Microvascular invasion in hepatocellular carcinoma

VIM-AS1, which is regulated by CpG methylation, cooperates with IGF2BP1 to inhibit tumor aggressiveness via EPHA3 degradation in hepatocellular carcinoma

Early tumor recurrence in hepatocellular carcinoma (HCC) remains a challenging area, as the mechanisms involved are not fully understood. While microvascular invasion is linked to early recurrence, established biomarkers for diagnosis and prognostication are lacking. In this study, our objective was to identify DNA methylation sites that can predict the outcomes of liver cancer patients and elucidate the molecular mechanisms driving HCC aggressiveness. Using DNA methylome data from HCC patient samples from the CGRC and TCGA databases, we pinpointed hypermethylated CpG sites in HCC. Our analysis revealed that cg02746869 acts as a crucial regulatory site for VIM-AS1 (vimentin antisense RNA1), a 1.8 kb long noncoding RNA. RNA sequencing of HCC cells with manipulated VIM-AS1 expression revealed EPHA3 as a pathogenic target of VIM-AS1, which performs an oncogenic function in HCC. Hypermethylation-induced suppression of VIM-AS1 significantly impacted HCC cell dynamics, particularly impairing motility and invasiveness. Mechanistically, reduced VIM-AS1 expression stabilized EPHA3 mRNA by enhancing the binding of IGF2BP1 to EPHA3 mRNA, leading to increased expression of EPHA3 mRNA and the promotion of HCC progression. In vivo experiments further confirmed that the VIM-AS1‒EPHA3 axis controlled tumor growth and the tumor microenvironment in HCC. These findings suggest that the downregulation of VIM-AS1 due to hypermethylation at cg02746869 increased EPHA3 mRNA expression via a m6A-dependent mechanism to increase HCC aggressiveness.

Predicting Microvascular Invasion in Liver Transplant Recipients for Hepatocellular Carcinoma

BACKGROUND

Among primary liver tumors, hepatocellular carcinoma (HCC) is considered the most common hepatic tumor. Liver transplantation is one of the curative treatment options for HCC. However, the risk of HCC recurrence after liver transplantation varies and is influenced by various factors. Microvascular invasion (MVI) is a major factor associated with HCC recurrence after a liver transplant (LT). The study assessed the pre-transplant factors to predict MVI on explant liver specimens.

METHODS

The retrospective study included adult LT recipients with HCC on explant specimens to identify pre-transplant predictors of MVI. Univariate analyses, including Mann-Whitney U tests and chi-square tests, were conducted to assess associations between variables and MVI. Logistic regression was employed for multivariate analysis, including variables significant in univariate analysis. Pearson or Spearman correlation coefficients were calculated to examine correlations between continuous variables. Cohen's kappa coefficient was used to measure inter-rater reliability.

RESULTS

Out of 523 LT recipients, 136 (26%) were diagnosed with HCC based on pre-transplant imaging and histopathological analysis of the explanted liver. Descriptive data showed an average age of 54.06 ± 8.16 years (range: 15-70), with a majority being male (76.47%). Hepatitis C (HCV) was the leading etiology (72.8%). Most patients had moderately differentiated grade-II tumors (75.7%) and met the Milan criteria (74.3%). Mean pre-operative alpha-fetoprotein (pre-op AFP) levels were 104.42 ± 308.38 ng/ml. 74.3% were within the Milan criteria. MVI was present in 28.7%. The frequency of MVI among HCCs within vs. outside Milan criteria was not statistically significant (26.73% vs. 34.28% (p = 0.395)). Univariate analysis revealed that pre-op AFP levels (p = 0.001), Child-Turcotte Pugh class (p=0.05), and body mass index (p=0.02) were significantly associated with MVI. Multivariate logistic regression analysis showed that pre-op AFP was the only independent predictor of MVI (OR: 1.006, 95% CI: 1.003-1.008, p < 0.001).

CONCLUSION

This study not only reinforces the clinical significance of pre-op AFP levels as a simple pre-transplant predictor of MVI in patients with HCC but also advocates for the safety of liver transplantation beyond conventional Milan criteria, promoting extended LT protocols.

Primary treatments for solitary hepatocellular carcinoma ≤ 3 cm: A systematic review and network meta-analysis

Various treatment modalities are available for small solitary hepatocellular carcinoma (HCC), yet the optimal primary treatment strategy for tumors ≤ 3 cm remains unclear. This network meta-analysis investigates the comparative efficacy of various interventions on the long-term outcomes of patients with solitary HCC ≤ 3 cm. A systematic search of electronic databases from January 2000 to December 2023 was conducted to identify studies that compared at least two of the following treatments: surgical resection (SR), radiofrequency ablation (RFA), microwave ablation (MWA), and transarterial chemoembolization (TACE). Survival data were extracted, and pooled hazard ratios with 95% confidence intervals were calculated using a frequentist network meta-analysis. A total of 30 studies, comprising 2 randomized controlled trials and 28 retrospective studies, involving 8,053 patients were analyzed. Surgical resection showed the highest overall survival benefit with a p-score of 0.95, followed by RFA at 0.59, MWA at 0.23, and TACE, also at 0.23. Moreover, SR provided the most significant recurrence-free survival advantage, with a p-score of 0.95, followed by RFA at 0.31 and MWA at 0.19. Sensitivity analyses, excluding low-quality or retrospective non-matched studies, corroborated these findings. This network meta-analysis demonstrates that SR is the most effective first-line curative treatment for single HCC ≤ 3 cm, followed by RFA in patients with preserved liver function. The limited data on MWA and TACE underscore the need for further studies.

Value of high frame rate contrast-enhanced ultrasound in predicting microvascular invasion of hepatocellular carcinoma

OBJECTIVES

To investigate the value of vascular morphology on high frame rate contrast-enhanced ultrasound (H-CEUS) and CEUS Li-RADS in predicting microvascular invasion (MVI), Ki-67 expression and recurrence of hepatocellular carcinoma (HCC).

METHODS

This retrospective study enrolled 78 patients with single HCC diagnosed by postoperative pathology between January 1, 2021, and June 30, 2022. All patients underwent ultrasound and H-CEUS examination before operation. H-CEUS image features and CEUS Li-RADS were compared in different MVI status and Ki-67 level. Multiple logistic regression analysis was performed to select independent variables for MVI. Differences in recurrence among different H-CEUS image features, MVI status and Ki-67 level were further analyzed.

RESULTS

Tumor shape, vascular morphology, LR-M category, necrosis and AFP level were different between the MVI-positive group and MVI-negative group (p < 0.05). Vascular morphology and LR-M category were independent risk factors related to MVI (p < 0.05). Vascular morphology was also different between the high Ki-67 expression group and low Ki-67 expression group (p < 0.05). Vascular morphology, MVI status and Ki-67 expression were different between the recurrence group and no recurrence group (p < 0.05).

CONCLUSION

The vascular morphology of HCC on H-CEUS can indicate the risk of MVI status, Ki-67 expression and recurrence, which provides a feasible imaging technique for predicting the prognosis before operation.

CRITICAL RELEVANCE STATEMENT

H-CEUS shows the different vascular morphology of HCC in arterial phase and indicates the risk of MVI, Ki-67 expression and recurrence, which provides a feasible imaging technique for clinician to judge the risk of MVI pre-operation and adopt appropriate treatment.

KEY POINTS

H-CEUS can clearly show different vascular morphology of HCC in arterial phase. Vascular morphology on H-CEUS is associated with MVI status, Ki-67 expression and HCC recurrence. Preoperative MVI and Ki-67 expression prediction could help surgeons choose a more appropriate treatment plan.

Prediction of microvascular invasion in hepatocellular carcinoma with conventional ultrasound, Sonazoid-enhanced ultrasound, and biochemical indicator: a multicenter study

PURPOSE

To develop and validate a preoperative prediction model based on multimodal ultrasound and biochemical indicator for identifying microvascular invasion (MVI) in patients with a single hepatocellular carcinoma (HCC) ≤ 5 cm.

METHODS

From May 2022 to November 2023, a total of 318 patients with pathologically confirmed single HCC ≤ 5 cm from three institutions were enrolled. All of them underwent preoperative biochemical, conventional ultrasound (US), and contrast-enhanced ultrasound (CEUS) (Sonazoid, 0.6 mL, bolus injection) examinations. Univariate and multivariate logistic regression analyses on clinical information, biochemical indicator, and US imaging features were performed in the training set to seek independent predictors for MVI-positive. The models were constructed and evaluated using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis in both validation and test sets. Subgroup analyses in patients with different liver background and tumor sizes were conducted to further investigate the model's performance.

RESULTS

Logistic regression analyses showed that obscure tumor boundary in B-mode US, intra-tumoral artery in pulsed-wave Doppler US, complete Kupffer-phase agent clearance in Sonazoid-CEUS, and biomedical indicator PIVKA-II were independently correlated with MVI-positive. The combined model comprising all predictors showed the highest AUC, which were 0.937 and 0.893 in the validation and test sets. Good calibration and prominent net benefit were achieved in both sets. No significant difference was found in subgroup analyses.

CONCLUSIONS

The combination of biochemical indicator, conventional US, and Sonazoid-CEUS features could help preoperative MVI prediction in patients with a single HCC ≤ 5 cm.

CRITICAL RELEVANCE STATEMENT

Investigation of imaging features in conventional US, Sonazoid-CEUS, and biochemical indicators showed a significant relation with MVI-positivity in patients with a single HCC ≤ 5 cm, allowing the construction of a model for preoperative prediction of MVI status to help treatment decision making.

KEY POINTS

MVI status is important for patients with a single HCC ≤ 5 cm. The model based on conventional US, Sonazoid-CEUS and PIVKA-II performs best for MVI prediction. The combined model has potential for preoperative prediction of MVI status.

Surgical Implications for Nonalcoholic Steatohepatitis-Related Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is an aggressive form of liver cancer that arises in a background of chronic hepatic injury. Metabolic syndrome-associated fatty liver disease (MAFLD) and its severe form, nonalcoholic steatohepatitis (NASH), are increasingly common mechanisms for new HCC cases. NASH-HCC patients are frequently obese and medically complex, posing challenges for clinical management. In this review, we discuss NASH-specific challenges and the associated implications, including benefits of minimally invasive operative approaches in obese patients; the value of y90 as a locoregional therapy; and the roles of weight loss and immunotherapy in disease management. The relevant literature was identified through queries of PubMed, Google Scholar, and clinicaltrials.gov. Provider understanding of clinical nuances specific to NASH-HCC can improve treatment strategy and patient outcomes.

