Role of microvascular invasion in early recurrence of hepatocellular carcinoma after liver resection: A literature review

Hepatectomy is widely considered a potential treatment for hepatocellular carcinoma (HCC). Unfortunately, one-third of HCC patients have tumor recurrence within 2 years after surgery (early recurrence), accounting for more than 60% of all recurrence patients. Early recurrence is associated with a worse prognosis. Previous studies have shown that microvascular invasion (MVI) is one of the key factors for early recurrence and poor prognosis in patients with HCC after surgery. This paper reviews the latest literature and summarizes the predictors of MVI, the correlation between MVI and early recurrence, the identification of suspicious nodules or subclinical lesions, and the treatment strategies for MVI-positive HCC. The aim is to explore the management of patients with MVI-positive HCC.

Noninvasive urinary protein signatures combined clinical information associated with microvascular invasion risk in HCC patients

BACKGROUND

Microvascular invasion (MVI) is the main factor affecting the prognosis of patients with hepatocellular carcinoma (HCC). The aim of this study was to identify accurate diagnostic biomarkers from urinary protein signatures for preoperative prediction.

METHODS

We conducted label-free quantitative proteomic studies on urine samples of 91 HCC patients and 22 healthy controls. We identified candidate biomarkers capable of predicting MVI status and combined them with patient clinical information to perform a preoperative nomogram for predicting MVI status in the training cohort. Then, the nomogram was validated in the testing cohort (n = 23). Expression levels of biomarkers were further confirmed by enzyme-linked immunosorbent assay (ELISA) in an independent validation HCC cohort (n = 57).

RESULTS

Urinary proteomic features of healthy controls are mainly characterized by active metabolic processes. Cell adhesion and cell proliferation-related pathways were highly defined in the HCC group, such as extracellular matrix organization, cell-cell adhesion, and cell-cell junction organization, which confirms the malignant phenotype of HCC patients. Based on the expression levels of four proteins: CETP, HGFL, L1CAM, and LAIR2, combined with tumor diameter, serum AFP, and GGT concentrations to establish a preoperative MVI status prediction model for HCC patients. The nomogram achieved good concordance indexes of 0.809 and 0.783 in predicting MVI in the training and testing cohorts.

CONCLUSIONS

The four-protein-related nomogram in urine samples is a promising preoperative prediction model for the MVI status of HCC patients. Using the model, the risk for an individual patient to harbor MVI can be determined.

Preoperative assessment of microvascular invasion of hepatocellular carcinoma using non-Gaussian diffusion-weighted imaging with a fractional order calculus model: A pilot study

PURPOSE

To assess the clinical potential of a set of new diffusion parameters (D, β, and μ) derived from fractional order calculus (FROC) diffusion model in predicting microvascular invasion (MVI) of hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

Between January 2019 to November 2020, a total of 63 patients with HCC were enrolled in this study. Diffusion-weighted images were acquired by using ten b-values (0-2000 s/mm²). The FROC model parameters including diffusion coefficient (D), fractional order parameter (β), a microstructural quantity (μ) together with a conventional apparent diffusion coefficient (ADC) were calculated. Intraclass coefficients were calculated for assessing the agreement of parameters quantified by two radiologists. The differences of these values between the MVI-positive and MVI-negative HCC groups were compared by using independent sample t-test or the Mann-Whitney U test. Then the parameters showing significant differences between subgroups, including the β and D, were integrated to develop a comprehensive predictive model via binary logistic regression. The diagnostic performance was evaluated by receiver operating characteristic (ROC) analysis.

RESULTS

Among all the studied diffusion parameters, significant differences were found in D, β, and ADC between the MVI-positive and MVI-negative groups. MVI-positive HCCs showed significantly higher β values (0.65 ± 0.17 vs. 0.51 ± 0.13, P = 0.001), along with lower D values (0.84 ± 0.11 μm²/ms vs. 1.03 ± 0.13 μm²/ms, P < 0.001) and lower ADC values (1.38 ± 0.46 μm²/ms vs. 2.09 ± 0.70 μm²/ms, P < 0.001) than those of MVI-negative HCCs. According to the ROC analysis, the combination of D and β demonstrated the largest area under the ROC curve (0.920) compared with individual parameters (D: 0.912; β: 0.733; and ADC: 0.831) for differentiating MVI-positive from MVI-negative HCCs.

CONCLUSIONS

The FROC parameters can be used as noninvasive quantitative imaging markers for preoperatively predicting the MVI status of HCCs.

