Clinical application of dual-layer spectral CT multi-parameter feature to predict microvascular invasion in hepatocellular carcinoma

Abstract

OBJECTIVE:

This study aimed to investigate the feasibility of using dual-layer spectral CT multi-parameter feature to predict microvascular invasion of hepatocellular carcinoma.

METHODS:

This retrospective study enrolled 50 HCC patients who underwent multiphase contrast-enhanced spectral CT studies preoperatively. Combined clinical data, radiological features with spectral CT quantitative parameter were constructed to predict MVI. ROC was applied to identify potential predictors of MVI. The CT values obtained by simulating the conventional CT scans with 70 keV images were compared with those obtained with 40 keV images.

RESULTS:

50 hepatocellular carcinomas were detected with 30 lesions (Group A) with microvascular invasion and 20 (Group B) without. There were significant differences in AFP,tumer size, IC, NIC,slope and effective atomic number in AP and ICrr in VP between Group A ((1000(10.875,1000),4.360±0.3105, 1.7750 (1.5350,1.8825) mg/ml, 0.1785 (0.1621,0.2124), 2.0362±0.2108,8.0960±0.1043,0.2830±0.0777) and Group B (4.750(3.325,20.425),3.190±0.2979,1.4700 (1.4500,1.5775) mg/ml, 0.1441 (0.1373,0.1490),1.8601±0.1595, 7.8105±0.7830 and 0.2228±0.0612) (all p < 0.05). Using 0.1586 as the threshold for NIC, one could obtain an area-under-curve (AUC) of 0.875 in ROC to differentiate between tumours with and without microvascular invasion. AUC was 0.625 with CT value at 70 keV and improved to 0.843 at 40 keV.

CONCLUSION:

Dual-layer spectral CT provides additional quantitative parameters than conventional CT to enhance the differentiation between hepatocellular carcinoma with and without microvascular invasion. Especially, the normalized iodine concentration (NIC) in arterial phase has the greatest potential application value in determining whether microvascular invasion exists, and can offer an important reference for clinical treatment plan and prognosis assessment.

Keywords

Hepatocellular carcinoma microvascular invasion dual-layer spectral CT energy spectrum curve effective atomic number

1 Introduction

According to the latest global cancer burden data for 2020 published by the WHO International Agency for Research on Cancer, primary liver cancer accounts for 19.33% of all malignancies and ranks as the sixth most common cancer and the second leading cause of cancer-related deaths [1], with approximately 840,000 new cases and 780,000 deaths every year [2], causing a serious public health burden. More than 40% of new cases of HCC have occurred in China [3]. The 5-year survival rate of HCC is approximately 10–20% [4], and the median overall survival time is only about 20–30 months worldwide [5]. One of the main causes for this is the high rate of tumor recurrence following radical hepatectomy, which is 35% after transplantation and up to 70% after resection at 5 years.

At present, a large number of studies have shown that the presence of microvascular invasion (MVI) is considered as an independent prognostic factor associated with HCC’s early recurrence and poor survival after resection [6 –8], which is usually related to the stage of HCC and the rapid progression of the disease. Therefore, preoperative prediction of whether MVI is present can help clinicians choose appropriate surgical programs for patients according to risk-benefit assessment. such as wide resection margins or anatomic resection [9 –11], which could theoretically decrease tumor recurrence rates and improve prognosis [12]. Unfortunately, MVI is a pathological criterion and can only be reliably determined by histopathological examination of surgical specimens obtained from hepatectomy or liver transplantation, which is equivalent to postoperative diagnosis and cannot provide preoperative guidance to the clinic. Therefore, there is an urgent need to study effective MVI prediction strategies and establish non-invasive prediction models before surgery, which is of great significance for the formulation of individualized treatment plans for HCC patients.

Computed Tomography (CT) is the preferred imaging method for diagnosing abnormal serum alpha-fetoprotein screening and/or for liver ultrasound detection of nodules or masses in high-risk populations. Previous studies [13 –15] have reported that non-smooth tumor margins, peritumoral enhancement in the arterial phase, peritumoral hypointensity in the hepatobiliary phase and increased CT value at portal vein stage are independent risk factors for hepatocellular carcinoma complicated with MVI, and the combined value of multiple predictors in the diagnosis of MVI is higher than that of a single sign. In addition to these subjective radiological features, numerous quantitative imaging methods have been utilized to improve the predictive performance of MVI [16 –21]. More recently, the double-layer spectral CT(dlct) [22] has been introduced, which is capable of achieving quantitative multi-parameters based on spectral imaging data and can help in the detection and characterization of lesions without presetting additional protocols, has shown great potential for MVI prediction [23 –25]. but earlier studies have concentrated only on arterial phases or employed a single parameter of iodine concentration. The significance of other useful parameters such as effective atomic number and energy spectrum curve in MVI prediction has not been fully investigated. Therefore, this study aimed to explore the feasibility of using dual-layer spectral CT multi-parameter feature to forecast MVI of HCC.