Identification of prognostic biomarkers for hepatocellular carcinoma with vascular invasion

OBJECTIVE

Vascular invasion (VI) profoundly impacts the prognosis of hepatocellular carcinoma (HCC), yet the underlying biomarkers and mechanisms remain elusive. This study aimed to identify prognostic biomarkers for HCC patients with VI.

METHODS

Transcriptome data from primary HCC tissues and HCC tissues with VI were obtained through the Genome Expression Omnibus database. Differentially expressed genes (DEGs) in the two types of tissues were analyzed using functional enrichment analysis to evaluate their biological functions. We examined the correlation between DEGs and prognosis by combining HCC transcriptome data and clinical information from The Cancer Genome Atlas database. Univariate and multivariate Cox regression analyses, along with the least absolute shrinkage and selection operator (LASSO) method were utilized to develop a prognostic model. The effectiveness of the model was assessed through time-dependent receiver operating characteristic (ROC) curve, calibration diagram, and decision curve analysis.

RESULTS

In the GSE20017 and GSE5093 datasets, a total of 83 DEGs were identified. Gene Ontology analysis indicated that these DEGs were predominantly associated with xenobiotic stimulus, collagen-containing extracellular matrix, and oxygen binding. Additionally, Kyoto Encyclopedia of Genes and Genomes analysis revealed that the DEGs were primarily involved in immune defense and cellular signal transduction. Cox and LASSO regression further identified 7 genes (HSPA8, ABCF2, EAF1, MARCO, EPS8L3, PLA3G1B, C6), which were used to construct a predictive model in the training cohort. We used X-tile software to calculate the optimal cut-off value to stratify HCC patients into low-risk and high-risk groups. Notably, the high-risk group exhibited poorer prognosis than the low-risk group (P < 0.001). The model demonstrated area under the ROC curve (AUC) values of 0.815, 0.730, and 0.710 at 1-year, 3-year, and 5-year intervals in the training cohort, respectively. In the validation cohort, the corresponding AUC values were 0.701, 0.571, and 0.575, respectively. The C-index of the calibration curve for the training and validation cohorts were 0.716 and 0.665. Decision curve analysis revealed the model's efficacy in guiding clinical decision-making.

CONCLUSIONS

The study indicates that 7 genes may be potential prognostic biomarkers and treatment targets for HCC patients with VI.

Preoperative evaluation of microvascular invasion in hepatocellular carcinoma with a radiological feature-based nomogram: a bi-centre study

PURPOSE

To develop a nomogram for preoperative assessment of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) based on the radiological features of enhanced CT and to verify two imaging techniques (CT and MRI) in an external centre.

METHOD

A total of 346 patients were retrospectively included (training, n = 185, CT images; external testing 1, n = 90, CT images; external testing 2, n = 71, MRI images), including 229 MVI-negative patients and 117 MVI-positive patients. The radiological features and clinical information of enhanced CT images were analysed, and the independent variables associated with MVI in HCC were determined by logistic regression analysis. Then, a nomogram prediction model was constructed. External validation was performed on CT (n = 90) and MRI (n = 71) images from another centre.

RESULTS

Among the 23 radiological and clinical features, size, arterial peritumoral enhancement (APE), tumour margin and alpha-fetoprotein (AFP) were independent influencing factors for MVI in HCC. The nomogram integrating these risk factors had a good predictive effect, with AUC, specificity and sensitivity values of 0.834 (95% CI: 0.774-0.895), 75.0% and 83.5%, respectively. The AUC values of external verification based on CT and MRI image data were 0.794 (95% CI: 0.700-0.888) and 0.883 (95% CI: 0.807-0.959), respectively. No statistical difference in AUC values among training set and testing sets was found.

CONCLUSION

The proposed nomogram prediction model for MVI in HCC has high accuracy, can be used with different imaging techniques, and has good clinical applicability.

Factors affecting overall survival and disease-free survival after surgery for hepatocellular carcinoma: a nomogram-based prognostic model-a Western European multicenter study

Few studies have assessed the clinical implications of the combination of different prognostic indicators for overall survival (OS) and disease-free survival (DFS) of resected hepatocellular carcinoma (HCC). This study aimed to evaluate the prognostic factors in HCC patients for OS and DFS outcomes and establish a nomogram-based prognostic model to predict the DFS of HCC. A multicenter, retrospective European study was conducted through the collection of data on 413 consecutive treated patients with a first diagnosis of HCC between January 2010 and December 2020. Univariate and multivariate Cox regression analyses were performed to identify all independent risk factors for OS and DFS outcomes. A nomogram prognostic staging model was subsequently established for DFS and its precision was verified internally by the concordance index (C-Index) and externally by calibration curves. For OS, multivariate Cox regression analysis indicated Child-Pugh B7 score (HR 4.29; 95% CI 1.74-10.55; p = 0.002) as an independent prognostic factor, along with Barcelona Clinic Liver Cancer (BCLC) stage ≥ B (HR 1.95; 95% CI 1.07-3.54; p = 0.029), microvascular invasion (MVI) (HR 2.54; 95% CI 1.38-4.67; p = 0.003), R1/R2 resection margin (HR 1.57; 95% CI 0.85-2.90; p = 0.015), and Clavien-Dindo Grade 3 or more (HR 2.73; 95% CI 1.44-5.18; p = 0.002). For DFS, multivariate Cox regression analysis indicated BCLC stage ≥ B (HR 2.15; 95% CI 1.34-3.44; p = 0.002) as an independent prognostic factor, along with multiple nodules (HR 2.04; 95% CI 1.25-3.32; p = 0.004), MVI (HR 1.81; 95% CI 1.19-2.75; p = 0.005), satellite nodules (HR 1.63; 95% CI 1.09-2.45; p = 0.018), and R1/R2 resection margin (HR 3.39; 95% CI 2.19-5.25; < 0.001). The C-Index of the nomogram, tailored based on the previous significant factors, showed good accuracy (0.70). Internal and external calibration curves for the probability of DFS rate showed optimal consistency and fit well between the nomogram-based prediction and actual observations. MVI and R1/R2 resection margins should be considered as significant OS and DFS predictors, while satellite nodules should be included as a significant DFS predictor. The nomogram-based prognostic model for DFS provides a more effective prognosis assessment for resected HCC patients, allowing for individualized treatment plans.

Therapeutic Efficacy and Safety of Lenvatinib after Atezolizumab Plus Bevacizumab for Unresectable Hepatocellular Carcinoma

A total of 137 HCC patients treated with atezolizumab plus bevacizumab from October 2020 to September 2022 were enrolled. The median overall survival (OS) and progression-free survival (PFS) from the beginning of atezolizumab plus bevacizumab were 21.1 months (range, 18.8 months-not reached) and 10.5 months (range, 8.2-12.1 months), respectively. Fifty patients were diagnosed with progressive disease after atezolizumab plus bevacizumab. Of this group, 24 patients were administered lenvatinib, and the median OS and PFS from the beginning of lenvatinib were 15.3 months (range, 10.5 months-not reached) and 4.0 months (range, 2.5-6.4 months), respectively. The objective response rates based on the response evaluation criteria in solid tumors (RECISTs) criteria version 1.1 and modified RECISTs were 33.3% and 54.2%, respectively. There was no significant difference in the median serum alpha-fetoprotein level between before and after lenvatinib. In the multivariate analysis, Child-Pugh class A (hazard ratio 0.02, 95% confidence interval (CI) 0.02-0.76, p = 0.02) and intrahepatic tumor occupancy rate < 50% (hazard ratio < 0.01, 95% CI 0.003-0.35, p < 0.01) were the significant factors for OS. There were some frequent adverse events (AEs) in patients treated with lenvatinib such as hypertension, fatigue, anorexia, proteinuria, and so on, but none directly caused death. In conclusion, lenvatinib after atezolizumab plus bevacizumab for unresectable HCC should be considered an effective treatment option.

Performances and variability of CT radiomics for the prediction of microvascular invasion and survival in patients with HCC: a matter of chance or standardisation?

OBJECTIVES

To measure the performance and variability of a radiomics-based model for the prediction of microvascular invasion (MVI) and survival in patients with resected hepatocellular carcinoma (HCC), simulating its sequential development and application.

METHODS

This study included 230 patients with 242 surgically resected HCCs who underwent preoperative CT, of which 73/230 (31.7%) were scanned in external centres. The study cohort was split into training set (158 patients, 165 HCCs) and held-out test set (72 patients, 77 HCCs), stratified by random partitioning, which was repeated 100 times, and by a temporal partitioning to simulate the sequential development and clinical use of the radiomics model. A machine learning model for the prediction of MVI was developed with least absolute shrinkage and selection operator (LASSO). The concordance index (C-index) was used to assess the value to predict the recurrence-free (RFS) and overall survivals (OS).

RESULTS

In the 100-repetition random partitioning cohorts, the radiomics model demonstrated a mean AUC of 0.54 (range 0.44-0.68) for the prediction of MVI, mean C-index of 0.59 (range 0.44-0.73) for RFS, and 0.65 (range 0.46-0.86) for OS in the held-out test set. In the temporal partitioning cohort, the radiomics model yielded an AUC of 0.50 for the prediction of MVI, a C-index of 0.61 for RFS, and 0.61 for OS, in the held-out test set.

CONCLUSIONS

The radiomics models had a poor performance for the prediction of MVI with a large variability in the model performance depending on the random partitioning. Radiomics models demonstrated good performance in the prediction of patient outcomes.

CLINICAL RELEVANCE STATEMENT

Patient selection within the training set strongly influenced the performance of the radiomics models for predicting microvascular invasion; therefore, a random approach to partitioning a retrospective cohort into a training set and a held-out set seems inappropriate.

KEY POINTS

• The performance of the radiomics models for the prediction of microvascular invasion and survival widely ranged (AUC range 0.44-0.68) in the randomly partitioned cohorts. • The radiomics model for the prediction of microvascular invasion was unsatisfying when trying to simulate its sequential development and clinical use in a temporal partitioned cohort imaged with a variety of CT scanners. • The performance of the radiomics models for the prediction of survival was good with similar performances in the 100-repetition random partitioning and temporal partitioning cohorts.