A nomogram model integrating LI-RADS features and radiomics based on contrast-enhanced magnetic resonance imaging for predicting microvascular invasion in hepatocellular carcinoma falling the Milan criteria

PURPOSE

To establish and validate a nomogram model incorporating both liver imaging reporting and data system (LI-RADS) features and contrast enhanced magnetic resonance imaging (CEMRI)-based radiomics for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC) falling the Milan criteria.

METHODS

In total, 161 patients with 165 HCCs diagnosed with MVI (n = 99) or without MVI (n = 66) were assigned to a training and a test group. MRI LI-RADS characteristics and radiomics features selected by the LASSO algorithm were used to establish the MRI and Rad-score models, respectively, and the independent features were integrated to develop the nomogram model. The predictive ability of the nomogram was evaluated with receiver operating characteristic (ROC) curves.

RESULTS

The risk factors associated with MVI (P<0.05) were related to larger tumor size, nonsmooth margin, mosaic architecture, corona enhancement and higher Rad-score. The areas under the ROC curve (AUCs) of the MRI feature model for predicting MVI were 0.85 (95% CI: 0.78-0.92) and 0.85 (95% CI: 0.74-0.95), and those for the Rad-score were 0.82 (95% CI: 0.73-0.90) and 0.80 (95% CI: 0.67-0.93) in the training and test groups, respectively. The nomogram presented improved AUC values of 0.87 (95% CI: 0.81-0.94) in the training group and 0.89 (95% CI: 0.81-0.98) in the test group (P<0.05) for predicting MVI. The calibration curve and decision curve analysis demonstrated that the nomogram model had high goodness-of-fit and clinical benefits.

CONCLUSIONS

The nomogram model can effectively predict MVI in patients with HCC falling within the Milan criteria and serves as a valuable imaging biomarker for facilitating individualized decision-making.

Histogram peritumoral enhanced features on MRI arterial phase with extracellular contrast agent can improve prediction of microvascular invasion of hepatocellular carcinoma

BACKGROUND

Preoperative microvascular invasion (MVI) prediction plays an important role in therapeutic decision-making of hepatocellular carcinoma (HCC). This study aimed to investigate the value of histogram based on the arterial phase (AP) of magnetic resonance imaging (MRI) with extracellular contrast agent compared with radiological features for predicting MVI of solitary HCC.

METHODS

In total, 113 patients with pathologically proven solitary HCC were retrospectively enrolled who received surgical resection and underwent preoperative abdominal MRI. The patients were divided into the ≤3 cm [small HCC (sHCC)] cohort and the >3 cm cohort. Based on pathological analysis of surgical specimens, the patients were classified into MVI negative (MVI-) and MVI positive (MVI+) groups. Peritumoral and intratumoral histogram features [mean, median, standard deviation (Std), coefficient of variation (CV), skewness, kurtosis] were acquired on AP subtraction images and radiological features [size, capsule, corona enhancement, corona enhancement thickness (CET), CET group]. Receiver operating characteristic (ROC) curve was constructed to assess predictive capability. Subgroup analysis of patients with a visible corona enhancement based on the CET cut-off value was performed.

RESULTS

None of the features extracted from the intratumor area were significantly different between the MVI+ and MVI- groups in both cohorts. Histogram defined peritumoral (peri-) mean, median, kurtosis, and radiological features including CET and CET group were associated with MVI in sHCCs. Peri-mean, median, Std and radiological features including incomplete capsule, CET, and CET group were associated with MVI in HCC >3 cm. In multivariate logistic regression analysis, the CET group and peri-mean were independent predictors for HCC >3 cm with an area under the curve (AUC) of 0.741. Peri-mean was an independent predictor for sHCC (AUC =0.798). Subgroup analysis of the corona enhancement using 8 mm as a cut-off value showed 100% sensitivity and negative predictive value (NPV).

CONCLUSIONS

Peritumoral AP enhanced degree on MRI showed an encouraging predictive performance for preoperative prediction of MVI, especially in sHCCs. CET ≤8 mm could be used as a negative predictive marker for MVI.

A novel classification in predicting prognosis and guiding postoperative management after R0 liver resection for patients with hepatocellular carcinoma and microvascular invasion

BACKGROUND

Microvascular invasion (MVI) is a significant risk factor affecting survival outcomes of patients after R0 liver resection (LR) for hepatocellular carcinoma (HCC). The current classification of MVI is not refined enough to prognosticate long-term survival of these patients, and a new MVI classification is needed.

METHODS

Patients with HCC who underwent R0 LR at the Eastern Hepatobiliary Surgery Hospital from January 2013 to December 2013 and with resected specimens showing MVI were included in this study with an aim to establish a novel MVI classification. The classification which was developed using multivariate cox regression analysis was externally validated.