2 Materials and methods

2.1 Patients

The institutional review board of our institution approved this retrospective study and waived the requirement for informed consent.

From December 2020 to May 2023, we retrospectively searched the medical record database in our institution to collect the consecutive patients with pathologically confirmed HCC who underwent multiphase liver DLCT (n = 124). The following inclusion criteria resulted in 89 participants: (1) radical partial hepatectomy, postoperative pathology confirmed as HCC; (2) new HCC rather than recurrent lesions; (3) spectral CT examination within 1 week before operation.. The exclusion criteria were: (1) preoperative imaging examination revealed macrovascular invasion and two or more HCC lesions(n = 17); (2) poor CT image quality affected the diagnosis of imaging signs(n = 3);(3) incomplete clinical and pathological data(n = 2); (4) HCC rupture and bleeding, history of malignant tumors in other systems, and preoperative anticancer treatment(n = 12); (5) trauma, fever or acute infection within 1 week before operation; complicated with autoimmune diseases (n = 5).

According to the strict inclusion and exclusion process (Fig. 1), 50 patients were finally included in the study.

Fig. 1

Diagram showing the recruitment of the study population and exclusion criteria. HCC, hepatocellular carcinoma; MVI,microvascular invasion.

2.2 Baseline clinical data

All patients were examined by blood routine, biochemical items, preoperative coagulation and tumor markers within one week before operation. The baseline data of sex, age, type of hepatitis, Child-Pugh grade, AFP,CEA,CA-199,CA125,PT, alanine aminotransferase, (ALT), aspartate aminotransferase (AST), total bilirubin, albumin, globulin, alkaline phosphatase (ALP), γ-glutamyl transpeptidase (GGT), platelet, creatinine, lymphocyte, monocyte and neutrophil count were collected.

2.3 Imaging methods

All patients underwent the non-contrast and two-phase contrast enhanced CT scans on a DLCT scanner (IQon Spectral CT, Philips Healthcare, Holland) using the dual-energy spectral CT acquisition mode in craniocaudal direction and supine position. Patients fasted for 8 h and took 800–1000ML warm water orally 5–10 min before the scanning to fill the upper digestive tract. After the non-enhanced abdominal scan was acquired, a bolus of nonionic contrast agent (iodixanol 300mgI/ ml,China, Jiangsu Hengrui medicine) was injected through the median cubital vein with a German ORICH high pressure syringe at a Patient weight-dependent dose of 1.5 ml/kg and infusion rate of 3.0 ml/s. AP and PP were obtained with a delay of 15 s and 30 s after reaching a threshold value of 100 Hounsfield units (HU) at the descending aorta near the level of the left renal hilum, respectively. The DLCT scan parameters were as follows: tube voltage, 120 kVp;noise figure, 10–12;helical pitch, 1; detector collimation,0.625 mm×64 and gantry rotation speed,0.5 s/rot. Automatic tube current modulation was enabled with a DoseRight Index (Philips Healthcare) of 22.

Conventional CT images were reconstructed using an iterative reconstruction algorithm (IDose4, Level 3, Filter B, Philips Healthcare), while spectral base images (SBIs) were reconstructed via a dedicated spectral image reconstruction algorithm (Spectral, Level 3, Filter B, Philips Healthcare) to generate all spectral parameters. All axial images were reconstructed with a slice thickness and section increment of 1.0 mm.

2.4 Observation and analysis method

2.4.1 Image analysis

Two radiologists with more than 5 years of experience in liver imaging after fellowship were selected to measured quantitative parameters together with the author of this paper, who were aware of the diagnosis of primary hepatocellular carcinoma but blinded to the clinical history and to the pathological results of MVI.

For qualitative analysis, 7major and ancillary LI-RADS features, including nonrim arteria”l phase hyperenhancement, nonperipheral “washout”, enhancing capsule, non enhancing capsule, nodule-in-nodule architecture, mosaic architecture, and corona enhancement (absent vs. present), as well as 6 non-LIRADS imaging features, including tumor margin, tumor capsule, intratumor necrosis, intratumoral hemorrhage,intratumoral steatosis and TTPVI were independently evaluated on conventional CT images. Measuring the maximum diameter of tumor at the same time and the average of the 3 measurements was used as the final result.

For quantitative parameters, images were transferred to the workstation (IntelliSpace Portal version 6.5, Philips Healthcare) for post-processing and analysis. Effective atomic number (Zeff) maps, iodine density (ID), and Virtual monochromatic images (VMIs) were generated using SBIs. After avoiding the blood vessels, calcified plaque and liquefy necrotic parts, the circular region-of-interest (ROI) was placed on the following structures in the 70 keV images: the most obvious enhancement of lesions (ROI 50±1mm2) and the abdominal aorta at the same imaging level (ROI 20±1mm2) in AP and PP. Both of the ROIs were copied onto VMIs at 40 keV and 90 keV, ID maps, and Zeff maps automatically in identical positions to measure DLCT parameters. Then CT values at 40 keV and 90 keV (CT40 keV and CT90 keV) in AP and ID values, and Zeff values in AP and PP were automatically generated. The copy and paste function was used to ensure measurement consistency.

