Computed tomography-based radiomics to predict early recurrence of hepatocellular carcinoma post-hepatectomy in patients background on cirrhosis

Table 1 The clinical-radiologic characteristics of primary cohort, n (%)

Variable	Training cohort (n = 150)	Validation cohort (n = 64)	P value
Tumor-volume (cm3), mean ± SD	249 ± 381	318 ± 361	0.219
Age (yr), mean ± SD	57.3 ± 10.1	54.5 ± 11.7	0.079
Rad-score	0.4 ± 0.2	0.52 ± 0.16	0.121
BMI			0.691
0, < 18.5	7 (4.67)	3 (4.69)
1, 18.5-25	109 (72.7)	43 (67.2)
2, ≥ 25	34 (22.7)	18 (28.1)
AFP (ng/mL)			0.382
0, ≤ 400	91 (60.7)	34 (53.1)
1, > 400	59 (39.3)	30 (46.9)
sex			0.839
0, male	128 (85.3)	56 (87.5)
1, female	22 (14.7)	8 (12.5)
Hepatitis (HBV/HCV)			0.407
0, absent	22 (14.7)	6 (34.4)
1, present	128 (85.3)	58 (90.6)
N (× 109/L)			0.275
0, < 1.8	127 (84.7)	54 (84.4)
1, 1.8-6.3	18 (12.0)	10 (15.6)
2, > 6.3	5 (3.33)	0 (0.00)
L (× 109/L)			0.504
0, ≥ 1.1	119 (79.3)	54 (84.4)
1, < 1.1	31 (20.7)	10 (15.6)
PLT (× 109/L)			0.703
0, > 100	120 (80.0)	49 (76.6)
1, ≤ 100	30 (20.0)	15 (23.4)
ALT (U/L)			0.959
0, ≤ 50	117 (78.0)	49 (76.6)
1, > 50	33 (22.0)	15 (23.4)
AST (U/L)			0.854
0, > 40	67 (44.7)	27 (39.1)
1, ≤ 40	83 (55.3)	39 (60.9)
GGT (U/L)			0.113
0, ≤ 60	78 (52.0)	25 (43.8)
1, > 60	72 (48.0)	39 (56.2)
TB (umol/L)			0.605
0, ≤ 21	110 (73.3)	44 (68.8)
1, > 21	40 (26.7)	20 (31.2)
ALB (g/L)			0.720
0, ≤ 40	76 (50.7)	30 (46.9)
1, > 40	74 (49.3)	34 (53.1)
NLR			0.697
0, ≤ 2	74 (49.3)	29 (45.3)
1, > 2	76 (50.7)	35 (54.7)
PLR			0.528
0, ≥ 95	78 (52.0)	37 (57.8)
1, < 95	72 (48.0)	27 (42.2)
HbsAg			1
Negative	18 (12.0)	8 (12.5)
Positive	132 (88.0)	56 (87.5)
BCLC			0.554
0, stage0	7 (4.67)	5 (7.81)
1, stageA	143 (95.3)	59 (92.2)
CNLC			0.308
0, Ia	74 (49.3)	26 (40.6)
1, Ib	76 (50.7)	38 (59.4)
Non-peripheral washout			0.738
0, absent	2 (1.33)	2 (3.12)
1, present	148 (98.7)	62 (96.9)
Tumor capsule			0.110
0, ill-defined capsule	58 (38.7)	33 (51.6)
1, well-defined capsule	92 (61.3)	31 (48.4)
Intratumor vascularity			0.654
0, absent	24 (16.0)	8 (12.5)
1, present	126 (84.0)	56 (87.5)
Tumor growth pattern			0.635
0, intrahepatic growth	56 (37.3)	21 (32.8)
1, extrahepatic growth	94 (62.7)	43 (67.2)
Fusion lesions			0.255
0, absent	87 (58.0)	31 (48.4)
1, present	63 (42.0)	33 (51.6)
Intratumor necrosis			0.05
0, absent	47 (31.3)	11 (17.2)
1, present	103 (68.7)	53 (82.8)
Peritumoral enhancement			1
0, absent	90 (60.0)	39 (60.9)
1, present	60 (40.0)	25 (39.1)
Tumor margin			0.76
0, smooth	84 (56.0)	38 (59.4)
1, non-smooth	66 (44.0)	26 (40.6)
Arterial phase hyperenhancement			0.738
0, absent	2 (1.33)	2 (3.12)
1, present	148 (98.7)	62 (96.9)

