone matrix‐based features; LoG, Laplacian of Gaussian; NGTDM, neighborhood gray tone difference matrix‐based features; SE, standard error.