IOP Injection, A Novel Superparamagnetic Iron Oxide Particle MRI Contrast Agent for the Detection of Hepatocellular Carcinoma: A Phase II Clinical Trial

BACKGROUND

MRI is crucial in diagnosing hepatocellular carcinoma (HCC). Superparamagnetic iron oxide particles (SPIO) are liver-specific contrast agents which enhance lesions in T2 -weighted images. Iron oxide nano-particle m-PEG-silane (IOP) Injection, a newly developed SPIO, showed promising imaging effects and good safety profile in preclinical studies and in phase I clinical trial.

PURPOSE

To evaluate the safety and clinical validity of IOP Injection as MRI contrast agent in diagnosing HCC.

STUDY TYPE

Prospective.

SUBJECTS

A total of 52 subjects (61.6 ± 11.05 years, 45 males/7 females) with suspected HCC.

FIELD STRENGTH/SEQUENCE

1.5 T, T1 -weighted in/opposed phase, T2 *-weighted gradient echo, T2 -weighted fast spin echo, true fast imaging with steady-state free precession.

ASSESSMENT

Adverse effects and clinical monitoring were recorded throughout the 5-day study. Two independent readers (M.G.H. with 30 years of experience, S.P.K. with 26 years of experience) made the diagnosis. The diagnostic performance of IOP-enhanced MRI was evaluated with sensitivity and positive predictive value by comparing to the pathology reports from subsequent hepatic resection. The number of lesions with various sizes and degrees of differentiation detected by IOP-enhanced MRI was assessed. The relative change in signal intensities over time was indirectly measured from acquired images.

STATISTICAL TESTS

Sensitivity and positive predictive value were used to evaluate the diagnostic performance of IOP-enhanced MRI. Prevalence-adjusted and bias-adjusted 𝜅 coefficient was used to assess the interreader variability.

RESULTS

No serious adverse event related to IOP Injection was found. IOP Injection enhanced the lesion-to-liver contrast ratio in T2 *-weighted images by 50.1% ± 4.8%. IOP-enhanced MRI detected HCC with 100% sensitivity by subject and 96% sensitivity by lesion. IOP Injection visualized subtle vascular invasion as filling defect within vessels in true fast imaging with steady-state free precession (TrueFISP) images.

DATA CONCLUSION

IOP Injection was safe and efficacious as MRI contrast agent in diagnosing HCC in a limited group of subjects.

EVIDENCE LEVEL

2.

TECHNICAL EFFICACY

Stage 2.

Achievement of Complete Response and Drug-Free Status by Atezolizumab plus Bevacizumab Combined with or without Curative Conversion in Patients with Transarterial Chemoembolization-Unsuitable, Intermediate-Stage Hepatocellular Carcinoma: A Multicenter Proof-Of-Concept Study

Introduction

Atezolizumab plus bevacizumab therapy is extremely effective in the treatment of intermediate-stage hepatocellular carcinoma (HCC), with a response rate of 44%, as reported in the IMbrave150 trial. When tumor shrinkage is obtained, achieving complete response (CR) is possible in many cases using curative conversion with resection, ablation, or superselective transarterial chemoembolization (TACE) with curative intent. This concept, i.e., curative conversion by combining systemic therapy and locoregional therapy, has not been reported before. This multicenter proof-of-concept study was conducted to show the value of curative conversion in immunotherapy-treated intermediate-stage HCC meeting TACE-unsuitable criteria.

Methods

This study included 110 consecutive Child-Pugh A patients who received atezolizumab plus bevacizumab as first-line treatment for unresectable and TACE-unsuitable intermediate-stage HCC at seven centers in Japan. CR rate, drug-free rate, time to CR, change in liver function, efficacy in positron emission tomography (PET)-positive HCC, progression-free survival (PFS), and overall survival (OS) were assessed in patients who achieved CR using resection, ablation, superselective TACE with curative intent following atezolizumab plus bevacizumab or atezolizumab plus bevacizumab alone.

Results

Clinical or pathological CR was achieved in 38 patients (35%) (median observation period: 21.2 months). The modalities of curative conversion in 35 patients were as follows: resection, 7; ablation, 13; and superselective TACE, 15. Three patients achieved clinical CR with atezolizumab plus bevacizumab therapy alone. Among the 38 CR patients, 25 achieved drug-free status. PFS was not reached, and 3 patients experienced recurrence after reaching CR. Regarding OS, there were no deaths in any of the CR patients. The albumin-bilirubin score did not deteriorate after locoregional therapy or resection. Of seven PET-positive patients who achieved CR with atezolizumab plus bevacizumab followed by curative conversion, five achieved drug-free status.

Conclusion

The achievement of CR rate by curative conversion in patients treated with atezolizumab plus bevacizumab as the preceding therapy for unresectable and TACE-unsuitable intermediate-stage HCC was 35%. Overall, 23% of patients achieved drug-free status and no recurrence was observed from this patient subgroup with CR and drug-free status. Thus, achieving CR and/or drug-free status should be a therapeutic goal for patients with intermediate-stage HCC without vascular invasion or extrahepatic spread.

Imaging prognostication and tumor biology in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common primary liver malignancy, and represents a significant global health burden with rising incidence rates, despite a more thorough understanding of the etiology and biology of HCC, as well as advancements in diagnosis and treatment modalities. According to emerging evidence, imaging features related to tumor aggressiveness can offer relevant prognostic information, hence validation of imaging prognostic features may allow for better noninvasive outcomes prediction and inform the selection of tailored therapies, ultimately improving survival outcomes for patients with HCC.

Classification of microvascular invasion of hepatocellular carcinoma: correlation with prognosis and magnetic resonance imaging

BACKGROUND/AIMS

The microvascular invasion (MVI) of hepatocellular carcinoma (HCC) involves a wide histological spectrum, and it is unclear whether the degree of MVI correlates with patient prognosis or imaging findings. Here, we evaluate the prognostic value of MVI classification and analyze the radiologic features predictive of MVI.

METHODS

Using a retrospective cohort of 506 patients with resected solitary HCCs, the histological and imaging features of MVI were reviewed and correlated with clinical data.

RESULTS

MVI-positive HCCs invading ≥5 vessels or those with ≥50 invaded tumor cells were significantly associated with decreased overall survival (OS). The 5-year OS, recurrence-free survival (RFS), and beyond Milan criteria RFS rates were significantly poorer in patients with severe MVI compared with those with mild or no MVI. Severe MVI was a significant independent predictive factor for OS (odds ratio [OR], 2.962; p<0.001), RFS (OR, 1.638; p=0.002), and beyond Milan criteria RFS (OR, 2.797; p<0.001) on multivariable analysis. On MRI, non-smooth tumor margins (OR, 2.224; p=0.023) and satellite nodules (OR, 3.264; p<0.001) were independently associated with the severe-MVI group on multivariable analysis. Both non-smooth tumor margins and satellite nodules were associated with worse 5-year OS, RFS, and beyond Milan criteria RFS.

CONCLUSION

Histologic risk classification of MVI according to the number of invaded microvessels and invading carcinoma cells was a valuable predictor of prognosis in HCC patients. Non-smooth tumor margin and satellite nodules were significantly associated with severe MVI and poor prognosis.

Significance of Basal Cell Carcinomas Exhibiting Intravascular Invasion

ABSTRACT

Intravascular invasion of tumor cells can be associated with metastasis in many cancers. Basal cell carcinomas (BCCs), however, rarely metastasize; therefore, the clinical impact of intravascularly invasive BCC (IVBCC) is currently unclear. Because of these facts and the rarity of IVBCC, questions have arisen on whether IVBCC truly exists. We present 4 cases of IVBCC: one case with obvious tumor islands within immunolabeled blood vessels in the context of advanced disease and 3 cases found incidentally during Mohs micrographic surgery. We discuss the difficulty in studying IVBCC, the idea that it could be due to artifact, and the lack of direct clinical-pathological correlation. Given these challenges, we propose diagnostic criteria for IVBCC to decrease ambiguity for pathological diagnosis. Such criteria may facilitate further studies on the clinical significance of IVBCC.

Liver Segment Disposition of Hepatocellular Carcinoma Predicts Microvascular Invasion

Background

Hepatocellular carcinoma (HCC) is a leading cause of cancer morbidity and mortality. Findings of microvascular invasion (MVI) in patients with HCC have emerged as an important prognostic factor for poor survival after tumor resection.

Aim

This study evaluated the relation between MVI and HCC within various anatomical Couinaud's segments of the liver.

Method

A multicenter retrospective review of HCC records was conducted from 2012 to 2017. HCC cases were identified using ICD-9 and 10 codes 155, C22.0, and C22.8. HCC patients who underwent liver transplants were included in this study. Liver segment of the location of HCC was obtained from radiographic records, and MVI information was obtained from pathology reports. Segmental distributions of HCC in MVI versus non-MVI groups were compared using Wilcoxon rank sum tests. p value was set at <0.05.

Results

We analyzed 120 HCC patients who underwent liver transplantation. The mean age of our cohort was 57 years, and the most common etiology of liver disease was hepatitis C at 58.3%. The median HCC size was 3.1 cm, and MVI was present in 23.3% of the explanted specimens. MVI was 2 to 3 times significantly higher in patients with HCC affecting segments 2 and 3 and segments 4b and 5 (p = 0.01). Moreover, median survival was significantly lower in patients with MVI versus those without MVI (50 vs. 137 months, p < 0.05).

Conclusion

MVI was significantly higher in HCC tumors located in liver segments 2 and 3 and 4b and 5, and survival was lower in patients with MVI compared with those without.

