Optimal 10-mm window: Integrating peri-ablation radiomics with preoperative features to predict early hepatocellular carcinoma recurrence after thermal ablation

Table 1 Characteristics of the included patients, n (%)/mean ± SD

	Training set			P value	Internal validation set (n = 72)	P value	External validation set (n = 61)	P value
	Total (n = 168)	ER (n = 67)	Non-ER (n = 101)
Patient characteristic
Age (years)	56.54 ± 9.56	56.15 ± 9.73	56.79 ± 9.48	0.671	56.28 ± 8.73	0.84	54.95 ± 9.34	0.266
Sex				0.874		0.215		0.173
Male	123 (73.2)	48 (71.6)	75 (74.3)		47 (65.3)		50 (65.3)
Female	45 (26.8)	19 (28.4)	26 (25.7)		25 (34.7)		11 (34.7)
Etiology				0.147		0.915		0.934
HBV	155 (92.3)	64 (95.5)	91 (90.1)		67 (93.1)		57 (93.4)
HCV	9 (5.4)	3 (4.5)	6 (5.9)		3 (4.2)		3 (4.9)
Others	4 (2.4)	0 (0.0)	4 (4.0)		2 (2.8)		1 (1.6)
Tumor characteristics
Tumor size (cm)	2.39 ± 0.91	2.72 ± 0.91	2.18 ± 0.91	< 0.001	2.33 ± 0.81	0.651	2.46 ± 0.97	0.612
No. of tumors				< 0.001		0.705		0.516
Solitary	123 (73.2)	39 (58.2)	84 (83.2)		51 (70.8)		42 (68.9)
Multiple	45 (26.8)	28 (41.8)	17 (16.8)		21 (29.2)		19 (31.1)
Barcelona Clinic Liver Cancer stage				< 0.001		0.702		0.862
0	46 (27.4)	5 (7.5)	41 (40.6)		18 (25.0)		16 (26.2)
A	122 (72.6)	62 (92.5)	60 (59.4)		54 (75.0)		45 (73.8)
Laboratory index
AST, IU/L	36.86 ± 5.25	37.00 ± 5.38	36.76 ± 5.19	0.779	36.25 ± 5.22	0.408	35.19 ± 5.41	0.036
ALT, IU/L	31.01 ± 21.78	34.42 ± 23.96	28.76 ± 20.01	0.099	33.12 ± 25.71	0.517	28.23 ± 15.93	0.362
TBIL, μmol/L	24.59 ± 13.65	25.63 ± 14.80	23.89 ± 12.87	0.423	21.34 ± 11.74	0.08	21.44 ± 12.96	0.12
ALB, g/L	30.78 ± 12.66	31.36 ± 10.08	30.40 ± 14.14	0.634	33.43 ± 20.90	0.229	32.74 ± 10.53	0.28
r-GGT (U/L)	35.78 ± 26.68	38.60 ± 31.92	33.91 ± 22.52	0.266	32.01 ± 18.89	0.278	40.42 ± 29.96	0.261
PLT, × 109/L	110.54 ± 55.67	106.62 ± 51.70	113.14 ± 58.14	0.459	112.81 ± 56.08	0.774	104.41 ± 54.45	0.459
Child-Pugh grade				0.249		0.975		0.204
A	121 (72.0)	47 (70.1)	74 (73.3)		52 (72.2)		49 (80.3)
B	47 (29.0)	20 (29.9)	27 (26.7)		20 (27.8)		12 (19.7)
AFP, ng/mL	215.98 ± 351.69	329.83 ± 440.58	140.44 ± 252.97	0.001	187.27 ± 331.25	0.556	294.55 ± 378.51	0.144

ER: Early recurrence; HBV: Hepatitis B virus; HCV: Hepatitis C virus; AST: Aspartate aminotransferase; ALT: Alanine aminotransferase; TBIL: Total bilirubin; ALB: Albumin; r-GGT: Gamma-glutamyl transferase; PLT: Platelet; AFP: Alpha-fetoprotein.