After the basic data is obtained, a series of formulas need to be calculated:(1) the normalized iodine density (NID) values:NIC = ID-tumor/ ID-aorta, where ID-tumor and ID-aorta represent the ID values in the tumor lesion and aorta, respectively. So that to reduce variations caused by individual differences in hemodynamics and the scanning times between patients;(2)The slope of spectral HU curve (λHU):λHU = (CT40 keV-–CT90 keV)/(90–-40),which was defined as the difference between the CT40 keV and CT90 keV divided by the energy difference (50 keV).;(3):iodine uptake ratio,(ICrr): ICrr = (IC-AP–IC-VP)/IC-AP. To reduce measurement variation, ROI was placed 3 times on the tumour and the average of the 3 measurements was used as the final result.

2.4.2 Pathological analysis

Two experienced pathologist (both with over 15 years of experience in liver pathology) performed all sample handling and pathological analysis and were blinded to the imaging and clinical information. MVI status, radiological features, histological grades, as well as liver background were evaluated and recorded. The pathological evaluation and diagnostic criteria for MVI adopted in this study were in accordance with the Chinese HCC Diagnosis and Treatment Guidelines (2019 Edition). When two physicians disagreed on MVI, consensus was reached through discussion and used for subsequent analyses.

2.5 Statistical analysis

All statistical analyses was performed SPSS26.0 (SPSS Inc,Chicago, IL), P-value of less than 0.05 was considered statistically significant. To compare parameters between the HCCs with and without microvascular invasion, continuous data were assessed using the Mann-Whitney test or the two-sample t-test, while categorical variables were analyzed using the chi-square test or Fisher exact test. The parameters with statistical differences in the comparison between groups were analyzed by subject working characteristics (ROC) to establish threshold values and to calculate the area-under-curve (AUC) for ROC curves, sensitivity and specificity for differentiating small hepatocellular carcinoma with and without microvascular invasion.

3 Results

3.1 Patient characteristics

The final study population comprised 50 patients (31men and 19women) with a mean age of 58.90±9.146 (SD) years (range, 35–76 years). There were 30 lesions with microvascular invasion (30/50, 60.0%, Group A) (Fig. 2) and 20 lesions without microvascular invasion (20/50, 40%, Group B) (Fig. 3). There were no statistically significant differences in age, gender and medical history between the two groups (p > 0.05). But according to the statistical analysis of the serological indexes of the two groups, it was found that the serum AFP in Group A was higher than that in Group B, and the difference was statistically significant (p < 0.05). The clinical and pathological characteristics of patients in both groups are described in Table 1.

Fig. 2

Dual-layer spectral-detector CT images of a 34-year-old male with a 3.9-cm pathologically confirmed hepatocellular carcinoma and microvascular invasion (M1) in the right hepatic lobe(S6). Figure A: The arterial phase spectral CT image shows that the lesion is not uniformly enhanced, and the edges are nodular and significantly enhanced; Figure B: The venous phase spectrum CT image shows that the intensity of the lesion is reduced and is lower than that of the surrounding normal liver tissue. Surrounded by low density; Figure C: Arterial phase 40Kev spectrum CT image, the lesion display rate is significantly improved; Figure D: Arterial phase iodine density map shows that the iodine concentration at the edge of the lesion is higher than that in the adjacent liver parenchyma; Figure E: Arterial phase,The effective atomic number map shows that the effective atomic number at the edge of the lesion is significantly higher than that of the adjacent liver parenchyma; Figure F: Postoperative HE staining shows cancer cell nests within the blood vessels of the lesion; Figure G: Arterial phase energy spectrum curve, microvascular invasion group The CT value increased significantly.

Fig. 3

Dual-layer spectral-detector CT images of a 68-year-old male with a 2.1-cm pathologically confirmed hepatocellular carcinoma and microvascular invasion (M0) in the right hepatic lobe(S8). Figure A: The arterial phase spectral CT image shows that the lesions are irregular in shape, clear in boundary, and slightly enhanced; Figure B:The venous phase spectral CT image shows that the intensity of the lesion is reduced and is lower than that of the adjacent liver tissue; Figure C: Arterial phase 40Kev spectrum CT image, the visibility of the lesion is significantly improved; Figure D: Arterial phase iodine density map shows that the lesion. The iodine concentration is higher than that in the adjacent liver parenchyma; Figure E: Arterial phase effective atomic number diagram, effective atomic number of the lesion The number is higher than that in the adjacent liver parenchyma; Figure F: Postoperative HE staining shows liver cancer smear without microvascular invasion; Figure G:In the arterial phase energy spectrum curve, the curve in the group without microvascular invasion was relatively gentle.