Overexpression of miR-4669 Enhances Tumor Aggressiveness and Generates an Immunosuppressive Tumor Microenvironment in Hepatocellular Carcinoma: Its Clinical Value as a Predictive Biomarker

Accumulating evidence suggests the involvement of tumor-derived exosomes in the development and recurrence of hepatocellular carcinoma (HCC). We previously identified miR-4669 as a highly expressed microRNA in circulating exosomes obtained from patients with post-transplant HCC recurrence. This study aimed to explore how overexpression of miR-4669 affects HCC development and recurrence. The impact of miR-4669 overexpression in Hep3B cells on tumor cell behavior and the tumor microenvironment was evaluated in vitro. In addition, the clinical value of exosomal miR-4669 for the prediction of treatment response to HCC downstaging therapies and following post-transplant HCC recurrence was explored. Overexpression of miR-4669 enhanced migration ability and led to acquired sorafenib resistance with an elevation of sirtuin 1 and long noncoding RNA associated with microvascular invasion. Active release of tumor-derived exosomes and glyceraldehyde 3-phosphate dehydrogenase (GAPDH) contributed to generating an immunosuppressive tumor microenvironment through the induction of M2 macrophage polarization. The retrospective analysis demonstrated the clinical value of exosomal miR-4669 for predicting treatment response to HCC downstaging therapies and for risk assessment of post-transplant HCC recurrence. In summary, the present data demonstrate the impact of exosomal miR-4669 on HCC recurrence through the enhancement of tumor aggressiveness and generation of an immunosuppressive tumor microenvironment.

A diagnostic test of three-dimensional magnetic resonance elastography imaging for preoperative prediction of microvascular invasion in patients with T1 stage clear cell renal carcinoma

Background

Detection of microvascular invasion (MVI) of kidney tumors is important for selecting the optimal therapeutic strategy. Currently, the prediction of MVI lacks an accurate imaging biomarker. This study evaluated the performance of three-dimensional (3D) magnetic resonance elastography (MRE) imaging in predicting microvascular invasion (MVI) of T1 stage clear cell renal carcinoma (ccRCC).

Methods

In this prospective study, we conducted pre-surgical imaging with a clinical 3.0 T magnetic resonance imaging (MRI) system. Firstly, 83 consecutive patients were enrolled in this study. A 3D MRE stiffness map was generated and transferred to a post-processing workstation. Contrast-enhanced computed tomography (CT) was conducted to calculate the tumor enhancement ratio. The presence of MVI was evaluated by histopathological analysis and graded according to the risk stratification based upon the number and distribution. The mean stiffness and CT tumor enhancement ratio was calculated for tumors with or without MVI. The diagnostic performance [sensitivity, specificity, positive predictive value, negative predictive value, area under the curve (AUC)] and independent predicting factors for MVI were investigated.

Results

Finally, A total of 80 patients (aged 46.7±13.2 years) were enrolled, including 22 cases of tumors with MVI. The mean MRE stiffness of kidney parenchyma and kidney tumors was 4.8±0.2 and 4.5±0.7 kPa, respectively. There was significant difference in the mean MRE stiffness between tumors with MVI (5.4±0.6 kPa) and tumors without MVI (4.1±0.3 kPa) (P<0.05). The sensitivity, specificity, positive predictive value, negative predictive value, and the AUC for mean stiffness in the prediction of MVI were 100%, 75%, 63%, 96%, and 0.87 [95% confidence interval (CI): 0.72, 0.94], respectively. The corresponding values for the CT tumor enhancement ratio were 90%, 80%, 63%, 96%, and 0.88 (95% CI: 0.71, 0.93), respectively. The odds ratio (OR) value for MRE tumor stiffness and CT kidney tumor enhancement ratio in the prediction of MVI was 2.9 (95% CI: 1.8, 3.7) and 1.2 (95% CI: 1.0, 1.7), respectively (P>0.05).

Conclusions

3D MRE imaging has promising diagnostic performance for predicting MVI in T1 stage ccRCC, which may improve the reliability of surgical strategy selection with T1 stage ccRCC.

Imaging Diagnosis of Hepatocellular Carcinoma: A State-of-the-Art Review

Hepatocellular carcinoma (HCC) remains not only a cause of a considerable part of oncologic mortality, but also a diagnostic and therapeutic challenge for healthcare systems worldwide. Early detection of the disease and consequential adequate therapy are imperative to increase patients' quality of life and survival. Imaging plays, therefore, a crucial role in the surveillance of patients at risk, the detection and diagnosis of HCC nodules, as well as in the follow-up post-treatment. The unique imaging characteristics of HCC lesions, deriving mainly from the assessment of their vascularity on contrast-enhanced computed tomography (CT), magnetic resonance (MR) or contrast-enhanced ultrasound (CEUS), allow for a more accurate, noninvasive diagnosis and staging. The role of imaging in the management of HCC has further expanded beyond the plain confirmation of a suspected diagnosis due to the introduction of ultrasound and hepatobiliary MRI contrast agents, which allow for the detection of hepatocarcinogenesis even at an early stage. Moreover, the recent technological advancements in artificial intelligence (AI) in radiology contribute an important tool for the diagnostic prediction, prognosis and evaluation of treatment response in the clinical course of the disease. This review presents current imaging modalities and their central role in the management of patients at risk and with HCC.

Prediction of microvascular invasion in hepatocellular carcinoma based on preoperative Gd-EOB-DTPA-enhanced MRI: Comparison of predictive performance among 2D, 2D-expansion and 3D deep learning models

Purpose

To evaluate and compare the predictive performance of different deep learning models using gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI in predicting microvascular invasion (MVI) in hepatocellular carcinoma.

Methods

The data of 233 patients with pathologically confirmed hepatocellular carcinoma (HCC) treated at our hospital from June 2016 to June 2021 were retrospectively analyzed. Three deep learning models were constructed based on three different delineate methods of the region of interest (ROI) using the Darwin Scientific Research Platform (Beijing Yizhun Intelligent Technology Co., Ltd., China). Manual segmentation of ROI was performed on the T1-weighted axial Hepatobiliary phase images. According to the ratio of 7:3, the samples were divided into a training set (N=163) and a validation set (N=70). The receiver operating characteristic (ROC) curve was used to evaluate the predictive performance of three models, and their sensitivity, specificity and accuracy were assessed.

Results

Among 233 HCC patients, 109 were pathologically MVI positive, including 91 men and 18 women, with an average age of 58.20 ± 10.17 years; 124 patients were MVI negative, including 93 men and 31 women, with an average age of 58.26 ± 10.20 years. Among three deep learning models, 2D-expansion-DL model and 3D-DL model showed relatively good performance, the AUC value were 0.70 (P=0.003) (95% CI 0.57-0.82) and 0.72 (P<0.001) (95% CI 0.60-0.84), respectively. In the 2D-expansion-DL model, the accuracy, sensitivity and specificity were 0.7143, 0.739 and 0.688. In the 3D-DL model, the accuracy, sensitivity and specificity were 0.6714, 0.800 and 0.575, respectively. Compared with the 3D-DL model (based on 3D-ResNet), the 2D-DL model is smaller in scale and runs faster. The frames per second (FPS) for the 2D-DL model is 244.7566, which is much larger than that of the 3D-DL model (73.3374).

Conclusion

The deep learning model based on Gd-EOB-DTPA-enhanced MRI could preoperatively evaluate MVI in HCC. Considering that the predictive performance of 2D-expansion-DL model was almost the same as the 3D-DL model and the former was relatively easy to implement, we prefer the 2D-expansion-DL model in practical research.

Multiparametric MRI-based radiomics for the prediction of microvascular invasion in hepatocellular carcinoma

BACKGROUND

Preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is essential in obtaining a successful surgical treatment, in decreasing recurrence, and in improving survival.

PURPOSE

To investigate the value of multiparametric magnetic resonance imaging (MRI)-based radiomics in the prediction of peritumoral MVI in HCC.

MATERIAL AND METHODS

A total of 102 patient with pathologically proven HCC after surgical resection from June 2014 to March 2018 were enrolled in this retrospective study. Histological analysis of resected specimens confirmed positive MVI in 48 patients and negative MVI in 54 patients. Radiomics features were extracted from four MRI sequences and selected with the least absolute shrinkage and selection operator (LASSO) regression and used to analyze the tumoral and peritumoral regions for MVI. Univariate logistic regression was employed to identify the most important clinical factors, which were integrated with the radiomics signature to develop a nomogram.

RESULTS

In total, 11 radiomics features were selected and used to build the radiomics signature. The serum level of alpha-fetoprotein was identified as the clinical factor with the highest predictive value. The developed nomogram achieved the highest AUC in predicting MVI status. The decision curve analysis confirmed the potential clinical utility of the proposed nomogram.

CONCLUSION

The multiparametric MRI-based radiomics nomogram is a promising tool for the preoperative diagnosis of peritumoral MVI in HCCs and helps determine the appropriate medical or surgical therapy.

Texture analysis of contrast-enhanced magnetic resonance imaging predicts microvascular invasion in hepatocellular carcinoma

BACKGROUND

Microvascular invasion is one of the important risk factors of postoperative recurrence of hepatocellular carcinoma. Texture analysis uses mathematical methods to analyze the gray's quantitative value and distribution of images, for quantifying the heterogeneity of tissues.

PURPOSE

To investigate the feasibility of predicting MVI in HCC by analyzing the texture features of hepatic MR-enhanced images.

METHODS

110 patients with HCC who underwent MR-enhanced examinations were included in this study, were divided into MVI-positive group (n = 52) and MVI-negative group (n = 58) according to postoperative pathology. Clinical, pathological data and MR imaging features were collected. 11 texture parameters were selected from the gray histogram and gray level co-occurrence matrix (GLCM). Texture parameters of MR-enhanced images were calculated for statistical analysis.

RESULTS

There were statistically significant differences in tumor size, location, degree of differentiation, AFP level, signal, pseudocapsule, margin, peritumoral enhancement and intratumoral artery between MVI-positive group and MVI-negative group (P < 0.05). The AUC value of combining MR image features in prediction of MVI was 0.693(sensitivity and specificity: 53.8 %, 82.8 %, respectively). There were statistically significant differences in the texture parameters of GLCM between two groups (P < 0.05). The AUC value of combining texture parameters in prediction of MVI was 0.797 (sensitivity and specificity: 88.2 %, 62.7 %, respectively).

CONCLUSION

MR image features and texture analysis have certain predictive effect on MVI, which are mutually verified and complementary. The texture parameters of GLCM could reflect tumor heterogeneity, which have great potential to help with preoperative decision. The combination of MR image features and texture analysis may improve the efficiency in prediction of MVI.

