Computed tomography radiomics-based machine learning nomogram for preoperative prediction of glypican-3 expression in hepatocellular carcinoma

Table 1 Comparison of clinical and imaging characteristics between the training and testing cohorts, n (%)/mean ± SD

Demographics	Training set (n = 72)		P value	Testing set (n = 31)		P value	P value
	GPC3 (+)	GPC3 (-)		GPC3 (+)	GPC3 (-)
Age	55.05 ± 10.69	57.21 ± 9.08	0.377	55.14 ± 9.25	52.50 ± 3.54	0.695	0.662
CEA (ng/mL)	10.31 ± 42.90	2.76 ± 1.65	0.357	4.75 ± 6.70	1.94 ± 0.83	0.563	0.647
Sex			1.000			0.100	0.421
Male	37 (84.09)	23 (82.14)		23 (79.31)	-
Female	7 (15.91)	5 (17.86)		6 (20.69)	2 (100.00)
Viral hepatitis			0.229			1.000	0.639
0	31 (70.45)	15 (53.57)		21 (72.41)	1 (50.00)
1	13 (29.55)	13 (46.43)		8 (27.59)	1 (50.00)
TBIL (μmol/L)			< 0.001			0.897	0.057
0	11 (25.00)	20 (71.43)		20 (68.97)	2 (100.00)
1	33 (75.00)	8 (28.57)		9 (31.03)	-
DBIL (μmol/L)			0.137			0.749	0.530
0	16 (36.36)	16 (57.14)		11 (37.93)	-
1	28 (63.64)	12 (42.86)		18 (62.07)	2 (100.00)
IBIL (μmol/L)			0.727			0.100	0.070
0	22 (50.00)	16 (57.14)		23 (79.31)	-
1	22 (50.00)	12 (42.86)		6 (20.69)	2 (100.00)
ALT (U/L)			0.758			0.749	0.015
0	27 (61.36)	19 (67.86)		11 (37.93)	-
1	17 (38.64)	9 (32.14)		18 (62.07)	2 (100.00)
AST (U/L)			0.448			0.897	0.017
0	23 (52.27)	18 (64.29)		9 (31.03)	-
1	21 (47.73)	10 (35.71)		20 (68.97)	2 (100.00)
TP (g/L)			0.163			1.000	0.397
0	30 (68.18)	24 (85.71)		19 (65.52)	1 (50.00)
1	14 (31.82)	4 (14.29)		10 (34.48)	1 (50.00)
ALB (g/L)			1.000			1.000	0.300
0	31 (70.45)	20 (71.43)		17 (58.62)	1 (50.00)
1	13 (29.55)	8 (28.57)		12 (41.38)	1 (50.00)
GLB (g/L)			0.638			0.724	0.704
0	38 (86.36)	26 (92.86)		25 (86.21)	1 (50.00)
1	6 (13.64)	2 (7.14)		4 (13.79)	1 (50.00)
AFP (ng/mL)			< 0.001			0.327	0.684
0	13 (29.55)	22 (78.57)		11 (37.93)	2 (100.00)
1	31 (70.45)	6 (21.43)		18 (62.07)	-
Liver cirrhosis			0.229			1.000	1.000
0	13 (29.55)	13 (46.43)		10 (34.48)	1 (50.00)
1	31 (70.45)	15 (53.57)		19 (65.52)	1 (50.00)
Ascites			0.597			0.724	0.280
0	2 (4.55)	3 (10.71)		4 (13.79)	1 (50.00)
1	42 (95.45)	25 (89.29)		25 (86.21)	1 (50.00)
Portal vein tumor thrombus			1.000			0.449	0.940
0	5 (11.36)	4 (14.29)		2 (6.90)	1 (50.00)
1	39 (88.64)	24 (85.71)		27 (93.10)	1 (50.00)

GPC3: Glypican-3; TBIL: Total bilirubin; DBIL: Direct bilirubin; IBIL: Indirect bilirubin; ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; TP: Total protein; ALB: Albumin; GLB: Globulin; AFP: Alpha-fetoprotein; CEA: Carcinoembryonic antigen.

Table 2 Final selected computed tomography radiomics features

Feature category and count	Radiomics feature name
Shape features (2)	original _ shape _ Sphericity _ P
	original _ shape _ Sphericity _ V
First-order features (3)	wavelet _ HLH _ firstorder _ Median _ P
	wavelet _ HHL _ firstorder _ Median _ V
	wavelet _ HHH _ firstorder _ Mean _ N
Texture features (5)	Wavelet _ LHL _ gldm _ DependenceEntropy _ V
	wavelet _ HLH _ glszm _ LowGrayLevelZoneEmphasis _ A
	wavelet _ LLH _ glszm _ GrayLevelNonUniformityNormalized _ V
	Wavelet _ LHL _ glcm _ ldn _ N
	wavelet _ HHH _ glszm _ ZoneEntropy _ N

N: Non-contrast phase; A: Arterial phase; P: Portal venous phase; V: Delayed phase.