Table 1

Comparison of the clinical characteristics of the study population according to pathological microvascular invasion

Variables	MVI(+)	MVI(–)	p-Value^a
Clinical characteristics
Age	59.07±9.850	58.65±8.216	0.876^b
Gender			0.122^c
Male	16(53.3)	15(75.0)
Female	14(46.7)	5(25.0)
HBV			0.065^c
Positive	25(83.3)	12(60.0)
Negative	5(16.70	8(40.0)
HCV			0.377^c
Positive	2(6.7)	3(15.0)
Negative	28(93.3)	17(85.0)
Child-pugh classification			0.265^c
A	27(90.0)	20(100.0)
B	3(10.0)	0(0.0)
hepatocirrhosis			0.065^c
Positive	25(83.3)	12(60.0)
Negative	5(16.7)	8(40.0)
AFP(ng/mL)	1000(10.875,1000)	4.750(3.325,20.425)	<0.001
CEA(ng/ml)	2.150(1.550,3.125)	1.900(1.400,2.800)	0.648
CA-199 (u/ml)	16.750(7.850,44.200)	14.150(5.725,24.900)	0.239
CA125(U/ml)	11.5500(7.4250,15.5750)	12.9500(8.9250,18.5000)	0.384
PT(s)	12.2500(11.4350,13.3500)	12.6000(11.5050,13.3525)	0.501
AST(IU/L)	30.800(24.675,38.750)	40.000(25.500,45.750)	0.172
ALT(IU/L)	27.400(20.750,41.750)	30.150(20.750,44.300)	0.751
ALB(g/L)	42.557±6.1837	41.990±2.6346	0.701^b
GLOB(g/L)	27.800±4.4755	26.785±4.7717	0.448^b
TB(μmol/L)	20.100(16.050,25.925)	17.900(12.950,21.225)	0.188
DB(μmol/L)	4.700(3.150,5.700)	3.500(2.925,5.000)	0.106
ALP(IU/L)	100.500(78.750,138.750)	97.000(80.175,123.775)	0.744
GGT(IU/L)	35.000(22.375,69.000)	46.650(28.550,98.550)	0.181
Cr(μmol/L)	73.750±17.2028	69.270±13.2300	0.329^b
PLT(×10⁹ /L)	97.50(71.75,152.75)	119.00(78.25,153.25)	0.507
LY(×10⁹ /L)	1.4550(0.9000,1.8075)	1.3800(1.0150,1.7475)	0.866
NE(×10⁹ /L)	2.5350(1.8400,3.7325)	2.5650(1.8775(3.3825)	0.843
MO(×10⁹ /L)	0.3600(9.2675,0.5525)	0.3750(0.2350,0.4625)	0.797

Note—Data are expressed as mean±SD or number with percentage in parentheses. Statistically significant results are marked in bold. ^aUnless otherwise indicated, p values were calculated with Mann-Whitney U test. ^bp values were calculated with the independent sample t test. ^cp values were calculated with x2 or Fisher exact test.

The patient’s preoperative serum AFP was analyzed through the ROC curve (Fig. 4), and the optimal cutoff value of AFP was 349.0. That is, when the patient’s preoperative serum alpha-fetoprotein content was 349.0 ng/ml, the patient could be predicted to have or not have HCC. The sensitivity and specificity of microvascular invasion are the best. At this time, the corresponding sensitivity and specificity are 66.7% and 90.0% respectively, and the AUC (95% CI) is 0.810(0.689∼0.931) (Table 2).

Fig. 4

ROC curve of AFP.

Table 2

Predictive performance of AFP for identifying MVI status

Variables	Youden index	Threshold	AUC (95% CI)	Sensitivity	Specificity
AFP	0.567	349.0	0.810(0.689∼0.931)	66.70%	90.00%

AUC, area under the curve; CI, confidence interval; MVI, microvascular invasion.

3.2 CT Imaging features

The CT imaging features of MVI(+) and MVI(–) are shown in Table 3. Only tumor size was significantly larger in MVI(+) than in MVI(–) (p = 0.013). Other CT imaging features were showed no significantly different distribution between the MVI(+) and MVI(–) group (all p > 0.05).

Table 3
Comparison of the radiological characteristics of the study population according to pathological microvascular invasion

