Computed tomography enterography-based radiomics for assessing mucosal healing in patients with small bowel Crohn's disease

Table 1 Patient characteristics in the training and test cohorts

Clinical characteristics	Training cohort (n = 55)			Test cohort (n = 18)
	MH	Non-MH	P value	MH	Non-MH	P value
Age (year)	27.32 ± 11.73	34.81 ± 10.93	0.007	31.67 ± 8.50	40.92 ± 13.91	0.158
Gender, n (%)			0.384			0.316
Male	16 (84.21)	25 (69.44)		6 (100.00)	8 (66.67)
Female	3 (15.79)	11 (30.56)		0 (0.00)	4 (33.33)
Disease duration, months	27.11 (23.50)	48.58 (43.50)	0.021	25.67 (3.00)	49.83 (37.00)	0.11
Montreal location, n (%)			0.715			0.761
L1	13 (68.42)	20 (55.56)		4 (66.67)	7 (58.33)
L3	4 (21.05)	12 (33.33)		2 (33.33)	4 (33.33)
L1 + L4	1 (5.26)	3 (8.33)		0 (0.00)	1 (8.33)
L3 + L4	1 (5.26)	1 (2.78)		0 (0.00)	0 (0.00)
Montreal behavior, n (%)			0.219			0.187
B1	15 (78.95)	21 (58.33)		4 (66.67)	3 (25.00)
B2	4 (21.05)	12 (33.33)		1 (16.67)	7 (58.33)
B3	0 (0.00)	3 (8.33)		1 (16.67)	2 (16.67)
Perianal involvement, n (%)	13 (68.42)	10 (27.78)	0.009	1 (16.67)	3 (25.00)	1.000
Treatment history, n (%)
5-aminosalicylic acid	2 (10.53)	5 (13.89)	1.00	0 (0.00)	0 (0.00)	1.000
Steroids	4 (21.05)	15 (41.67)	0.218	2 (33.33)	5 (41.67)	1.000
Immunomodulator	4 (21.05)	19 (52.78)	0.048	2(33.33)	5 (41.67)	1.000
Anti-TNF agent	17 (89.47)	27 (75.00)	0.357	4 (66.67)	9 (75.00)	1.000
Smoking history, n (%)	3 (15.79)	6 (16.67)	1.000	3 (50.00)	3 (25.00)	0.596
CDAI, mean (IQR)	63.51 (31.09)	92.84 (89.14)	0.589	51.72 (35.21)	82.96 (62.13)	0.200
BMI (mean ± SD)	20.98 ± 3.28	21.15 ± 3.73	0.937	23.75 ± 5.06	20.60 ± 1.99	0.250
WBC (× 109/L)	5.69 ± 1.56	5.97 ± 1.92	0.586	5.50 ± 2.40	5.08 ± 1.64	0.779
NLR	1.67 ± 0.70	2.47 ± 1.11	0.006	1.93 ± 1.17	3.80 ± 5.71	0.335
HB (g/L)	139.58 ± 11.79	120.19 ± 27.90	0.004	136.83 ± 15.94	132.08 ± 23.51	0.664
HCT (%)	42.07 ± 3.50	37.12 ± 7.34	0.004	41.05 ± 4.81	39.61 ± 6.49	0.638
ALB (g/L)	42.55 ± 2.85	39.07 ± 4.75	0.006	42.60 ± 1.41	38.87 ± 5.24	0.068
CRP (mg/dL)	2.56 (1.44)	9.86 (8.38)	0.000	1.51 (0.24)	9.00 (10.64)	0.067
CPT (μg/g)	348.02 (455.37)	513.29 (410.31)	0.012	349.45 (374.54)	677.13 (258.78)	0.111

ALB: Albumin; BMI: Body mass index; CDAI: Clinical disease activity index; CPT: Calprotectin; CRP: C-reactive protein; HB: Haemoglobin; HCT: Haematocrit; NLR: Neutrophil-to-lymphocyte ratio; TNF: Tumor necrosis factor; WBC: White blood cell.

Table 2 Diagnostic performance of the radiomic signature based on different machine learning models in the training and test cohorts

Model		AUC (95%CI)	ACC	SENS	SPEC	PPV	NPV
LR	Training	0.950 (0.893-1.000)	0.927	0.842	0.972	0.941	0.921
	Test	0.806 (0.598-1.000)	0.722	1.000	0.583	0.545	1.000
SVM	Training	0.959 (0.887-1.000)	0.964	0.947	0.972	0.947	0.972
	Test	0.944 (0.843-1.000)	0.889	0.833	0.917	0.833	0.917
ExtraTrees	Training	0.965 (0.915-1.000)	0.945	0.895	0.972	0.944	0.946
	Test	0.917 (0.783-1.000)	0.833	1.000	0.750	0.667	1.000
XGBoost	Training	0.971 (0.938-1.000)	0.909	0.947	0.889	0.818	0.970
	Test	0.882 (0.741-1.000)	0.778	1.000	0.667	0.600	1.000
LightGBM	Training	0.932 (0.863-1.000)	0.891	0.895	0.914	0.810	0.941
	Test	0.750 (0.506-0.994)	0.667	1.000	0.500	0.500	1.000

LR: Logistic regression; SVM: Support vector machine; ExtraTrees: Extremely randomized trees; XGBoost: Extreme gradient boosting; LightGBM: Light gradient boosting machine; ACC: Accuracy; AUC: Area under the receiver operating characteristic curve; 95%CI: 95% confidence interval; SENS: Sensitivity; SPEC: Specificity.

Table 3 Diagnostic performance of the clinical, radiomic, and nomogram models

Model	Training cohort (n = 55)				Test cohort (n = 18)
	AUC (95%CI)	ACC	SENS	SPEC	AUC (95%CI)	ACC	SENS	SPEC
Clinical model	0.917 (0.828-1.000)	0.891	0.895	0.889	0.854 (0.678-1.000)	0.778	1.000	0.667
Radiomic model	0.959 (0.887-1.000)	0.964	0.947	0.972	0.944 (0.843-1.000)	0.889	0.833	0.917
Nomogram	0.961 (0.886-1.000)	0.964	0.947	0.972	0.958 (0.877-1.000)	0.889	1.000	0.833

ACC: Accuracy; AUC: Area under the receiver operating characteristic curve; 95%CI: 95% confidence interval; SENS: Sensitivity; SPEC: Specificity.