A Scoring System for Predicting Microvascular Invasion in Hepatocellular Carcinoma Based on Quantitative Functional MRI

Microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is a histopathological marker and risk factor for HCC recurrence. We integrated diffusion-weighted imaging (DWI) and magnetic resonance (MR) image findings of tumors into a scoring system for predicting MVI. In total, 228 HCC patients with pathologically confirmed MVI who underwent surgical resection or liver transplant between November 2012 and March 2021 were enrolled retrospectively. Patients were divided into a right liver lobe group (n = 173, 75.9%) as the model dataset and a left liver lobe group (n = 55, 24.1%) as the model validation dataset. Multivariate logistic regression identified two-segment involved tumor (Score: 1; OR: 3.14; 95% CI: 1.22 to 8.06; p = 0.017); ADC_min ≤ 0.95 × 10⁻³ mm²/s (Score: 2; OR: 10.88; 95% CI: 4.61 to 25.68; p = 0.000); and largest single tumor diameter ≥ 3 cm (Score: 1; OR: 5.05; 95% CI: 2.25 to 11.30; p = 0.000), as predictive factors for the scoring model. Among all patients, sensitivity was 89.66%, specificity 58.04%, positive predictive value 68.87%, and negative predictive value 84.41%. For validation of left lobe group, sensitivity was 80.64%, specificity 70.83%, positive predictive value 78.12%, and negative predictive value 73.91%. The scoring model using ADC_min, largest tumor diameter, and two-segment involved tumor provides high sensitivity and negative predictive value in MVI prediction for use in routine functional MR.

MVI-Mind: A Novel Deep-Learning Strategy Using Computed Tomography (CT)-Based Radiomics for End-to-End High Efficiency Prediction of Microvascular Invasion in Hepatocellular Carcinoma

Simple Summary

Microvascular invasion is an important indicator for reflecting the prognosis of hepatocellular carcinoma, but the traditional diagnosis requires a postoperative pathological examination. This study is the first to propose an end-to-end deep learning architecture for predicting microvascular invasion in hepatocellular carcinoma by collecting retrospective data. This method can achieve noninvasive, accurate and efficient preoperative prediction only through the patient’s radiomic data, which is very beneficial to doctors for clinical decision making in HCC patients.

Abstract

Microvascular invasion (MVI) in hepatocellular carcinoma (HCC) directly affects a patient’s prognosis. The development of preoperative noninvasive diagnostic methods is significant for guiding optimal treatment plans. In this study, we investigated 138 patients with HCC and presented a novel end-to-end deep learning strategy based on computed tomography (CT) radiomics (MVI-Mind), which integrates data preprocessing, automatic segmentation of lesions and other regions, automatic feature extraction, and MVI prediction. A lightweight transformer and a convolutional neural network (CNN) were proposed for the segmentation and prediction modules, respectively. To demonstrate the superiority of MVI-Mind, we compared the framework’s performance with that of current, mainstream segmentation, and classification models. The test results showed that MVI-Mind returned the best performance in both segmentation and prediction. The mean intersection over union (mIoU) of the segmentation module was 0.9006, and the area under the receiver operating characteristic curve (AUC) of the prediction module reached 0.9223. Additionally, it only took approximately 1 min to output a prediction for each patient, end-to-end using our computing device, which indicated that MVI-Mind could noninvasively, efficiently, and accurately predict the presence of MVI in HCC patients before surgery. This result will be helpful for doctors to make rational clinical decisions.

Effect of Microvascular Invasion on the Prognosis in Hepatocellular Carcinoma and Analysis of Related Risk Factors: A Two-Center Study

Objective: Microvascular invasion is considered to initiate intrahepatic metastasis and postoperative recurrence of hepatocellular carcinoma (HCC). We aimed to analyze the effect of MVI on the prognosis in HCC and identify related risk factors for microvascular invasion (MVI).

Methods: The clinical data of 553 HCC patients who underwent liver surgery at Qingdao University from January 2014 to December 2018 and 89 patients at Beijing Tsinghua Changgung Hospital treated between October 2014 and October 2019 were collected retrospectively. We explored the impact of MVI on the prognosis of patients with HCC using Kaplan-Meier analysis. We conducted logistic regression analysis to identify variables significantly related to MVI.

Results: Pathological examination confirmed the presence of MVI in 265 patients (41.3%). Six factors independently correlated with MVI were incorporated into the multivariate logistic regression analysis: Edmondson-Steiner grade [odds ratio (OR) = 3.244, 95%CI: 2.243–4.692; p < 0.001], liver capsule invasion (OR = 1.755; 95%CI: 1.215–2.535; p = 0.003), bile duct tumor thrombi (OR = 20.926; 95%CI: 2.552–171.553; p = 0.005), α-fetoprotein (> 400 vs. < 400 ng/ml; OR = 1.530; 95%CI: 1.017–2.303; p = 0.041), tumor size (OR = 1.095; 95%CI: 1.027–1.166; p = 0.005), and neutrophil-lymphocyte ratio (OR = 1.086; 95%CI: 1.016–1.162; p = 0.015). The area under the receiver operating characteristic curve (AUC) was 0.743 (95%CI: 0.704–0.781; p < 0.001), indicating that our logistic regression model had significant clinical usefulness.

Conclusions: We analyzed the effect of MVI on the prognosis in HCC and evaluated the risk factors for MVI, which could be helpful in making decisions regarding patients with a high risk of recurrence.

Tumor in vein (LR-TIV) and liver imaging reporting and data system (LI-RADS) v2018: diagnostic features, pitfalls, prognostic and management implications

Vascular invasion by hepatocellular carcinoma (HCC), also known as tumor in vein (TIV), indicates highly invasive tumor behavior and is also associated with poor outcome. Because a diagnosis of TIV precludes liver transplantation, knowledge of the imaging findings to differentiate between TIV and bland thrombus is key for proper patient management. Prior versions of liver imaging reporting and data system (LI-RADS) included presence of TIV as part of LR-5 criteria. However, even if HCC is the most common liver malignancy associated with TIV, other tumors can have vascular invasion and may occur in cirrhotic patients. For these reasons, in LI-RADS v2017 LR-TIV has been introduced as a new different diagnostic category. The aim of this article is to discuss the diagnostic criteria of LR-TIV according to LI-RADS v2018 and analyze potential pitfalls encountered on daily clinical practice. Indeterminate cases and how to manage them will also be discussed.

Prediction of Microvascular Invasion of Hepatocellular Carcinoma Based on Preoperative Diffusion-Weighted MR Using Deep Learning

RATIONALE AND OBJECTIVES

To investigate the value of diffusion-weighted magnetic resonance imaging for the prediction of microvascular invasion (MVI) of Hepatocellular Carcinoma (HCC) using Convolutional Neural Networks (CNN).

MATERIAL AND METHODS

This study was approved by the local institutional review board and the patients' informed consent was waived. Consecutive 97 subjects with 100 HCCs from July 2012 to October 2018 with surgical resection were retrieved. All subjects with diffusion-weighted imaging (DWI) examinations were performed with single-shot echo-planar imaging in a breath-hold routine. DWI parameters were three b values of 0,100,600 sec/mm². First, apparent diffusion coefficients (ADC) images were computed by mono-exponentially fitting the three b-value points. Then, multiple 2D axial patches (28 × 28) of HCCs from b0, b100, b600, and ADC images were extracted to increase the dataset for training the CNN model. Finally, the fusion of deep features derived from three b value images and ADC was conducted based on the CNN model for MVI prediction. The data set was split into the training set (60 HCCs) and the independent test set (40 HCCs). The output probability of the deep learning model in the MVI prediction of HCCs was assessed by the independent student's t-test for data following a normal distribution and Mann-Whitney U test for data violating the normal distribution. Receiver operating characteristic curve and area under the curve (AUC) were also used to assess the performance for MVI prediction of HCCs in the fixed test set.

RESULTS

Deep features in b600 images yielded better performance (AUC = 0.74, p = 0.004) for MVI prediction than b0 (AUC = 0.69, p = 0.023) and b100 (AUC = 0.734, p = 0.011). Comparatively, deep features in the ADC map obtained lower performance (AUC = 0.71, p = 0.012) than that of the higher b value images (b600) for MVI prediction. Furthermore, the fusion of deep features from the b0, b100, b600, and ADC images yielded the best results (AUC = 0.79, p = 0.002) for MVI prediction.

CONCLUSION

Fusion of deep features derived from DWI images concerning the three b-value images and the ADC image yields better performance for MVI prediction.

β-Catenin Expression and Its Association with Prognostic Factors in Hepatocellular Carcinoma: A Study on Alpha-fetoprotein, Histologic Grade, and Microvascular Invasion

Background. Hepatocellular carcinoma (HCC), the most common primary liver cancer. In addition to its high incidence, the disease burden is high due to its poor prognosis and high recurrence rate. Some of the currently known clinicopathologic prognostic factors include alpha-fetoprotein (AFP) level, histologic grade, and microvascular invasion. At the molecular level, β-catenin is one of the most common driver mutation found in HCC. The Wnt/β-catenin pathway regulates cellular processes related to initiation, growth, survival, migration, differentiation, and apoptosis. Although the underlying pathogenesis of hepatocarcinogenesis is known, clinical application warrants a greater understanding of the molecular characteristics and tumor phenotype, especially for determining the prognosis. This study aims to analyze the expression of β-catenin and its association with AFP, histologic grade, and microvascular invasion. Materials and methods. Thirty-five samples of surgically resected HCCs at Cipto Mangunkusumo National Referral Hospital were examined. Diagnoses were made based on histopathological and immunohistochemical findings followed by β-catenin staining. β-catenin expression was analyzed to determine difference between variables. Results and conclusions. Here we show that β-catenin expression is significantly associated with low serum alpha-fetoprotein and well to moderate differentiation Implications. Strong nuclear β-catenin expression implies better prognosis in HCC.

The impact of liver fibrosis on microvascular invasion and prognosis of hepatocellular carcinoma with a solitary nodule: a Surveillance, Epidemiology, and End Results (SEER) database analysis

Background

The pathogenesis of non-cirrhotic hepatocellular carcinoma (HCC) with a high recurrence remains controversial, while microvascular invasion (MVI) is highly suggestive of tumor recurrence. This study aimed to investigate the effects of liver fibrosis on MVI and prognosis in HCC.

Methods

Based on the data of HCC in the Surveillance, Epidemiology, and End Results (SEER) database [2004–2015], multivariate logistic regression was used for correlation analysis. Survival was analyzed by Log-Rank test and Cox regression, and decision curve analysis and receiver operating characteristic curves were established to evaluate alternative diagnostic and prognostic strategies.