Variables MVI(+) MVI(–) p-Value^a

Tumor size (cm)^b 4.360±0.3105 3.190±0.2979 0.013

Nonrim APHE 13 (43.3) 8(40.0) 0.815

Nonperipheral washout 7(23.3) 3(15.0) 0.470

Enhancing capsule 5(16.7) 2(10.0) 0.506

Nonenhancing capsule 4(13.3) 2(10.0) 0.722

Nodule-in-nodule architecture 2(6.7) 1(5.0) 0.808

Mosaic architecture 10(33.3) 3(15.0) 0.148

Corona enhancement 8(26.7) 3(15.0) 0.329

Nonsmooth tumor margin 18(60.0) 9(45.0) 0.297

Incomplete tumor capsule 17(56.7) 6(30.0) 0.064

Intratumor necrosis 10(33.3) 3(15.0) 0.148

Intratumoral hemorrhage 4(13.3) 3(15.0) 0.868

Intratumoral steatosis 2(6.7) 2(10.0) 0.659

TTPVI 15(50.0) 5(25.0) 0.273

Variables	MVI(+)	MVI(–)	p-Value^a
Tumor size (cm)^b	4.360±0.3105	3.190±0.2979	0.013
Nonrim APHE	13 (43.3)	8(40.0)	0.815
Nonperipheral washout	7(23.3)	3(15.0)	0.470
Enhancing capsule	5(16.7)	2(10.0)	0.506
Nonenhancing capsule	4(13.3)	2(10.0)	0.722
Nodule-in-nodule architecture	2(6.7)	1(5.0)	0.808
Mosaic architecture	10(33.3)	3(15.0)	0.148
Corona enhancement	8(26.7)	3(15.0)	0.329
Nonsmooth tumor margin	18(60.0)	9(45.0)	0.297
Incomplete tumor capsule	17(56.7)	6(30.0)	0.064
Intratumor necrosis	10(33.3)	3(15.0)	0.148
Intratumoral hemorrhage	4(13.3)	3(15.0)	0.868
Intratumoral steatosis	2(6.7)	2(10.0)	0.659
TTPVI	15(50.0)	5(25.0)	0.273

Note—Data are expressed as mean±SD or number with percentage in parentheses. Statistically significant results are marked in bold. APHE, arterial phase hyperenhancement; TTPVI, two-trait predictor of venous invasion. ^aUnless otherwise indicated, p values were calculated with x2 or Fisher exact test. ^bp values were calculated with the independent sample t test.

The patient’s Tumor size was analyzed through the ROC curve (Fig. 5), and the optimal cutoff value of Tumor size was 3.75. That is, when the patient’s Tumor size was 3.75 cm, the patient could be predicted to have or not have HCC. The sensitivity and specificity of microvascular invasion are the best. At this time, the corresponding sensitivity and specificity are 56.7% and 85.0% respectively, and the AUC (95% CI) is 0.732(0.588∼0.875) (Table 4).

Fig. 5

ROC curve of tumor size.

Table 4

Predictive performance of tumor size for identifying MVI status

Variables	Youden index	Threshold	AUC (95% CI)	Sensitivity	Specificity
Tumor size	0.417	3.75	0.732(0.588∼0.875)	56.70%	85.00%

AUC, area under the curve; CI, confidence interval; MVI, microvascular invasion.

3.3 DLCT Quantitative parameters

Results of the comparison of DLCT quantitative parameters are summarized in Table 5. As indicated in Table 5, the iodine concentration (IC), normalized iodine concentration (NIC), effective atomic number(Zeff), slope (λHU) of the spectral HU curve in the arterial phase and and reduction rate of venous phase iodine uptake ratio (ICrr) in Group A were significantly higher than those in Group B (p < 0.05). The CT values measured on the images at 70 keV, which simulating the energy level of a conventional 120kVp imaging, were statistically the same between tumours with and without microvascular invasion (p > 0.05). ROC analysis results using the above significant parameters are presented in Table 4. The CT value measurement at 70 keV had low AUC value at 0.625, while CT value at 40 keV improved it to 0.843. The dual-energy spectral CT-specific parameters IC, NIC,λHU, Zeff and ICrr all generated higher area under-curve values for the ROC study than using the conventional CT value measurement (Fig. 6). AUCs of the 6 significant parameters ranged from 0.670 to 0.875 for differentiating Group A from Group B in HCC. Among these single parameters, NIC showed the highest predictive effectiveness for discriminating MVI status, with an AUC of 0.875(95% CI, 0.777–0.973), accuracy of 83.3%, sensitivity of 85.0%.

Table 5
Comparison of DLCT quantitative parameters between the MVI-Negative and MVI-Positive groups

