Construction and efficacy validation of a multi-parametric contrast-enhanced ultrasound nomogram model for discriminating hepatic inflammatory lesions from malignancies

Table 1 Comparative baseline measurements across datasets

Variable	Total	Training set (n = 373)	Validation set (n = 124)	Test set (n = 124)	Statistic	P value
Sex					0.464	0.793
Male	417 (67.15)	250 (67.02)	81 (65.32)	86 (69.35)
Female	204 (32.85)	123 (32.98)	43 (34.68)	38 (30.65)
Diabetes history					4.847	0.089
Yes	69 (11.11)	33 (8.85)	18 (14.52)	18 (14.52)
No	552 (88.89)	340 (91.15)	106 (85.48)	106 (85.48)
Hypertension history					4.351	0.114
Yes	100 (16.10)	51 (13.67)	23 (18.55)	26 (20.97)
No	521 (83.90)	322 (86.33)	101 (81.45)	98 (79.03)
Echocardiographic features					1.083	0.582
Mixed	358 (57.65)	216 (57.91)	67 (54.03)	75 (60.48)
Other	263 (42.35)	157 (42.09)	57 (45.97)	49 (39.52)
Lesion location					0.010	0.995
Left lobe	497 (80.03)	299 (80.16)	99 (79.84)	99 (79.84)
Right lobe	124 (19.97)	74 (19.84)	25 (20.16)	25 (20.16)
Border clarity					1.055	0.590
Ill-defined	456 (73.43)	275 (73.73)	87 (70.16)	94 (75.81)
Well-defined	165 (26.57)	98 (26.27)	37 (29.84)	30 (24.19)
Morphology					2.515	0.284
Irregular	361 (58.13)	219 (58.71)	77 (62.10)	65 (52.42)
Regular	260 (41.87)	154 (41.29)	47 (37.90)	59 (47.58)
Homogeneous enhancement					0.709	0.701
Yes	390 (62.80)	236 (63.27)	80 (64.52)	74 (59.68)
No	231 (37.20)	137 (36.73)	44 (35.48)	50 (40.32)
Cirrhosis					0.158	0.924
Yes	226 (36.39)	134 (35.92)	45 (36.29)	47 (37.90)
No	395 (63.61)	239 (64.08)	79 (63.71)	77 (62.10)
Hepatitis					3.419	0.181
Yes	477 (76.81)	296 (79.36)	91 (73.39)	90 (72.58)
No	144 (23.19)	77 (20.64)	33 (26.61)	34 (27.42)
Blood flow signals					4.104	0.663
0	221 (35.59)	132 (35.39)	41 (33.06)	48 (38.71)
1	153 (24.64)	100 (26.81)	27 (21.77)	26 (20.97)
2	157 (25.28)	90 (24.13)	37 (29.84)	30 (24.19)
3	90 (14.49)	51 (13.67)	19 (15.32)	20 (16.13)
Calcification					0.832	0.660
Yes	303 (48.79)	178 (47.72)	65 (52.42)	60 (48.39)
No	318 (51.21)	195 (52.28)	59 (47.58)	64 (51.61)
Age (year)	57.00 (51.00, 62.00)	57.00 (51.00, 63.00)	57.00 (52.00, 63.00)	56.50 (51.00, 61.00)	2.096	0.351
Body mass index (kg/m2)	23.01 ± 1.99	22.96 ± 2.01	22.96 ± 1.92	23.18 ± 2.00	0.607	0.545
Lesion size (cm)	4.10 (2.30, 6.00)	3.80 (2.10, 5.90)	4.50 (2.70, 6.03)	4.05 (2.60, 6.05)	3.948	0.139
Wash-in time (second)	23.00 (19.00, 27.00)	23.00 (19.00, 27.00)	22.50 (20.00, 25.00)	22.00 (19.00, 27.00)	2.816	0.245
Wash-out time (second)	135.00 (72.00, 212.00)	137.00 (72.00, 210.00)	135.50 (88.00, 211.75)	127.00 (64.50, 215.50)	0.666	0.717
AFP	9.55 (6.17, 17.97)	9.70 (6.20, 17.27)	9.13 (5.74, 18.25)	9.36 (6.14, 18.41)	0.114	0.945
MELD score	18.79 (15.78, 22.45)	18.85 (15.98, 22.33)	19.05 (15.14, 23.01)	18.59 (15.68, 22.23)	0.818	0.664

Descriptive statistics of continuous variables are expressed using the mean ± SD (normal distribution) or median (quartile; skewed distribution); categorical variables are shown as n (%). AFP: Alpha-fetoprotein; MELD: Model for End-Stage Liver Disease.