Results

The study included 1,492 patients with MVI (17.8%) or without MVI (82.2%) for HCC with a solitary nodule. Liver fibrosis was significantly correlated with the occurrence of MVI, and the risk of MVI in patients with a fibrosis score F5–6 was lower than in those with a score of F0–4 (OR =0.651, 95% CI: 0.492–0.860). Combining liver fibrosis could improve the prediction performance of MVI risk models, but liver fibrosis was less associated with survival outcomes in comparison with other tumor characteristics.

Conclusions

Lower liver fibrosis correlated with a higher risk of MVI in HCC with a solitary nodule and was a good indicator for improving the performance of MVI risk models. However, it was not a prognostic sensitive indicator.

HCC: role of pre- and post-treatment tumor biology in driving adverse outcomes and rare responses to therapy

Liver cancer is the fastest-growing cause of cancer deaths in the United States and is a complex disease. The response of hepatocellular carcinoma (HCC) to treatment can be variable. Predicting response to determine the most effective therapy is an active area of research. Our understanding of underlying factors which drive response to therapy is continually increasing. As more therapies for the treatment of this disease evolve, it is crucial to identify and match the ideal therapy for a particular tumor and patient. The potential predicative imaging features of tumor behavior, while of research interest, have not been validated for clinical use and do not currently inform treatment planning. If further validated though, prognostic features may be used in the future to personalize treatment plans according to individual patients and tumors. Unexpected post-treatment responses such as potential tumor biology changes and abscopal effect which are important to be aware of. This review is intended for radiologists who routinely interpret post treatment HCC imaging and is designed to increase their cognizance about how HCC tumor biology drives response to therapy and explore rare responses to therapy.

Microvascular invasion may be the determining factor in selecting TACE as the initial treatment in patients with hepatocellular carcinoma

ABSTRACT

The aim of this study was to investigate factors affecting tumor necrosis with transcatheter arterial chemoembolization (TACE). Factors associated with early hepatocellular carcinoma recurrence after curative hepatectomy were also evaluated.Data of 51 patients who underwent surgery after a single session of TACE at a single university hospital were retrospectively analyzed. Factors that might affect tumor necrosis were determined by evaluating the TACE approach and by analyzing computed tomography and TACE findings, pathologic reports, and laboratory findings.In univariate analysis, microvascular invasion (MVI), radiological capsule appearance on the computed tomography, chronic hepatitis B, diabetes mellitus and serum albumin, MVI were significantly associated with tumor necrosis by TACE (P < .02). In multivariate analysis, MVI was the only statistically significant factor in TACE-induced tumor necrosis (P = .001). In univariate and multivariate analysis, MVI was the strongest factor for recurrence-free survival rate within 2 years (P = .008, P = .002).MVI could be a crucial factor in determining TACE as an initial treatment for hepatocellular carcinoma. MVI is also a strong indicator of recurrence within 2 years after curative hepatic resection.

State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma

The most common liver malignancy is hepatocellular carcinoma (HCC), which is also associated with high mortality. Often HCC develops in a chronic liver disease setting, and early diagnosis as well as accurate screening of high-risk patients is crucial for appropriate and effective management of these patients. While imaging characteristics of HCC are well-defined in the diagnostic phase, challenging cases still occur, and current prognostic and predictive models are limited in their accuracy. Radiomics and machine learning (ML) offer new tools to address these issues and may lead to scientific breakthroughs with the potential to impact clinical practice and improve patient outcomes. In this review, we will present an overview of these technologies in the setting of HCC imaging across different modalities and a range of applications. These include lesion segmentation, diagnosis, prognostic modeling and prediction of treatment response. Finally, limitations preventing clinical application of radiomics and ML at the present time are discussed, together with necessary future developments to bring the field forward and outside of a purely academic endeavor.

2-[18F]FDG PET/CT as a Predictor of Microvascular Invasion and High Histological Grade in Patients with Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) generally presents a low avidity for 2-deoxy-2-[18F]fluoro-d-glucose (FDG) in PET/CT although an increased FDG uptake seems to relate to more aggressive biological factors. To define the prognostic value of PET/CT with FDG in patients with an HCC scheduled for a tumor resection, forty-one patients were prospectively studied. The histological factors of a poor prognosis were determined and FDG uptake in the HCC lesions was analyzed semi-quantitatively (lean body mass-corrected standardized uptake value (SUL) and tumor-to-liver ratio (TLR) at different time points). The PET metabolic parameters were related to the histological characteristics of the resected tumors and to the evolution of patients. Microvascular invasion (MVI) and a poor grade of differentiation were significantly related to a worse prognosis. The SULpeak of the lesion 60 min post-FDG injection was the best parameter to predict MVI while the SULpeak of the TLR at 60 min was better for a poor differentiation. Moreover, the latter parameter was also the best preoperative variable available to predict any of these two histological factors. Patients with an increased TLRpeak60 presented a significantly higher incidence of poor prognostic factors than the rest (75% vs. 28.6%, p = 0.005) and a significantly higher incidence of recurrence at 12 months (38% vs. 0%, p = 0.014). Therefore, a semi-quantitative analysis of certain metabolic parameters on PET/CT can help identify, preoperatively, patients with histological factors of a poor prognosis, allowing an adjustment of the therapeutic strategy for those patients with a higher risk of an early recurrence.

Microvascular Venous Invasion in Hepatocellular Carcinoma: Why Do Recurrences Occur?

PURPOSE

Hepatocellular carcinoma is the most common primary cancer of the liver. It is almost always associated with cirrhosis and it is usually diagnosed in later stages of the disease. Furthermore, recurrence rate following liver transplantation ranges between 15 and 30%. The most important factor determining the recurrence is vascular invasion.

METHODS

In this review, the issue of microvascular invasion causing hepatocellular carcinoma recurrence is reviewed. Macroscopic vascular invasion is almost easy to diagnose on radiologic evaluation. However, microscopic vascular invasion is almost always diagnosed with pathologic evaluation. On the other hand, microscopic vascular invasion is associated with early recurrences and reduced disease-free survival. The type of vessel that is invaded determines the nature of the spread of the tumor cells. Invasion of the hepatic venous tributaries leads to systemic metastasis whereas portal venous invasions lead to intrahepatic spread of the tumor. Microscopic vascular invasion should be diagnosed before liver transplantation or liver resection in order to deliver the appropriate therapy to the patients.

RESULTS

Yet, there is no ideal marker to suggest microscopic vascular invasion before any intervention. Markers such as alpha-fetoprotein, des carboxy prothrombin, or gamma-glutamyl transferase have been found to be correlated with microscopic vascular invasion. These parameters are not very efficient to be used in routine clinical practice.

CONCLUSION

Therefore, further research is needed to define ideal marker associated with microscopic vascular invasion.

LI-RADS v2018 major criteria: Do hepatocellular carcinomas in non-alcoholic steatohepatitis differ from those in virus-induced chronic liver disease on MRI?

PURPOSE

LI-RADS v2018 diagnostic system is used to diagnose hepatocellular carcinoma (HCC) in at risk patients. However, its applicability to HCC in non-alcoholic steatohepatitis (NASH) has not been specifically studied. The purpose of this study was to assess the applicability of LI-RADS v2018 diagnostic system for HCC in patients with NASH.

MATERIALS AND METHODS

The MRI examinations of 41 patients with HCC and NASH (NASH group) were reviewed and compared to those obtained in 41 patients with HCC and virus-induced chronic liver disease (Virus group). MRI examinations of the two groups were compared for imaging presentation, LI-RADS major criteria and LI-RADS categorization. Qualitative variables were compared using Fisher exact test and quantitative variables using Mann-Whitney U test Interreader agreement was assessed using kappa statistic.

RESULTS

No significant differences in qualitative and quantitative variables were observed between the two groups. Most common findings in the two groups were hyperenhancement during the arterial phase and visibility on T2-weighted images (93 % vs. 98 %, P = 0.616 and 85 % vs. 88 %, P = 1.000 for NASH group and Virus group, respectively). No differences in prevalence between the two groups were found for any major LI-RADS v2018 criterion. Interreader agreement for LI-RADS categorization was strong for the NASH group (kappa = 0.802) and moderate for the virus group (kappa = 0.720). No differences were found between the two groups for LI-RADS categories (P =  0.303).

CONCLUSIONS

The LI-RADS v2018 diagnostic algorithm can be applied in patients with NASH.

Prediction of Microvascular Invasion of Hepatocellular Carcinoma Based on Contrast-Enhanced MR and 3D Convolutional Neural Networks

Background and Purpose

It is extremely important to predict the microvascular invasion (MVI) of hepatocellular carcinoma (HCC) before surgery, which is a key predictor of recurrence and helps determine the treatment strategy before liver resection or liver transplantation. In this study, we demonstrate that a deep learning approach based on contrast-enhanced MR and 3D convolutional neural networks (CNN) can be applied to better predict MVI in HCC patients.

Materials and Methods

This retrospective study included 114 consecutive patients who were surgically resected from October 2012 to October 2018 with 117 histologically confirmed HCC. MR sequences including 3.0T/LAVA (liver acquisition with volume acceleration) and 3.0T/e-THRIVE (enhanced T1 high resolution isotropic volume excitation) were used in image acquisition of each patient. First, numerous 3D patches were separately extracted from the region of each lesion for data augmentation. Then, 3D CNN was utilized to extract the discriminant deep features of HCC from contrast-enhanced MR separately. Furthermore, loss function for deep supervision was designed to integrate deep features from multiple phases of contrast-enhanced MR. The dataset was divided into two parts, in which 77 HCCs were used as the training set, while the remaining 40 HCCs were used for independent testing. Receiver operating characteristic curve (ROC) analysis was adopted to assess the performance of MVI prediction. The output probability of the model was assessed by the independent student's t-test or Mann-Whitney U test.

Results

The mean AUC values of MVI prediction of HCC were 0.793 (p=0.001) in the pre-contrast phase, 0.855 (p=0.000) in arterial phase, and 0.817 (p=0.000) in the portal vein phase. Simple concatenation of deep features using 3D CNN derived from all the three phases improved the performance with the AUC value of 0.906 (p=0.000). By comparison, the proposed deep learning model with deep supervision loss function produced the best results with the AUC value of 0.926 (p=0.000).