Variables MVI(+) MVI(–) p-Value^a

40Kev 178.3064(167.0450, 188.0990) 157.6500(154.8875, 165.7100) <0.001^b

70Kev 91.6382±5.6533 89.3978±5.5795 0.174

λHU 2.0362±0.2108 1.8601±0.1595 0.003

IC-A 1.7750(1.5350,1.8825) 1.4700(1.4500,1.5775) <0.001 ^b

IC-P 1.2100(1.1250,1.3400) 1.1500(1.1150,1.2200) 0.098^b

NIC-A 0.1785(0.1621,0.2124) 0.1441(0.1373,0.1490) <0.001 ^b

NIC-P 0.4068±0.0545 0.3837±0.3777 0.083

ICRR 0.2830±0.0777 0.2228±0.0612 0.005

Zeff-A 8.0960±0.1043 8.0335±0.1087 0.047

Zeff-P 7.8373±0.1359 7.8105±0.7830 0.381

Variables	MVI(+)	MVI(–)	p-Value^a
40Kev	178.3064(167.0450, 188.0990)	157.6500(154.8875, 165.7100)	<0.001^b
70Kev	91.6382±5.6533	89.3978±5.5795	0.174
λHU	2.0362±0.2108	1.8601±0.1595	0.003
IC-A	1.7750(1.5350,1.8825)	1.4700(1.4500,1.5775)	<0.001 ^b
IC-P	1.2100(1.1250,1.3400)	1.1500(1.1150,1.2200)	0.098^b
NIC-A	0.1785(0.1621,0.2124)	0.1441(0.1373,0.1490)	<0.001 ^b
NIC-P	0.4068±0.0545	0.3837±0.3777	0.083
ICRR	0.2830±0.0777	0.2228±0.0612	0.005
Zeff-A	8.0960±0.1043	8.0335±0.1087	0.047
Zeff-P	7.8373±0.1359	7.8105±0.7830	0.381

Note—Data are expressed as mean±standard deviation or median and interquartile range in parentheses. DLCT, Dual-layer spectral-detector CT; MVI, microvascular invasion; IC,iodine concentratio;A, hepatic arterial phase; P, portalvenous phase; Zeff, effective atomic number; (N)ID, (normalized) iodine density;λHU, the slope of spectral HU curve; ICrr,iodine uptake ratio. Statistically significant results are marked in bold. ^ap values were calculated with the independent sample t test. ^bUnless otherwise indicated, p values were calculated with x2 or Fisher exact test.

Table 6

Predictive performance of DLCT quantitative parameters for identifying MVI status

Variables	Youden index	Threshold	AUC (95% CI)	Sensitivity	Specificity
40Kev	0.667	170.582	0.843(0.723,0.964)	76.7%	90.0%
70Kev	0.333	86.885	0.625(0.456,0.794)	93.3%	40.0%
λHU	0.617	1.938	0.757(0.619,0.895)	76.7%	75.0%
IC-A	0.633	1.72	0.803(0.682,0.925)	63.3%	100%
NIC-A	0.683	0.1586	0.875(0.777,0.973)	83.3%	85.0%
ICRR	0.633	0.282	0.795(0.673,0.917)	63.3%	100%
Zeff-A	0.367	8.025	0.670(0.516,0.824)	76.7%	60.0%

DLCT, Dual-layer spectral-detector CT; MVI, microvascular invasion; IC, iodine concentratio;A, hepatic arterial phase; P, portalvenous phase; Zeff, effective atomic number; (N)ID, (normalized) iodine density; λHU, the slope of spectral HU curve; ICrr,iodine uptake ratio.

Fig. 6

ROC curve of DLCT quantitative parameters.

4 Discussion

Microvascular invasion (MVI) in hepatocellular carcinoma is a complex, multi-step, dynamic biological process that describes the histological presence of malignant tumor cells in the adjacent vasculature, and is considered to capture the metastasis of cancer cells to adjacent tissues and distant sites. Regular CT is helpful in detecting the vascular invasions to larger blood vessels in hepatocellular carcinoma. However, microscopic vascular invasions such as intratumoural microvascular invasion cannot be observed directly. In our study, we screened out the parameters with statistical differences between the two groups from clinical data, radiological characteristics and spectral CT quantitative parameters, and evaluated their diagnostic performance by area analysis under ROC curve, so as to provide a reference basis for preoperative non-invasive evaluation of microvascular invasion of hepatocellular carcinoma, in order to improve the understanding of hepatocellular carcinoma MVI and provide new ideas for clinical practice and research.

AFP is a special glycoprotein molecule, the activity of AFP-related genes is activated and re-expressed during hepatocellular carcinogenesis, and its serum level is related to the growth and progression of malignant tumor cells [26]. The content of serum alpha-fetoprotein is significantly increased in more than 70% of patients with hepatocellular carcinoma before operation, which is recognized as an index related to HCC in clinical practice [27, 28]. Initially, McHugh [29] reported that there was a significant correlation between serum AFP and the presence of MVI (OR5.0,95% CI1.4–18.1,P = 0.006), and suggested that MVI increased the risk of recurrence and death in patients with hepatocellular carcinoma after liver transplantation. In recent years, many scholars have developed alignment maps based on serological data for preoperative prediction of microvascular invasion in hepatocellular carcinoma. AFP is an independent risk factor for MVI and was included in the construction of a model for predicting and diagnosing microvascular invasion of HCC before operation. In our study, there was a significant difference in preoperative serum AFP in patients with HCC with or without microvascular invasion, which was consistent with previous results.