Table 2 Baseline characteristics of patients in the training group

Variable	Total	Training set (n = 190)	Validation set (n = 183)	Statistic	P value
Sex				2.149	0.143
Male	250 (67.02)	134 (70.53)	116 (63.39)
Female	123 (32.98)	56 (29.47)	67 (36.61)
Diabetes history				1.050	0.305
Yes	33 (8.85)	14 (7.37)	19 (10.38)
No	340 (91.15)	176 (92.63)	164 (89.62)
Hypertension history				0.087	0.768
Yes	51 (13.67)	25 (13.16)	26 (14.21)
No	322 (86.33)	165 (86.84)	157 (85.79)
Echocardiographic features				1.112	0.292
Mixed	216 (57.91)	105 (55.26)	111 (60.66)
Other	157 (42.09)	85 (44.74)	72 (39.34)
Lesion location				1.487	0.223
Left lobe	299 (80.16)	157 (82.63)	142 (77.60)
Right lobe	74 (19.84)	33 (17.37)	41 (22.40)
Border clarity				0.472	0.492
Ill-defined	275 (73.73)	143 (75.26)	132 (72.13)
Well-defined	98 (26.27)	47 (24.74)	51 (27.87)
Morphology				5.797	0.016
Irregular	219 (58.71)	123 (64.74)	96 (52.46)
Regular	154 (41.29)	67 (35.26)	87 (47.54)
Homogeneous enhancement				55.858	< 0.001
Yes	236 (63.27)	155 (81.58)	81 (44.26)
No	137 (36.73)	35 (18.42)	102 (55.74)
Cirrhosis				69.952	< 0.001
Yes	134 (35.92)	107 (56.32)	27 (14.75)
No	239 (64.08)	83 (43.68)	156 (85.25)
Hepatitis				35.315	< 0.001
Yes	296 (79.36)	174 (91.58)	122 (66.67)
No	77 (20.64)	16 (8.42)	61 (33.33)
Blood flow signals				111.642	< 0.001
0	132 (35.39)	20 (10.53)	112 (61.20)
1	100 (26.81)	61 (32.11)	39 (21.31)
2	90 (24.13)	72 (37.89)	18 (9.84)
3	51 (13.67)	37 (19.47)	14 (7.65)
Calcification				42.207	< 0.001
Yes	178 (47.72)	122 (64.21)	56 (30.60)
No	195 (52.28)	68 (35.79)	127 (69.40)
Age (year)	57.41 ± 8.26	59.34 ± 9.16	55.41 ± 6.68	-4.718	< 0.001
Body mass index (kg/m2)	22.96 ± 2.01	22.97 ± 1.98	22.96 ± 2.04	-0.032	0.974
Lesion size (cm)	3.80 (2.10, 5.90)	4.95 (2.50, 7.38)	3.20 (1.90, 4.70)	5.105	< 0.001
Wash-in time (second)	22.99 ± 5.25	21.24 ± 5.27	24.81 ± 4.59	6.963	< 0.001
Wash-out time (second)	137.00 (72.00, 210.00)	117.00 (64.00, 162.75)	183.00 (95.50, 252.00)	5.512	< 0.001
AFP	9.70 (6.20, 17.27)	17.07 (10.87, 23.52)	6.50 (4.38, 8.82)	13.616	< 0.001
MELD score	18.85 (15.98, 22.33)	21.88 (18.77, 25.53)	16.34 (13.64, 18.98)	11.503	< 0.001

Descriptive statistics of continuous variables are expressed using the mean ± SD (normal distribution) or median (quartile; skewed distribution); categorical variables are shown as n (%). AFP: Alpha-fetoprotein; MELD: Model for End-Stage Liver Disease.

Table 3 Subgroup analysis by cirrhosis status (test set)

Model	Subgroup	n	AUC (95%CI)	Sensitivity	Specificity	Accuracy
Logistic	Cirrhosis	47	0.979 (0.947-1.000)	0.886	1.000	0.915
	Non-cirrhosis	77	0.943 (0.876-1.000)	0.875	0.981	0.948
DT	Cirrhosis	47	0.937 (0.843-1.000)	0.914	0.917	0.915
	Non-cirrhosis	77	0.774 (0.662-0.885)	0.750	0.830	0.805
RF	Cirrhosis	47	0.993 (0.977-1.000)	0.971	1.000	0.979
	Non-cirrhosis	77	0.938 (0.878-0.997)	0.750	1.000	0.922
XGBoost	Cirrhosis	47	0.974 (0.932-1.000)	0.971	0.917	0.957
	Non-cirrhosis	77	0.972 (0.943-1.000)	0.958	0.887	0.909
SVM	Cirrhosis	47	0.979 (0.944-1.000)	0.971	0.917	0.957
	Non-cirrhosis	77	0.940 (0.868-1.000)	0.875	0.981	0.948

AUC: Area under the curve; SVM: Support vector machine; RF: Random forest; DT: Decision tree.

Table 4 Subgroup analysis by lesion size (test set)

Model	Subgroup (cm)	n	AUC (95%CI)	Sensitivity	Specificity	Accuracy
Logistic	Large (≥ 4.0)	62	0.983 (0.955-1.000)	0.906	1.000	0.952
	Small (< 4.0)	62	0.950 (0.891-1.000)	0.889	0.971	0.936
DT	Large (≥ 4.0)	62	0.887 (0.805-0.969)	0.875	0.833	0.855
	Small (< 4.0)	62	0.882 (0.795-0.968)	0.815	0.857	0.839
RF	Large (≥ 4.0)	62	0.984 (0.963-1.000)	0.938	0.967	0.952
	Small (< 4.0)	62	0.956 (0.906-1.000)	0.815	1.000	0.919
XGBoost	Large (≥ 4.0)	62	0.988 (0.968-1.000)	0.969	0.967	0.968
	Small (< 4.0)	62	0.971 (0.940-1.000)	0.963	0.886	0.919
SVM	Large (≥ 4.0)	62	0.983 (0.959-1.000)	0.938	0.967	0.952
	Small (< 4.0)	62	0.950 (0.886-1.000)	0.889	0.971	0.936

Median lesion size = 4.0 cm was used as the cut-off value. AUC: Area under the curve; SVM: Support vector machine; RF: Random forest; DT: Decision tree.