Conclusion

A deep learning framework based on 3D CNN and deeply supervised net with contrast-enhanced MR could be effective for MVI prediction.

A Nomogram Estimation for the Risk of Microvascular Invasion in Hepatocellular Carcinoma Patients Meeting the Milan Criteria

OBJECTIVE

We aimed to develop and validate a nomogram for preoperatively estimating the risk of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) within the Milan criteria.

METHODS

The clinical data of 312 HCC patients who underwent liver surgery at the xxx from Jan 2017 to Dec 2019 were retrospectively collected. Then, the study population was categorized into the training and validation group based on the date of surgery. To identify risk factors related to MVI, we conducted a series of logistic regression analyses. By combining these risk factors, a nomogram was then established. We further clarified the usability of our model through the area under the ROC curve (AUC), decision curve analysis (DCA), and calibration curve.

RESULTS

Pathological examination revealed MVI in 108 patients with HCC (34.6%). Three independent predictors were identified: level of alpha-fetoprotein (AFP) exceeds 194 ng/mL (OR = 2.20, 95% CI: 1.13-4.31, p = 0.021), size of tumor (OR = 1.59; 95% CI: 1.18-2.12; P < 0.001) and number of tumors (OR = 3.37, 95% CI: 1.64-7.28, p < 0.001). A nomogram was subsequently built with an AUC of 0.73 and 0.74 respectively in the training cohort and validation cohort. The calibration curve showed a relatively high consistency between predicted probability and observed outcomes. Besides, the DCA revealed that the model was clinically beneficial for preoperatively predicting MVI in HCC.

CONCLUSIONS

A model for evaluating the risk of MVI HCC patients was developed and validated. The model could provide clinicians with a relatively reliable basis for optimizing treatment decisions.

Morphophenotypic Classification of Hepatocellular Carcinoma: the Biliary/Stem Cell Subgroup and Worst Outcome-Implications on Patient Selection

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide and the third cause of cancer-related death. Current clinical/pathological criteria contribute to risk stratification, but are far from the desired on individualized medicine. Recently, HCC classifications have been published based on immunohistochemical and morphological features.

METHODS

A retrospective review of patients submitted to surgical treatment-partial hepatectomy (PH) or liver transplantation (LT), with pathological diagnosis of HCC, in a 9-year period (2007-2015) was performed.

RESULTS

Applying the classification of Srivastava et al. (#1), based on the expression of CD31, p53, AFP and CD44, tumour size and presence of vascular invasion, HCC were categorized as low- and high-risk HCC. With the classification of Tsujikawa et al. (#2), HCC were classified into biliary/stem cell marker positive, Wnt signalling positive and the "all negative" HCC, according to the expression of CK19, SALL4, β-catenin glutamine synthetase, EpCAM and p53. There were sixty-six patients (53 males; 13 females), with median age of 64.5 ± 9.46 years (range 38-86), with solitary HCC, comprehending 37 PH (56.1%) and 29 LT (43.9%). The mean overall survival (OS) was 75.4 ± 6.9 months. Biliary/stem cell type of HCC was a predictive factor of worse OS on the overall population (24.4 versus 78.3 months, p = 0.032) and in PH cohort (11.5 versus 64.01 months, p = 0.016), on uni- and multivariate analyses.

CONCLUSION

These results support the relevance of a risk stratification classification of HCC. Classification #2 seems adequate to our reality demonstrating OS impact, allowing its application in future biopsies, prompting individualized medicine.

Pre-operative Microvascular Invasion Prediction Using Multi-parametric Liver MRI Radiomics

Microvascular invasion (mVI) is the most significant independent predictor of recurrence for hepatocellular carcinoma (HCC), but its pre-operative assessment is challenging. In this study, we investigate the use of multi-parametric MRI radiomics to predict mVI status before surgery. We retrospectively collected pre-operative multi-parametric liver MRI scans for 99 patients who were diagnosed with HCC. These patients received surgery and pathology-confirmed diagnosis of mVI. We extracted radiomics features from manually segmented HCC regions and built machine learning classifiers to predict mVI status. We compared the performance of such classifiers when built on five MRI sequences used both individually and combined. We investigated the effects of using features extracted from the tumor region only, the peritumoral marginal region only, and the combination of the two. We used the area under the receiver operating characteristic curve (AUC) and accuracy as performance metrics. By combining features extracted from multiple MRI sequences, AUCs are 86.69%, 84.62%, and 84.19% when features are extracted from the tumor only, the peritumoral region only, and the combination of the two, respectively. For tumor-extracted features, the T2 sequence (AUC = 80.84%) and portal venous sequence (AUC = 79.22%) outperform other MRI sequences in single-sequence-based models and their combination yields the highest AUC of 86.69% for mVI status prediction. Our results show promise in predicting mVI from pre-operative liver MRI scans and indicate that information from multi-parametric MRI sequences is complementary in identifying mVI.

The impact of metabolic syndrome (MetS) on surgical outcome for patients with mostly HBV-related hepatocellular carcinoma (HCC) underwent hepatectomy

BACKGROUND AND OBJECTIVES

The impact of metabolic syndrome (MetS) on surgical outcome, mostly in patients with HBV-related hepatocellular carcinoma (HCC) who underwent hepatectomy.

METHODS

A propensity score matching analysis was conducted. Patients were categorized into two groups MetS-related hepatocellular carcinoma (MetS-HCC) and 1:1 matched non-MetS-related HCC (non-MetS-HCC). Surgical outcomes were compared between the two groups.

RESULTS

Seventy-four MetS-HCC patients and 74 propensity score-matched non-MetS-HCC patients were selected for analysis. The incidence of surgical site infection was higher in the MetS-HCC group than in the non-MetS-HCC group (12.16% vs 0%, P < .005). There was no difference in the recurrence-free survival and overall survival between patients in the MetS-HCC group and in non-MetS-HCC group (P > .05). Microvascular invasion and severe postoperative complications were independent risk factors for recurrence-free survival and overall survival.

CONCLUSIONS

Hepatectomy for patients with mostly HBV-related HCC in the presence of MetS can result in a higher rate of postoperative surgical site infection compared with those in the absence of MetS, but long-term survival rates are comparable.

Hepatocellular carcinoma in non-cirrhotic versus cirrhotic liver: a clinico-radiological comparative analysis

AIM

To compare clinico-radiological pattern of non-cirrhotic versus cirrhotic HCC and correlate them with histopathological tumor grade.

MATERIALS AND METHODS

This prospective study was carried out on 94 patients enrolled following ultrasound diagnosis of a liver mass measuring > 3 cm. Multiphasic MDCT was performed on all treatment-naïve cases and 56 cases with imaging pattern consistent with unifocal HCC were selected. Background liver parenchyma was assessed on ultrasound for cirrhosis and NAFLD. Cases were categorized into cirrhotic liver (CL) and non-cirrhotic liver (NCL) groups with 26 and 30 cases, respectively, and guided biopsy of each liver mass was performed. AFP levels were compared in both groups. Serum markers for hepatitis B and C were assessed. Masses in both groups were compared for morphology, attenuation on each phase and washout time. Presence of capsule, corona enhancement, satellite nodules and portal vein invasion was noted.

RESULTS

AFP level was higher in CL group. HBV serum marker was raised in both groups. Most HCCs in NCL were moderately differentiated (histopathology), larger, had well-defined margins, showed mosaic pattern of enhancement, complete capsule and delayed phase washout. Majority in CL group were poorly differentiated, smaller, had ill-defined margins, showed heterogeneous enhancement, absent capsule and portal venous phase washout. Time of washout correlated with histopathological differentiation of masses, with earlier washout indicating poorer differentiation.

CONCLUSION

HCCs in NCL have different clinico-radiological characteristics than HCCs in CL. Time of contrast washout correlates with histopathological grade of HCC. Non-cirrhotic NAFLD may require formulation of new screening guidelines for HCC.

Heparanase: Historical Aspects and Future Perspectives

Heparanase is an endo-β-glucuronidase that cleaves at a limited number of internal sites the glycosaminoglycan heparan sulfate (HS). Heparanase enzymatic activity was first reported in 1975 and by 1983 evidence was beginning to emerge that the enzyme was a facilitator of tumor metastasis by cleaving HS chains present in blood vessel basement membranes and, thereby, aiding the passage of tumor cells through blood vessel walls. Due to a range of technical difficulties, it took another 16 years before heparanase was cloned and characterized in 1999 and a further 14 years before the crystal structure of the enzyme was solved. Despite these substantial deficiencies, there was steady progress in our understanding of heparanase long before the enzyme was fully characterized. For example, it was found as early as 1984 that activated T cells upregulate heparanase expression, like metastatic tumor cells, and the enzyme aids the entry of T cells and other leukocytes into inflammatory sites. Furthermore, it was discovered in 1989 that heparanase releases pre-existing growth factors and cytokines associated with HS in the extracellular matrix (ECM), the liberated growth factors/cytokines enhancing angiogenesis and wound healing. There were also the first hints that heparanase may have functions other than enzymatic activity, in 1995 it being reported that under certain conditions the enzyme could act as a cell adhesion molecule. Also, in the same year PI-88 (Muparfostat), the first heparanase inhibitor to reach and successfully complete a Phase III clinical trial was patented.Nevertheless, the cloning of heparanase (also known as heparanase-1) in 1999 gave the field an enormous boost and some surprises. The biggest surprise was that there is only one heparanase encoding gene in the mammalian genome, despite earlier research, based on substrate specificity, suggesting that there are at least three different heparanases. This surprising conclusion has remained unchanged for the last 20 years. It also became evident that heparanase is a family 79 glycoside hydrolase that is initially produced as a pro-enzyme that needs to be processed by proteases to form an enzymatically active heterodimer. A related molecule, heparanase-2, was also discovered that is enzymatically inactive but, remarkably, recently has been shown to inhibit heparanase-1 activity as well as acting as a tumor suppressor that counteracts many of the pro-tumor properties of heparanase-1.The early claim that heparanase plays a key role in tumor metastasis, angiogenesis and inflammation has been confirmed by many studies over the last 20 years. In fact, heparanase expression is enhanced in all major cancer types, namely carcinomas, sarcomas, and hematological malignancies, and correlates with increased metastasis and poor prognosis. Also, there is mounting evidence that heparanase plays a central role in the induction of inflammation-associated cancers. The enzymatic activity of heparanase has also emerged in unexpected situations, such as in the spread of HS-binding viruses and in Type-1 diabetes where the destruction of intracellular HS in pancreatic insulin-producing beta cells precipitates diabetes. But the most extraordinary recent discoveries have been with the realization that heparanase can exert a range of biological activities that are independent of its enzymatic function, most notably activation of several signaling pathways and being a transcription factor that controls methylation of histone tails. Collectively, these data indicate that heparanase is a truly multifunctional protein that has the additional property of cleaving HS chains and releasing from ECM and cell surfaces hundreds of HS-binding proteins with a plethora of functional consequences. Clearly, there are many unique features of this intriguing molecule that still remain to be explored and are highlighted in this Chapter.