Table 3 Performance of radiomics models for predicting glypican-3 expression status in hepatocellular carcinoma

Set	Model	AUC	95%CI	Sensitivity	Specificity	PPV	NPV	Accuracy
Training set	LR	0.889	0.814-0.963	0.643	0.932	0.857	0.804	0.819
	SVM	0.949	0.903-0.995	0.857	0.932	0.889	0.911	0.903
	KNN	0.853	0.773-0.934	0.750	0.773	0.677	0.829	0.764
	RF	0.959	0.921-0.997	0.964	0.818	0.771	0.973	0.875
	ET	0.953	0.900-1.000	0.893	0.909	0.862	0.930	0.903
	XGBoost	1.000	1.000-1.000	0.964	1.000	1.000	0.978	0.986
	LightGBM	0.933	0.879-0.987	0.857	0.841	0.774	0.902	0.847
	MLP	0.900	0.827-0.973	0.786	0.864	0.786	0.864	0.833
Testing set	LR	0.759	0.277-1.000	0.500	0.517	0.067	0.937	0.516
	SVM	0.707	0.190-1.000	0.000	0.966	0.000	0.933	0.903
	KNN	0.586	0.000-1.000	0.000	1.000	0.000	0.935	0.935
	RF	0.862	0.638-1.000	0.705	0.759	0.725	0.957	0.742
	ET	0.819	0.515-1.000	0.500	0.690	0.100	0.952	0.677
	XGBoost	0.759	0.277-1.000	0.500	0.517	0.067	0.937	0.516
	LightGBM	0.741	0.291-1.000	0.500	0.552	0.071	0.941	0.548
	MLP	0.655	0.000-1.000	0.000	1.000	0.000	0.935	0.935

LR: Logistic regression; SVM: Support vector machine; KNN: K-nearest neighbors; RF: Random forest; ET: Extra trees; MLP: Multilayer perceptron; AUC: Area under the curve; PPV: Positive predictive value; NPV: Negative predictive value; XGBoost: Extreme gradient boosting; LightGBM: Light Gradient Boosting Machine.

Table 4 Univariate and multivariate analysis of clinical and computed tomography imaging features

Characteristics	Univariate analysis			Multivariate analysis
	OR	95%CI	P value	OR	95%CI	P value
Age	1.005	0.995-1.015	0.377
CEA (ng/mL)	0.998	0.995-1.001	0.357
Sex	1.034	0.797-1.342	0.832
Viral hepatitis	1.190	0.975-1.452	0.150
TBIL (μmol/L)	0.638	0.535-0.759	< 0.001	0.636	0.549-0.738	< 0.001
DBIL (μmol/L)	0.819	0.676-0.991	0.086
IBIL (μmol/L)	0.934	0.770-1.134	0.560
ALT (U/L)	0.935	0.764-1.145	0.582
AST (U/L)	0.890	0.733-1.081	0.322
TP (g/L)	0.801	0.643-0.998	0.097
ALB (g/L)	0.989	0.799-1.224	0.931
GLB (g/L)	0.855	0.629-1.163	0.400
AFP (ng/mL)	0.627	0.529-0.744	< 0.001	0.626	0.541-0.725	< 0.001
Liver cirrhosis	0.840	0.689-1.025	1.150
Ascites	0.797	0.546-1.165	0.322
Portal vein tumor thrombus	0.938	0.700-1.259	0.719

OR: Odds ratio; CEA: Carcinoembryonic antigen; TBIL: Total bilirubin; DBIL: Direct bilirubin; IBIL: Indirect bilirubin; ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; TP: Total protein; ALB: Albumin; GLB: Globulin; AFP: Alpha-fetoprotein.

Table 5 Performance of the combined model for predicting glypican-3 expression status in hepatocellular carcinoma

Set	Model	AUC	95%CI	Sensitivity	Specificity	PPV	NPV	Accuracy
Training set	Clinic	0.861	0.783-0.940	0.500	0.977	0.933	0.754	0.792
	ALL	0.959	0.921-0.997	0.964	0.818	0.771	0.973	0.875
	Nomogram	0.979	0.955-1.000	0.964	0.864	0.818	0.974	0.903
Testing set	Clinic	0.845	0.759-0.931	0.495	0.688	0.599	0.935	0.935
	ALL	0.862	0.638-1.000	0.705	0.759	0.725	0.957	0.742
	Nomogram	0.948	0.832-1.000	0.815	0.897	0.801	0.963	0.871

Clinic: Clinical model; ALL: Random forest radiomics model; Nomogram: Combined model; AUC: Area under the curve; PPV: Positive predictive value; NPV: Negative predictive value.

Table 6 Comparison of net reclassification improvement and integrated discrimination improvement among the clinical model, random forest radiomics model, and combined model

Comparisons between models	Training set		Testing set
	NRI	IDI	NRI	IDI
Nomogram vs clinic	0.263	0.071	-0.034	-0.046
Nomogram vs ALL	0.032	0.048	0.397	0.166
ALL vs clinic	0.231	0.023	-0.431	-0.211

Clinic: Clinical model; ALL: Random forest radiomics model; Nomogram: Combined model; NRI: Net reclassification improvement; IDI: Integrated discrimination improvement.