BMI: Body mass index; AFP: Alpha fetoprotein; N: Neutrophil; L: Lymphocyte; ALT: Alanine transferase; AST: Aspartate transferase; GGT: Gamma-glutamyl transferase; PLT: Platelets; TB: Total bilirubin; ALB: Albumin; NLR: Neutrophil-to-lymphocyte ratio; PLR: Platelet-to-lymphocyte ratio; BCLC: Barcelona Clinic Liver Cancer; CNLC: China Liver Cancer Staging System.

Table 2 Of 150 patients univariate and multivariate logistic regression analysis

Variable	Univariable			Multivariable
	OR	95%CI	P value	OR	95%CI	P value
Tumor-volume	1.00	1.00-1.00	0.022	1.00	1.00-1.00	0.189
Rad-score	238.02	20.57-2754.04	0.001	298.44	12.57-7083.67	< 0.001
AST
0, > 40
1, ≤ 40	0.47	0.25-0.91	0.025	0.93	0.38-2.29	0.875
GGT
0, ≤ 60
1, > 60	3.99	2.02-7.86	0.001	2.50	1.07-5.86	0.034
CNLC
0, Ia
1, Ib	2.26	1.17-4.35	0.015	1.02	0.39-2.63	0.975
Capsule appearance
0, ill-defined
1, well-defined capsule	0.44	0.23-0.86	0.017	0.33	0.14-0.77	0.01
Peritumoral enhancement
0, absent
1, present	3.21	1.62-6.34	0.001	3.85	1.67-8.88	0.002

AST: Aspartate transferase; GGT: Gamma-glutamyl transferase; CNLC: China Liver Cancer Staging System.

Table 3 Comparison of machine learning model performance

	Models	Training cohort			Validation cohort
		Accuracy	Precision	AUC	Accuracy	Precision	AUC
Cli	SVM	0.720	0.750	0.736	0.688	0.867	0.686
	RF	0.720	0.750	0.768	0.688	0.867	0.680
	KNN	0.693	0.667	0.763	0.672	0.756	0.701
	XGB	0.720	0.750	0.768	0.688	0.867	0.680
	LightGBM	0.720	0.750	0.761	0.688	0.867	0.693
Rad	SVM	0.673	0.695	0.726	0.719	0.853	0.756
	RF	0.753	0.761	0.849	0.672	0.744	0.688
	KNN	0.713	0.716	0.809	0.656	0.778	0.690
	XGBoost	0.860	0.849	0.945	0.594	0.700	0.588
	LightGBM	0.727	0.696	0.820	0.688	0.729	0.629
Con	SVM	0.727	0.754	0.778	0.688	0.867	0.739
	RF	0.727	0.731	0.820	0.797	0.872	0.777
	KNN	0.767	0.747	0.844	0.719	0.816	0.790
	XGB	0.840	0.875	0.924	0.609	0.743	0.656
	LightGBM	0.807	0.787	0.892	0.719	0.800	0.771

Five model performance evaluation results from clinical-radiologic, radiomics andcombined models. Cli: Clinical-radiologic model; Rad: Radiomics model; Con: Combined model.

Table 4 Delong test between models

Model	Model	Train		Validation
		Z	P value	Z	P value
Cli	Rad	0.757	0.449	-0.791	0.429
Cli	Con	-2.988	0.003	-2.099	0.036
Rad	Con	-3.253	0.001	-0.713	0.476

Cli: Clinical-radiologic model, Rad: Radiomics model, Con: Combined model.