Postoperative adjuvant treatment strategy for hepatocellular carcinoma with microvascular invasion: a non-randomized interventional clinical study

BACKGROUND

Microvascular invasion (MVI) is considered to be one of the important prognostic factors that affect postoperative recurrence in patients with hepatocellular carcinoma (HCC) with variable results across their treatment options. This study was carried out to investigate efficacy of postoperative adjuvant RT in HCC patients with MVI.

METHODS

This was single center, prospective study carried out in HCC patients with MVI, aged 35-72 years. All patients were non-randomly allocated to receive standard postoperative treatment of HBV/HCV and nutritional therapy or RT in addition to standard postoperative treatment (1:1). The primary endpoints assessed were relapse-free survival and overall survival. The prognostic factors associated with survival outcomes were also analyzed. The safety events were graded according to NCI-CTCAE v4.03 criteria.

RESULTS

Of the 115 patients eligible for study, 59 patients were included in analysis. Univariate analysis revealed that MVI classification (P = 0.009), post-operative treatment strategies (P = 0.009) were prognostic factors for worst RFS; tumor size (P = 0.011), MVI classification (P = 0.005) and post-operative treatment (P = 0.015) were associated for OS. The 1-, 2-, 3-year RFS rates were 86.2, 70.5 and 63.4% for patients in RT group, and 46.4, 36.1, and 36.1% in control group. For OS, corresponding rates were 96.6, 80.7, and 80.7% for patients in RT group and 79.7, 58.3, and 50.0% in control group. Subgroup classification of HCC patients according to low risk MVI showed significantly longer RFS (P = 0.035) and OS (P = 0.004) in RT group than control group, while for high risk MVI, RT depicted longer OS than control group with no significance (P = 0.106). Toxicities were usually observed in acute stage with no grade 4 toxicities.

CONCLUSION

Postoperative adjuvant RT following hepatectomy offers better RFS for HCC patients with MVI than with standard postoperative therapy. Also, it will be useful to control microscopic lesions in both M1 (low risk) and M2 (high risk) subgroups of HCC patients with MVI.

TRIAL REGISTRATION

Trial Registration number: ChiCTR1800017371 . Date of Registration: 2018-07-26. Registration Status: Retrospectively registered.

The Value of TTPVI in Prediction of Microvascular Invasion in Hepatocellular Carcinoma

Purpose

The objective of our study was to evaluate the value of two-trait predictor of venous invasion (TTPVI) in the prediction of pathological microvascular invasion (pMVI) in patients with hepatocellular carcinoma (HCC) from preoperative computed tomography (CT) and magnetic resonance (MR).

Methods

A total of 128 preoperative patients with findings of HCC were enrolled. Tumor size, tumor margins, tumor capsule, peritumoral enhancement, and TTPVI was assessed on preoperative CT and MRI images. Histopathological features were reviewed: pathological tumor size, tumor differentiation, pMVI along with alpha-fetoprotein level (AFP). Significant imaging findings and histopathological features were determined with univariate and multivariate logistic regression analysis.

Results

Univariate analysis revealed that tumor size (p<0.01), AFP level (p=0.043), tumor differentiation (p<0.01), peritumoral enhancement (p=0.003), pathological tumor size (p<0.01), tumor margins (p<0.01) on CT and MRI, and TTPVI (p<0.01) showed statistically significant associations with pMVI. In multivariate logistic regression analysis, tumor size (odds ratio [OR] = 1.294; 95% confidence interval [CI]: 1.155, 1.451; p < 0.001), tumor differentiation (odds ratio [OR] =1.384; 95% confidence interval [CI]: 1.224, 1.564; p < 0.001), and TTPVI (odds ratio [OR] = 4.802; 95% confidence interval [CI]: 1.037, 22.233; p=0.045) were significant independent predictors of pMVI. Using 5.8 as the threshold for size, one could obtain an area-under-curve (AUC) of 0.793, 95% confidence interval [CI]: 0.715 to 0.857.

Conclusion

Tumor size, tumor differentiation, and TTPVI depicted in preoperative CT and MRI had a statistically significant correlation with pMVI. Hence, TTPVI detected on CT and MRI may be predictive of pMVI in HCC cases.

Microscopic vascular invasion by hepatocellular carcinoma in liver transplant patients

BACKGROUND

A characteristic of Hepatocellular Carcinoma (HCC) is to invade the portal venous system in the liver as a means of spread within the liver and systemically. The ensuing Portal Vein Thrombosis (PVT) is a poor prognosis parameter and often diagnosed radiologically pre-treatment. More limited Microvascular Portal Invasion (microPVI) is typically diagnosed on examination of tumors removed after treatment by resection or transplant. The biological characteristics and subsets of PVI are incompletely characterized.

AIMS

To examine HCC patients with and without microPVI to understand the clinical relationships to other tumor and clinical characteristics and to survival.

METHODS

A cohort of 270 liver transplant patients with HCC without macroscopic PVT that were available to us was examined. Patients with (165) and without (105) microPVI were compared for survival and clinical features.

RESULTS

The mean survival of patients with and without microPVI was significantly different: 86.6 versus 110.5 months, p=0.007.The microPVI+ patients differed from microPVI- patients in having a significantly larger number of tumor nodules, tumor size and higher serum levels of both Alpha-Fetoprotein (AFP) and almost significant for higher Gamma-Glutamyl Transpeptidase (GGT, p=0.053). Survival in microPVI+ patients related significantly to serum GGT (p=0.006) but not to AFP levels. The incidence of microPVI increased with increase in tumor size and survival decreased significantly with increase in tumor size for microPVI patients. Increase in tumor size was also associated with significantly higher serum GGT levels in patients who were microPVI+, but not in those who were microPVI. Furthermore, patients with microPVI who had prolonged survival significantly differed from those with shorter survival in respect only to tumor size and serum GGT levels.

CONCLUSION

These findings draw attention to a group of patients with microPVI who have long survival and to the usefulness of serum GGT levels in their evaluation and prognosis.

Anatomic resection and wide resection margin play an important role in hepatectomy for hepatocellular carcinoma with peritumoural micrometastasis

BACKGROUND

Anatomic hepatectomy and wide resection margin may improve surgical outcome of patients with hepatocellular carcinoma (HCC), but not everyone gain survival benefit. It remains unclear what kind of patients would benefit from those surgical methods. We investigated the factors affecting survival of patients with HCC, with special attention paid to the surgical methods and pathological factors.

METHODS

A retrospective analysis was conducted on 231 patients with hepatitis B-related HCC who underwent surgery from August 2011 to November 2013 in authors' institute. The survival analysis included the following variables: gender, age, viral load, alpha-fetoprotein, des-γ-carboxy prothrombin, tumour size, cirrhosis, blood transfusion, complications, resection method, resection margin, microvascular invasion (mVI), peritumoural satellite nodule, recurrence time and recurrent burden.

RESULTS

The median follow-up time was 59 months. A total of 196 patients (84.9%) recurred and 151 patients (65.4%) deceased due to the disease. Multivariate analysis showed that cirrhosis, mVI and periturmoral satellite nodules were independent risk factors affecting overall survival after operation. The comparison between anatomic resection and local resection, and wide resection margin and narrow resection margin showed no significant differences for recurrence-free survival and overall survival, respectively (P = 0.089 and 0.068, 0.108 and 0.122). Stratified analysis revealed that anatomic resection and wide resection margin surgery improved survival when mVI or peritumoural satellite existed.

CONCLUSION

Anatomic resection and wide resection margin are effective methods to improve the surgical outcome of HCC with periturmoral micrometastasis, although tumour characteristics affect patients' survival more than surgical techniques.

IVIM improves preoperative assessment of microvascular invasion in HCC

PURPOSE

To prospectively evaluate the potential role of intravoxel incoherent motion (IVIM) and conventional radiologic features for preoperative prediction of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC).

METHODS

Institutional review board approval and written informed consent were obtained for this study. A cohort comprising 115 patients with 135 newly diagnosed HCCs between January 2016 and April 2017 were evaluated. Two radiologists independently reviewed the radiologic features and the apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudodiffusion coefficient (D*), and pseudodiffusion component fraction (f) were also measured. Interobserver agreement was checked and univariate and multivariate logistic regressions were used for screening the risk factors. Receiver operating characteristics (ROC) curve analyses were performed to evaluate the diagnostic performance.

RESULTS

Features significantly related to MVI of HCC at univariate analysis were reduced ADC (odds ratio, 0.341; 95% CI, 0.211-0.552; p < 0.001), D (odds ratio, 0.141; 95% CI, 0.067-0.299; p < 0.001), and irregular circumferential enhancement (odds ratio, 9.908; 95% CI, 3.776-25.996; p < 0.001). At multivariate analysis, only D value (odds ratio, 0.096; 95% CI, 0.025-0.364; p < 0.001) was the independent risk factor for MVI of HCC. The mean D value for MVI of HCC showed an area under ROC curves of 0.815 (95% CI, 0.740-0.877).

CONCLUSION

IVIM model-derived D value is superior to ADC measured with mono-exponential model for evaluating the MVI of HCC. Among MR imaging features, tumor margin, enhancement pattern, tumor capsule, and peritumoral enhancement were not predictive for MVI.

KEY POINTS

• Diffusion MRI is useful for non-invasively evaluating the microvascular invasion of hepatocellular carcinoma. • IVIM model is advantageous over mono-exponential model for assessing the microvascular invasion of hepatocellular carcinoma. • Decreased D value was the independent risk factor for predicting MVI of HCC.

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.