Hepatocellular carcinoma: Liver biopsy in the balance

Near-Infrared II Fluorescence Imaging Highlights Tumor Angiogenesis in Hepatocellular Carcinoma with a VEGFR-Targeted Probe

Hepatocellular carcinoma (HCC) is typically characterized by rich vascularity, with angiogenesis playing a crucial role in its growth and invasion. Molecular imaging of specific receptors in blood vessels is crucial in HCC diagnosis. In particular, in vivo imaging utilizing the second near-infrared (NIR-II) window offers improved tissue penetration, reduced light scattering, and lower autofluorescence. Despite the great potential of the NIR-II window, developing safe and effective probes to provide better imaging performance for HCC is urgently needed. In this study, NIR-II imaging integrated with a vascular endothelial growth factor receptor (VEGFR)-targeted probe generated by combining a VEGFR-targeted peptide with indocyanine green (ICG) is used to characterize HCC-related angiogenesis at a resolution of 56.0 µm. For the first time, liver metabolic curves and parameters of liver function reserve (LFR) are obtained by fitting NIR-II fluorescence signals with high spatiotemporal resolution, showing significant differences between HCC mice and controls. Moreover, unlike ICG, the targeting probe has a targeted effect on blood vessels in vivo. The tumor-to-normal (T/N) ratio in NIR-II imaging reaches up to 3.30 after post-injection of the targeting probe. The results indicate that the VEGFR-targeted probe is a powerful tool for NIR-II fluorescence imaging to enhance early diagnosis of HCC.

CEMRI-Based Quantification of Intratumoral Heterogeneity for Predicting Aggressive Characteristics of Hepatocellular Carcinoma Using Habitat Analysis: Comparison and Combination of Deep Learning

RATIONALE AND OBJECTIVES

To explore both an intratumoral heterogeneity (ITH) model based on habitat analysis and a deep learning (DL) model based on contrast-enhanced magnetic resonance imaging (CEMRI) and validate its efficiency for predicting microvascular invasion (MVI) and pathological differentiation in hepatocellular carcinoma (HCC).

METHODS

CEMRI images were retrospectively obtained from 277 HCCs in 265 patients. Habitat analysis and DL features were extracted from the CEMRI images and selected with the least absolute shrinkage and selection operator approach to develop ITH and DL models, respectively, and these robust features were then integrated to design a fusion model for predicting MVI and poorly differentiated HCC (pHCC). The predictive value of the three models was assessed using the area under the receiver operating characteristic curve (AUC).

RESULTS

The training and validation sets comprised 221 HCCs and 56 HCCs, respectively. The ITH and DL models presented AUC values of (0.90 vs. 0.87) for predicting MVI in the training set, with AUC values of 0.86 and 0.83 in the validation set. The AUC values of the ITH model to predict pHCC were 0.90 and 0.86 in the two sets, respectively; they were 0.84 and 0.80 for the DL model. The fusion model yielded the best performance for predicting MVI and pHCC in the training set (AUC=0.95, 0.90) and in the validation set (AUC=0.89, 0.87), respectively.

CONCLUSION

A fusion model integrating ITH and DL features derived from CEMRI images can serve as an excellent imaging biomarker for predicting aggressive characteristics in HCC.

Machine Learning-Based CEMRI Radiomics Integrating LI-RADS Features Achieves Optimal Evaluation of Hepatocellular Carcinoma Differentiation

Purpose

To develop and compare various machine learning (ML) classifiers that employ radiomics extracted from contrast-enhanced magnetic resonance imaging (CEMRI) for diagnosing pathological differentiation of hepatocellular carcinoma (HCC), and validate the performance of the best model.

Methods

A total of 251 patients with HCCs (n = 262) were assigned to a training (n = 200) cohort and a validation (n = 62) cohort. A collection of 5502 radiomics signatures were extracted from the CEMRI images for each HCC nodule. To reduce redundancy and dimensionality, Spearman rank correlation, minimum redundancy maximum relevance (mRMR), and the least absolute shrinkage and selection operator (LASSO) approach were employed. Eight ML classifiers were trained to obtain the best radiomics model. The performance of each model was evaluated based on the area under the receiver operating characteristic curve (AUC). The radiomics model was integrated with liver imaging reporting and data system (LI-RADS) features to design a combined model.

Results

The eXtreme Gradient Boosting (XGBoost)-based radiomics model outperformed other ML classifiers in evaluating pHCC, achieving an AUC of 1.00 and accuracy of 1.00 in the training cohort. The LI-RADS model demonstrated an AUC value of 0.77 and 0.82 in the training and validation cohorts. The combined model exhibited best performance in both the training and validation cohorts, with AUCs of 1.00 and 0.86 for evaluating HCC differentiation, respectively.

Conclusion

CEMRI radiomics integrating LI-RADS features demonstrated excellent performance in evaluating HCC differentiation, suggesting an optimal clinical decision tool for individualized diagnosis of HCC differentiation.

Consensus on the tertiary prevention of primary liver cancer

To effectively prevent recurrence, improve the prognosis and increase the survival rate of primary liver cancer (PLC) patients with radical cure, the Chinese Society of Hepatology, Chinese Medical Association, invited clinical experts and methodologists to develop the Consensus on the Tertiary Prevention of Primary Liver Cancer, which was based on the clinical and scientific advances on the risk factors, histopathology, imaging finding, clinical manifestation, and prevention of recurrence of PLC. The purpose is to provide a current basis for the prevention, surveillance, early detection and diagnosis, and the effective measures of PLC recurrence.

Combining serum AFP and CEUS LI-RADS for better diagnostic performance in Chinese high-risk patients

PURPOSE

To evaluate and compare the diagnostic performance of revised contrast-enhanced ultrasound (CEUS) Liver Imaging Reporting and Data System version by combining LR-M category and serum alpha-fetoprotein (AFP) under different cut-off values.