Table 2 Predictive performance of seven machine learning models of all classifiers across different feature sets, area under the curve (95% confidence interval)

		KNN	SVM	LR	DT	GBDT	RF	XGBoost
Clinical + rad-preop	Training set	0.966 (0.947-0.984)	0.928 (0.893-0.964)	0.941 (0.91-0.973)	0.757 (0.701-0.814)	0.973 (0.954-0.992)	0.909 (0.871-0.946)	0.987 (0.974-1)
	Internal validation set	0.76 (0.631-0.888)	0.848 (0.936-0.98)	0.867 (0.772-0.963)	0.756 (0.64-0.87)	0.845 (0.799-0.892)	0.76 (0.636-0.884)	0.864 (0.817-0.91)
	External validation set	0.752 (0.622-0.883)	0.849 (0.747-0.951)	0.84 (0.735-0.945)	0.716 (0.597-0.836)	0.825 (0.715-0.935)	0.752 (0.625-0.878)	0.83 (0.72-0.939)
Clinical + rad-preop + peri-necrotic (5 mm)	Training set	0.97 (0.954-0.987)	0.957 (0.933-0.982)	0.963 (0.94-0.986)	0.757 (0.701-0.814)	0.986 (0.974-0.999)	0.917 (0.88-0.953)	0.993 (0.984-1)
	Internal validation set	0.938 (0.911-0.964)	0.894 (0.852-0.935)	0.865 (0.819-0.911)	0.751 (0.618-0.883)	0.933 (0.898-0.967)	0.839 (0.788-0.89)	0.839 (0.928-0.985)
	External validation set	0.81 (0.692-0.928)	0.863 (0.761-0.965)	0.859 (0.752-0.966)	0.716 (0.597-0.836)	0.819 (0.706-0.932)	0.824 (0.709-0.94)	0.816 (0.702-0.93)
Clinical + rad-preop + peri-necrotic (10 mm)	Training set	0.952 (0.929-0.975)	0.942 (0.912-0.972)	0.95 (0.921-0.979)	0.757 (0.701-0.814)	0.973 (0.953-0.993)	0.913 (0.875-0.951)	0.987 (0.975-1)
	Internal validation set	0.833 (0.717-0.949)	0.898 (0.859-0.936)	0.898 (0.859-0.937)	0.736 (0.678-0.794)	0.907 (0.878-0.943)	0.803 (0.688-0.919)	0.904 (0.869-0.939)
	External validation set	0.818 (0.7-0.935)	0.876 (0.784-0.969)	0.873 (0.775-0.971)	0.716 (0.597-0.836)	0.871 (0.773-0.969)	0.795 (0.674-0.917)	0.855 (0.749-0.961)
Clinical + rad-preop + peri-necrotic (15 mm)	Training set	0.96 (0.94-0.98)	0.941 (0.908-0.974)	0.95 (0.919-0.981)	0.757 (0.701-0.814)	0.976 (0.958-0.994)	0.913 (0.876-0.95)	0.985 (0.97-1)
	Internal validation set	0.927 (0.893-0.96)	0.875 (0.784-0.967)	0.873 (0.777-0.9969)	0.725 (0.599-0.851)	0.913 (0.876-0.951)	0.833 (0.72-0.946)	0.839 (0.74-0.937)
	External validation set	0.851 (0.747-0.954)	0.863 (0.763-0.962)	0.856 (0.754-0.958)	0.716 (0.597-0.836)	0.84 (0.734-0.946)	0.815 (0.7-0.93)	0.833 (0.727-0.939)
Clinical + rad-preop + peri-necrotic (20 mm)	Training set	0.971 (0.955-0.987)	0.986 (0.974-0.997)	0.958 (0.934-0.982)	0.767 (0.711-0.823)	0.927 (0895-0.959)	0.918 (0.885-0.952)	0.995 (0.99-1)
	Internal validation set	0.771 (0.712-0.831)	0.792 (0.67-0.913)	0.79 (0.732-0.849)	0.742 (0.684-0.8)	0.801 (0.676-0.926)	0.81 (0.691-0.929)	0.834 (0.72-0.948)
	External validation set	0.717 (0.583-0.851)	0.795 (0.676-0.915)	0.773 (0.645-0.9)	0.664 (0.537-0.791)	0.763 (0.637-0.889)	0.753 (0.625-0.881)	0.782 (0.654-0.91)

KNN: K-nearest neighbors; SVM: Support vector machine; LR: Logistic regression; DT: Decision tree; GBDT: Gradient boosting decision tree; RF: Random forest; XGBoost: Extreme gradient boosting.

