The Feasibility of Using Tri-Exponential Intra-Voxel Incoherent Motion DWI for Identifying the Microvascular Invasion in Hepatocellular Carcinoma

Figures & data

Table 1 Clinical Characteristics

Characteristics	No of Patient (n = 67)
Gender
Male	59 (88.1%)
Female	8 (11.9%)
Age (y)	57.6±10.4 (Min: 33.0, Max: 83.0)
Etiology of liver disease
Hepatitis B virus	67 (100.0%)
Hepatitis C virus	3 (4.5%)
α-fetoprotein
≥ 20 ng/mL	41 (61.2%)
< 20 ng/mL	26 (38.8%)
Tumor Size (cm)	4.87±3.27 (Min: 1.0, Max: 15.0)
Degree of Differentiation
Well and medium	47 (70.1%)
Poor	20 (29.9%)
Fibrosis
With	33 (49.3%)
Without	34 (50.7%)
Total bilirubin (TBiL)
>20.4 μmol/L	9 (13.4%)
≤20.4 μmol/L	58 (86.6%)
Direct Bilirubin (DBiL)
>6.8 μmol/	9 (13.4%)
≤6.8 μmol/L	58 (86.6%)
Alanine Aminotransferase (ALT)
>35 U/L	16 (23.9%)
≤35 U/L	51 (76.1%)
Aspartate Aminotransferase (AST)
>40 U/L	12 (17.9%)
≤40 U/L	55 (82.1%)
γ-glutamyl transferase (γGT)
>60 U/L	22 (32.8%)
≤60 U/L	45 (67.2%)
Carcinoembryonic Antigen (CEA)
≥5 ng/mL	8 (11.9%)
<5 ng/mL	59 (88.1%)
Carbohydrate Antigen 19–9 (CA199)
≥37 ng/mL	6 (9.0%)
<37 ng/mL	61 (91.0%)
MVI
MVI-Positive	29 (43.3%)
MVI-Negative	38 (56.7%)

Figure 1 The flowchart shows the inclusion and exclusion of subjects.

Figure 2 Representative MRI images including the T2WI, T1WI, DWI of b = 100 s/mm² and bi-IVIM-derived parametric maps (f_p, D_t and D_p) as well as tri-IVIM-derived parametric maps (f_s, f_f, f_vf, D_s, D_f and D_vf) of a patient with MVI-negative HCC.

Notes: It is important to note that the parametric pseudo-color maps of the segmented tumors were superimposed on the DWI images with a b-value of 500 s/mm². All pseudo-color maps utilize the same color bar, which ranges from blue to red. The following ranges apply to different parameters: f_p, f_s, f_f, and f_vf range from 0.0 to 1.0; D_t and D_s range from 0.0 to 5×10⁻³ mm²/s; D_p and D_f range from 0.0 to 200×10⁻³ mm²/s; D_vf ranges from 0.0 to 10,000×10⁻³ mm²/s.

Figure 3 Representative MRI images including the T2WI, T1WI, DWI of b = 100 s/mm² and bi-IVIM-derived parametric maps (f_p, D_t and D_p) as well as tri-IVIM-derived parametric maps (f_s, f_f, f_vf, D_s, D_f and D_vf) of a patient with MVI-positive HCC.

Note: The color bar and ranges of different metrics are same as those in Figure 2.

Figure 4 Quantitative comparison of bi-IVIM and tri-IVIM derived metrics between MVI-negative and MVI-positive HCCs.

Figure 5 The receiver characteristics curves (ROC) of D_f, D_s, D_t and the combination of D_f and D_s (D_f+D_s).

Table 2 Explore the Independent Risk Factors of MVI Through Univariable and Multivariable Logistic Regression Analysis

Parameters	Univariable Analysis		Multivariable Analysis
	OR (95% CI)	P	OR (95% CI)	P
Gender	0.411 (0.090–1.886)	0.253		0.523
CEA	0.968 (0.923–1.016)	0.187		0.738
CA199	2.217 (0.563–8.738)	0.255		0.209
HCV	0.643 (0.055–7.455)	0.724		0.646
Tumor Size	1.268 (1.050–1.531)	0.013	1.248 (1.020–1.527)	0.032
TBIL	0.615 (0.140–2.702)	0.520		0.850
DBIL	0.328 (0.063–1.715)	0.187		0.654
ALT	1.429 (0.463–4.411)	0.535		0.788
AST	1.391 (0.398–4.866)	0.605		0.472
γGT	1.500 (0.537–4.188)	0.439		0.780
AFP	4.259 (1.416–12.812)	0.010	3.653 (1.174–11.371)	0.025

Abbreviations: CEA, carcinoembryonic antigen; CA199, Carbohydrate antigen199; HCV, Hepatitis C; TBIL, total bilirubin; DBIL, direct bilirubin; ALT, alanine aminotransferase; AST, aspartate aminotransferase; γGT, γ glutamyltransferase; AFP, alpha-fetoprotein.

Figure 6 Evaluation of the nomogram-based model.

Notes: (A) The nomogram plot. (B) The ROC and (C) calibration curve of the constructed nomogram model.

Figure 7 The decision curve of the nomogram-based model, tri-IVIM (D_s+D_f) and bi-IVIM (D_t).