Preoperative model for predicting early recurrence in hepatocellular carcinoma patients using radiomics and deep learning: A multicenter study

Table 1 Clinical characteristics of patients included in three sets, n (%)

	Training set (n = 155)	Internal validation set (n = 67)	External validation set (n = 66)	P value
Sex				0.386
Female	30 (19.4)	13 (19.4)	18 (27.3)
Male	125 (80.6)	54 (80.6)	48 (72.7)
Age (years)	57.9 ± 11.7	58.9 ± 11.3	61.8 ± 9.07	0.06
≤ 50	38 (24.5)	20 (29.9)	12 (18.2)	0.290
> 50	117 (75.5)	47 (70.1)	54 (81.8)
History of hepatitis				0.110
Negative	57 (36.8)	30 (44.8)	18 (27.3)
Positive	98 (63.2)	37 (55.2)	48 (72.7)
Cirrhosis				0.424
Negative	28 (18.1)	17 (25.4)	12 (18.2)
Positive	127 (81.9)	50 (74.6)	54 (81.8)
Platelets (× 109/L)				0.236
> 100	62 (40.0)	35 (52.2)	28 (42.4)
≤ 100	93 (60.0)	32 (47.8)	38 (57.6)
Alanine transferase (U/L)				0.806
≤ 50	115 (74.2)	47 (70.1)	49 (74.2)
> 50	40 (25.8)	20 (29.9)	17 (25.8)
Aspartate transferase (U/L)				0.784
> 40	68 (43.9)	27 (40.3)	26 (39.4)
≤ 40	87 (56.1)	40 (59.7)	40 (60.6)
Gammaglutamyl transferase (U/L)				0.606
≤ 60	85 (54.8)	38 (56.7)	41 (62.1)
> 60	70 (45.2)	29 (43.3)	25 (37.9)
Total bilirubin (umol/L)				0.07
≤ 21	88 (56.8)	47 (70.1)	46 (69.7)
> 21	67 (43.2)	20 (29.9)	20 (30.3)
Activated partial thromboplastin time (second)				0.207
< 42	130 (83.9)	62 (92.5)	58 (87.9)
≥ 42	25 (16.1)	5 (7.46)	8 (12.1)
Fibrinogen (g/L)				0.075
≥ 2	90 (58.1)	42 (62.7)	49 (74.2)
< 2	65 (41.9)	25 (37.3)	17 (25.8)
International normalized ratio				0.265
≤ 1.2	101 (65.2)	51 (76.1)	44 (66.7)
> 1.2	54 (34.8)	16 (23.9)	22 (33.3)
Hepatitis B surface antigen				0.313
Negative	28 (18.1)	13 (19.4)	7 (10.6)
Positive	127 (81.9)	54 (80.6)	59 (89.4)
Alpha fetoprotein (ng/mL)				0.374
≤ 400	131 (84.5)	53 (79.1)	58 (87.9)
> 400	24 (15.5)	14 (20.9)	8 (12.1)
Size (cm)	2.7 ± 1.05	2.70 ± 1.10	2.57 ± 0.90	0.586
Early recurrence				0.642
No	93 (60.0)	41 (61.2)	44 (66.7)
Yes	62 (40.0)	26 (38.8)	22 (33.3)

Table 2 The six best models with optimal machine learning approach and their performance in training and internal validation set

Model	Training set				Internal-Validation set
	Accuracy	AUC (95%CI)	NPV	PPV	Accuracy	AUC (95%CI)	NPV	PPV
The tumor-only model (KNN)	0.690	0.757 (0.724-0.789)	0.532	0.796	0.731	0.750 (0.700-0.801)	0.654	0.780
The 5 mm peri-tumor model (LightGBM)	0.768	0.857 (0.824-0.891)	0.629	0.860	0.746	0.713 (0.665-0.761)	0.577	0.854
The 10 mm peri-tumor model (KNN)	0.723	0.802 (0.776-0.829)	0.548	0.839	0.776	0.803 (0.763-0.842)	0.577	0.902
The tumor-peri-tumor 5 mm model (extreme gradient boosting)	0.935	0.987 (0.934-1.000)	0.887	0.968	0.701	0.774 (0.696-0.853)	0.654	0.732
The tumor-peri-tumor 10 mm model (logistic regression)	0.781	0.862 (0.821-0.904)	0.774	0.785	0.761	0.841 (0.777-0.904)	0.692	0.805
The model that incorporates features from all regions—tumor, 5 mm, and 10 mm (LightGBM)	0.832	0.924 (0.890-0.958)	0.726	0.903	0.776	0.899 (0.850-0.948)	0.615	0.878

AUC: Area under the receiver operating characteristic curve; KNN: K-nearest neighbors; LightGBM: Light gradient boosting machine; NPV: Negative predictive value; PPV: Positive predictive value.

Table 3 Comparative analysis of models in internal validation set

Model 1	Model 2	Net reclassification improvement	P value	Integrated discrimination improvement	P value
The tumor-only model	Combined	0.261	0.021	0.493	< 0.0001
The 5 mm peri-tumor model	Combined	0.321	0.002	0.353	< 0.0001
The 10 mm peri-tumor model	Combined	0.292	0	0.166	0.00033
The tumor-peri-tumor 5 mm model	Combined	0.189	0.049	0.166	0.00052
The tumor-peri-tumor 10 mm model	Combined	0.220	0.02	0.231	0.00311

Combined: The model that incorporates features from all regions—tumor, 5 mm, and 10 mm.