Table 3 Comparative performance of the optimal preoperative clinical-radiomic model vs optimal models incorporating peri-ablation zones features, area under the curve (95% confidence interval)

		AUC	Sensitivity	Specificity	PPV	NPV	Accuracy	F1 score
Clinical + rad-preop	Training set	0.928 (0.893-0.964)	0.934 (0.882-0.964)	0.809 (0.715-0.877)	0.764 (0.713-0.832)	0.949 (0.803-0.968)	0.859	0.841
	Internal validation set	0.848 (0.742-0.955)	0.842 (0.696-0.926)	0.739 (0.535-0.875)	0.554 (0.503-0.773)	0.924 (0.835-0.975)	0.768	0.668
	External validation set	0.849 (0.747-0.951)	0.763 (0.608-0.87)	0.826 (0.629-0.93)	0.707 (0.698-0.847)	0.864 (0.747-0.897)	0.804	0.734
Clinical + rad-preop + peri-necrotic (5 mm)	Training set	0.957 (0.933-0.982)	0.921 (0.866-0.954)	0.888 (0.805-0.938)	0.845 (0.791-0.903)	0.944 (0.803-0.965)	0.901	0.881
	Internal validation set	0.894 (0.852-0.9935)	0.821 (0.752-0.874)	0.831 (0.74-0.895)	0.651 (0.613-0.734)	0.924 (0.839-0.979)	0.828	0.726
	External validation set	0.863 (0.761-0.965)	0.816 (0.666-0.908)	0.783 (0.581-0.903)	0.674 (0.615-0.839)	0.886 (0.724-0.927)	0.803	0.795
Clinical + rad-preop + peri-necrotic (10 mm)	Training set	0.942 (0.912-0.972)	0.894 (0.835-0.934)	0.854 (0.766-0.913)	0.803 (0.782-0.901)	0.924 (0.836-0.974)	0.87	0.846
	Internal validation set	0.898 (0.859-0.936)	0.815 (0.745-0.868)	0.843 (0.753-0.904)	0.666 (0.612-0.788)	0.922 (0.874-0.965)	0.835	0.733
	External validation set	0.876 (0.784-0.969)	0.947 (0.827-0.985)	0.652 (0.449-0.812)	0.59 (0.543-0.705)	0.957 (0.857-0.997)	0.757	0.734
Clinical + rad-preop + peri-necrotic (15 mm)	Training set	0.941 (0.908-0.974)	0.907 (0.85-0.944)	0.854 (0.766-0.913)	0.805 (0.743-0.849)	0.933 (0.856-0.969)	0.875	0.853
	Internal validation set	0.875 (0.784-0.967)	0.789 (0.637-0.889)	0.826 (0.629-0.93)	0.636 (0.612-0.782)	0.911 (0.815-0.941)	0.816	0.704
	External validation set	0.863 (0.763-0.962)	0.868 (0.727-0.942)	0.783 (0.581-0.903)	0.688 (0.627-0.742)	0.915 (0.831-0.958)	0.813	0.768
Clinical + rad-preop + peri-necrotic (20 mm)	Training set	0.986 (0.974-0.997)	0.94 (0.891-0.968)	0.955 (0.889-0.982)	0.933 (0.867-0.989)	0.960 (0.826-0.987)	0.949	0.937
	Internal validation set	0.792 (0.67-0.913)	0.868 (0.727-0.942)	0.696 (0.491-0.844)	0.523 (0.501-0.713)	0.932 (0.849-0.987)	0.744	0.653
	External validation set	0.795 (0.676-0.915)	0.658 (0.499-0.788)	0.864 (0.667-0.953)	0.727 (0.701-0.863)	0.821 (0.723-0.895)	0.791	0.691

Data are 95% confidence intervals in parentheses.

AUC: Area under the curve; PPV: Positive predictive value; NPV: Negative predictive value.