Tumor size is another potential predictor for MVI. It is generally believed that the larger the size of the tumor, especially > 5 cm, indicates the greater the risk of MVI and postoperative recurrence. In our study, although it was concluded that there was a significant positive correlation between tumor size and MVI (ρ= 0.013), the cutoff value was in 3.75 cm.Among all the radiological features, we did not find any factors closely related to MVI, which does not match the studies of other scholars who have come to the conclusion that 2 LI-RADS features (mosaic architecture and corona enhancement) and 2non-LI-RADS features (incomplete tumor capsule and TTPVI) were independently associated with MVI [30 –33], Mosaic architecture is the presence of randomly distributed internal nodules or compartments within a mass that differ in shape on enhancement [34]. In HCC, mosaic architecture is characteristic of tumor heterogeneity with histologic and cytologic variations more common in large HCC, which might cause MVI formation. Corona enhancement is typically observed with high arterial input and high venous output around the tumor because of abnormal tumor venous drainage [35]. Studies shown that corona enhancement might convey information on MVI, and HCCs with corona enhancement findings tend to be diagnosed as progressed, hypervascular HCC. An incomplete tumor envelope often reflects the aggressive biological behavior of HCC by invading the outer edge of the tumor and spreading to surrounding normal liver tissue. Someone [36] reported that TTPVI algorithm could well predict MVI based on the association between radiological features and gene expression. But it is a pity that we have not come to a similar conclusion. This may be due to the small number of cases, so more cases should be included in follow-up studies.

Our study showed that multiparameter DLCT was effective in predicting MVI of HCC preoperatively. After contrast, ID,NID,Zeff in AP and ICRR in PP andλHU were significantly higher in group A than group B. Spectral CT is highly sensitive to changes of iodine content of materials. ID values can directly reflect the iodine contrast concentration in the tissues, supplying information on perfusion and vascularity. Zeff is determined by the average density of tissue and it is related to the absorption and attenuation coefficient of the substance. Therefore, ID and Zeff changes could be used to indicate the change in microcirculation and serve as a specific biomarker of tumor vessels to help predict the presence of microvascular invasion in HCC patients [37, 38].

Most HCC tumors are hypervascular, and the high vascularity of tumor has been identified as a major source of HCC progression and a critical pathophysiological process, plays an important role in promoting the growth, diffusion and metastasis of cancer cells [39, 40]. Thus we assume that MVI is closely related to microvessel density (MVD). MVD is a predictor of tumor angiogenesis. The higher the MVD value of hepatocellular carcinoma, the greater the risk of tumor cells invading microvessels, entering blood circulation, local and distant colonization and metastasis. The reasons may be: 1) the abundant neovascularization within the tumor accelerates the tumor progression, while the fast-growing tumor tissue releases more growth factors to stimulate the activation, proliferation and migration of neovascularization endothelial cells, which promote and influence each other; 2) in the presence of MVI, the microenvironment of tumor tissue changes to obtain the ability to produce matrix proteases, and the extracellular matrix is degraded. At the same time, the decrease of cadherin expression, intercellular adhesion and tissue integrity of tissue epithelial cells promote tumor cells to escape and invade neovascularization, then the increase of microvessel density will be accompanied by an increase in the probability of malignant tumor cells infiltrating blood vessels, which provides favorable conditions for the occurrence of MVI. The incomplete microvascular structure caused by immature microvascular hyperplasia leads to the increase of the permeability of small neovascularization [41]. This leads to a significant increase in blood perfusion in the focus tissue, resulting in an increase in iodine content in hepatocellular carcinoma lesions. Increased blood perfusion and capillary density contributed to elevated ID, NID, ICRR and Zeff in group A.

λHU reflects the rate of attenuation changes at different X-ray energies in a tumor and is characteristic of chemical compound compositions [42]. The difference in attenuation at low energies will expand after iodine contrast injection because of the hyper-vasculature of HCC. Thus, a significantly higherλHU in HCC with MVI at AP was observed in this study. And the results indicated that the CT value at 70 keV monochromatic images had low efficacy in the diagnosis of microvascular invasion of HCC. Since the conventional 120kVp polychromatic x-ray has an average energy of about 70 keV after penetrating the abdomen of an average size adult patient [43], CT values of the lesion for 70 keV and 120kVp images are similar. Our results obtained using the 70 keV CT value are indicative and consistent with the fact that conventional 120kVp CT imaging does not provide satisfactory diagnosis of microvascular invasion of HCC. At the same time, with the increased conspicuity, lower keV images (i.e. at 40 keV) improved the area-under-curve in ROC study from 0.625 with CT value at 70 keV to 0.843 with CT value at 40 keV, which is consistent with the clinical use of low kilovolt single-energy image magnification to enhance the visualization of iodine in tissue and the increase of focus sharpness, which improves the image resolution and CT value uniformity. Spectral CT low-energy imaging can improve the contrast between blood vessels and surrounding tissues, not only reduce the amount of iodine-enhanced contrast agent, but also maintain good temporal and spatial resolution. Although it can increase image noise, it can be suppressed and improved by its anti-correlation noise principle and adaptive statistical iterative reconstruction (ASIR), which still has high clinical application value.