RESULTS

There were 180 patients in the derivation cohort and 131 patients in the external validation cohort. The following factors were used for scoring: α-fetoprotein level (AFP), liver cirrhosis, tumor number, tumor diameter, MVI number, and distance between MVI and HCC. Three classes of patients could be distinguished by using the total score: class A, ≤3 points; class B, 3.5-5 points and class C, >5 points with distinct long-term survival outcomes (median recurrence free survival (mRFS), 22.6, 10.2, and 1.9 months, P < 0.001). The predictive accuracy of this classification was more accurate than the other commonly used classifications for HCC patients with MVI. In addition, the mRFS of class C patients was significantly prolonged (1.9 months vs. 6.2 months, P < 0.001) after adjuvant transcatheter arterial chemoembolization (TACE).

CONCLUSIONS

A novel MVI classification was established in predicting prognosis of HCC patients with MVI after R0 LR. Adjuvant TACE was useful for class C patients.

A nomogram based on bi-regional radiomics features from multimodal magnetic resonance imaging for preoperative prediction of microvascular invasion in hepatocellular carcinoma

Background

We aimed to develop and validate a nomogram combining bi-regional radiomics features from multimodal magnetic resonance imaging (MRI) and clinicoradiological characteristics to preoperatively predict microvascular invasion (MVI) of hepatocellular carcinoma (HCC).

Methods

A total of 267 HCC patients were divided into training (n=194) and validation (n=73) cohorts according to MRI data. Bi-regional features were extracted from whole tumors and peritumoral regions in multimodal MRI. The minimum redundancy maximum relevance (mRMR) algorithm was applied to select features and build signatures. The predictive performance of the optimal radiomics signature was further evaluated within subgroups defined by tumor size and alpha fetoprotein (AFP) level. Then, a radiomics nomogram including the optimal radiomics signature, radiographic descriptors, and clinical variables was developed using multivariable regression. The nomogram performance was evaluated based on its discrimination, calibration, and clinical utility.

Results

The fusion radiomics signature derived from triphasic dynamic contrast-enhanced (DCE) MR images can effectively classify MVI and non-MVI HCC patients, with an AUC of 0.784 (95% CI: 0.719-0.840) in the training cohort and 0.820 (95% CI: 0.713-0.900) in the validation cohort. The fusion radiomics signature also performed well in the subgroups defined by the two risk factors, respectively. The nomogram, consisting of the fusion radiomics signature, arterial peritumoral enhancement, and AFP level, outperformed the clinicoradiological prediction model in the validation cohort (AUCs: 0.858 vs. 0.729; P=0.022), fitting well in the calibration curves (P>0.05). Decision curves confirmed the clinical utility of the nomogram.

Conclusions

The radiomics nomogram can serve as a visual predictive tool for MVI in HCCs, and thus assist clinicians in selecting optimal treatment strategies to improve clinical outcomes.

Can living donor liver transplantation offer similar outcomes to deceased donor liver transplantation using expanded selection criteria for hepatocellular carcinoma?

OBJECTIVE

To compare the outcomes of living donor liver transplantation (LDLT) versus deceased donor liver transplantation (DDLT) for patients with hepatocellular carcinoma (HCC) in different selection criteria.

METHODS

Data of patients with HCC who underwent liver transplantation between 2005 and 2013 at our center were reviewed. Clinical data of LDLT recipients and DDLT recipients were compared. The postoperative recurrence-free survival (RFS) rate and overall survival (OS) rate after LDLT versus DDLT were compared in the Milan recipients, the University of California, San Francisco (UCSF) recipients, the up-to-seven recipients, the Hangzhou recipients and the Chengdu recipients.

RESULTS

Data of 255 patients were retrospectively reviewed in this study. Seventeen DDLT recipient and 9 LDLT recipients died during the perioperative period. Among the remaining 229 recipients (NLDLT=66, NDDLT=163), 96 patients met the Milan criteria, 123 recipients met the UCSF criteria, 135 patients met the up-to-seven criteria, 216 patients met the Hangzhou criteria, and 229 recipients met the Chengdu criteria. The overall RFS and OS rates of the Milan recipients, the UCSF recipients, the up-to-seven recipients, the Hangzhou recipients and the Chengdu recipients after LDLT and DDLT were all similar.

CONCLUSION

Using well-studied selection criteria, LDLT offers similar outcomes to DDLT for patient with HCC, even using expanded selection criteria.