In fact,many studies try to explore the correlation between different imaging methods and MVI. Contrast-enhanced ultrasound(CEUS) is able to sensitively visualize in real-time even weak tissue and lesion perfusion thereby showing the hemodynamic changes of liver tumors and has advantages in evaluating microvascular perfusion of HCC. Previously, a review [44] investigated the clinical value of VueBox^®-based DCE-US in predicting the MVI of HCC, and the results [45] revealed that the wash-in area under the curve and wash-out area under the curve were higher in MVI-positive group than in MVI-negative group either in the lesion center or at the margin, the use of these techniques allows for the estimation of blood flow parameters of liver cancer lesions. Dong et al. [46] concluded radiomic model based on Kupffer phase ultrasound images of tissue adjacent to HCC lesions has potential value to facilitate preoperative identification of HCC patients at higher risk of MVI. Currently, the evaluation indicators of ultrasonic examination in MVI assessment are controversial which may be due to differences in machine equipment and operator experience levels. Therefore, in-depth research based on standardized collection and establishment of an ultrasound image database is the future development direction. Magnetic Resonance Imaging(MRI)can display well soft tissues and blood vessels with high resolution because of its unique imaging principle, the non-invasive dynamic contrast-enhanced MRI(DCE-MRI) method provides valuable insights into tissue perfusion and vascularity. A meta-analysis [47] on DCE-MRI screened 7 MRI features from 235 articles significantly correlated with MVI, among which peritumoral hypointensity on HBP was the most indicative of MVI. The relationship between this feature and MVI can be explained by peritumoral perfusion change resulting from the dysfunction of organic anion-transporting polypeptide transporters in the hepatocytes around the liver cancer [48]. In this regard, Wu et al. [49] also reached a similar conclusion. And Chen et al. [50] found that in patients without peritumoral HBP hypointensity, a radiological capsule is helping in identifying MVI. However, it is less sensitive to rely solely on imaging features to predict MVI. Wang et al. [51] retrospectively analyzed the signal intensity (SI) of normal liver tissue and tumor parenchyma in 113 HCC patients based on Gd-EOB-DTPA enhanced MRI in the arterial and hepatobiliary phases, it is concluded that SI ratio of peritumoral tissue to normal liver in arterial phase(SIAp/Al) is a potential diagnosis marker for MVI, and the AUC value of SIAp/Al is higher than that of peritumoral hypointensity on hepatobiliary phase imaging, which indicates that quantitative analysis and evaluation of MVI may be more convincing. The current research status shows that preoperative imaging assessment of MVI has made some progress, but it is still challenging. Currently, there are no universally recognized criteria for preoperative diagnosis of MVI in clinical practice. However, spectral CT shows certain advantages in predicting MVI before surgery because it provides a large number of quantitative parameters.

There are still some limitations in this study: firstly, our research data is based on a retrospective analysis of a single hospital, and the sample size of the study is small. Secondly, there are many clinical inflammatory indicators, such as the ratio of platelet to lymphocyte, the ratio of glutamic oxaloacetic transaminase to glutamic pyruvic transaminase and so on. This paper only focuses on the relationship between AFP and MVI risk, so more research is needed to cover and analyze all the preoperative indicators. Thirdly, our region of interest does not include the whole tumor, but only outlines the areas with the most obvious enhancement of the lesion, this delineation may make it difficult to match the ROI depicted on imaging images with pathological sections. Future full focus assessment may be a potential method to ensure the repeatability of the same study in different institutions. Finally, our study is based on a specific group of patients, including only hepatocellular carcinoma. Critical points are, that CT cannot differantiate small HCC lesions from shunts, high Flow hemangiomas, NET and angiomas. Therefore, it is difficult to extend this conclusion to other liver lesions similar to HCC enhancement patterns, so there is limited experience in the diagnosis of microvascular invasion in these diseases. In the future, the combined application of CT, CEUS, MRI and other imaging techniques to improve the diagnostic performance and accuracy of different diseases may become a new research direction.

In summary, dual-energy spectrum CT provides additional quantitative parameters compared with other imaging methods, which can improve the identification of small liver cancer with or without microvascular invasion.

Conflict of interest

The authors have no conflict of interest.

Author contributions

Yi-xiang Li,Yong-sheng XU,Lu-lu Jia and Miao-miao Wang collected the data. Yi-xiang Li,Meng-meng Qu and Jun-qiang Lei did the analysis and interpretation of data; Yi-xiang Li,Li-li Wang,Xian-de Lu and Wen-jing Li conceptualized and wrote the manuscript. Grant to Clinical Foundation Of The First Hospital of Lanzhou University financed this study. All the authors approved the version to be published.

Funding

Funding from the Clinical Foundation Of The First Hospital of Lanzhou University, (No. Ldyyyn2020-14) is gratefully acknowledged.

Ethics approval and consent to participate

This case was approved by the institutional ethical review board of The First Hospital of Lanzhou University of Science and Technology[LDYYLL-2024-29].Written informed consent for the present study was obtained from the patient.

Consent for publication

The manuscript has not been and will not be a podium or poster meeting presentation.

Data availability

The datasets used and analysed during the current study are available from the corresponding author on reasonable request.

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