MATERIAL AND METHODS

This retrospective study enrolled 152 high-risk patients with 152 histology-proven nodules. For revised LI-RADS, nodules in LR-M with different elevated AFP thresholds have been reclassified as the LR-5 category. The diagnostic performances of original and revised CEUS LI-RADS were evaluated and compared.

RESULTS

To compare with the original version, the sensitivity of revised LR-5 (adjusted with AFP value > 200 ng/ml or 400 ng/ml) for the diagnosis of hepatocellular carcinoma (HCC) improved from 52.5 to 69.2% or 65.0%, respectively (both p < 0.001) without compromising specificity (87.5% vs. 71.9% or 78.1%, respectively, both p > 0.05). For the diagnosis of non-HCC malignancy, the specificity of the LR-M after reclassification was improved (69.6% vs. 84.4% or 80.7%, respectively, both p < 0.001) with a non-significant sensitivity reduction (100.0 vs. 70.6% or 82.4%, respectively, both p > 0.05). After modification, the sensitivity of LR-5 also increased to 69.1% or 64.9% (both p < 0.001), while the specificity and PPV did not change (both p > 0.05) for larger nodules (> 20 mm).

CONCLUSION

The diagnostic performance of CEUS LI-RADS can be further improved by reclassifying LR-M nodules with elevated AFP thresholds to LR-5.

Development and validation of MRI-based model for the preoperative prediction of macrotrabecular hepatocellular carcinoma subtype

BACKGROUND

Macrotrabecular hepatocellular carcinoma (MTHCC) has a poor prognosis and is difficult to diagnose preoperatively. The purpose is to build and validate MRI-based models to predict the MTHCC subtype.

METHODS

Two hundred eight patients with confirmed HCC were enrolled. Three models (model 1: clinicoradiologic model; model 2: fusion radiomics signature; model 3: combined model 1 and model 2) were built based on their clinical data and MR images to predict MTHCC in training and validation cohorts. The performance of the models was assessed using the area under the curve (AUC). The clinical utility of the models was estimated by decision curve analysis (DCA). A nomogram was constructed, and its calibration was evaluated.

RESULTS

Model 1 is easier to build than models 2 and 3, with a good AUC of 0.773 (95% CI 0.696-0.838) and 0.801 (95% CI 0.681-0.891) in predicting MTHCC in training and validation cohorts, respectively. It performed slightly superior to model 2 in both training (AUC 0.747; 95% CI 0.689-0.806; p = 0.548) and validation (AUC 0.718; 95% CI 0.618-0.810; p = 0.089) cohorts and was similar to model 3 in the validation (AUC 0.866; 95% CI 0.801-0.928; p = 0.321) but inferior in the training (AUC 0.889; 95% CI 0.851-0.926; p = 0.001) cohorts. The DCA of model 1 had a higher net benefit than the treat-all and treat-none strategy at a threshold probability of 10%. The calibration curves of model 1 closely aligned with the true MTHCC rates in the training (p = 0.355) and validation sets (p = 0.364).

CONCLUSION

The clinicoradiologic model has a good performance in diagnosing MTHCC, and it is simpler and easier to implement, making it a valuable tool for pretherapeutic decision-making in patients.

Deep Learning-Based Classification of Hepatocellular Nodular Lesions on Whole-Slide Histopathologic Images

BACKGROUND & AIMS

Hepatocellular nodular lesions (HNLs) constitute a heterogeneous group of disorders. Differential diagnosis among these lesions, especially high-grade dysplastic nodules (HGDNs) and well-differentiated hepatocellular carcinoma (WD-HCC), can be challenging, let alone biopsy specimens. We aimed to develop a deep learning system to solve these puzzles, improving the histopathologic diagnosis of HNLs (WD-HCC, HGDN, low-grade DN, focal nodular hyperplasia, hepatocellular adenoma), and background tissues (nodular cirrhosis, normal liver tissue).

METHODS

The samples consisting of surgical and biopsy specimens were collected from 6 hospitals. Each specimen was reviewed by 2 to 3 subspecialists. Four deep neural networks (ResNet50, InceptionV3, Xception, and the Ensemble) were used. Their performances were evaluated by confusion matrix, receiver operating characteristic curve, classification map, and heat map. The predictive efficiency of the optimal model was further verified by comparing with that of 9 pathologists.

RESULTS

We obtained 213,280 patches from 1115 whole-slide images of 738 patients. An optimal model was finally chosen based on F1 score and area under the curve value, named hepatocellular-nodular artificial intelligence model (HnAIM), with the overall 7-category area under the curve of 0.935 in the independent external validation cohort. For biopsy specimens, the agreement rate with subspecialists' majority opinion was higher for HnAIM than 9 pathologists on both patch level and whole-slide images level.

CONCLUSIONS

We first developed a deep learning diagnostic model for HNLs, which performed well and contributed to enhancing the diagnosis rate of early HCC and risk stratification of patients with HNLs. Furthermore, HnAIM had significant advantages in patch-level recognition, with important diagnostic implications for fragmentary or scarce biopsy specimens.

An assessment of liver lesions using a combination of CEUS LI-RADS and AFP

PURPOSE

To improve noninvasive diagnosis of HCC using a combination of CE US LI-RADS and alpha-fetoprotein (AFP).

METHODS

757 solitary liver nodules from 757 patients at risk of HCC with CE US and serum AFP test were categorized as LR-1 to LR-5 through LR-M according to CE US LI-RADS version 2017. In LR-3, LR-4, and LR-M nodules, those with AFP > 200 ng/ml were reclassified as mLR-5. Nodules with LR-5 and mLR-5 were reclassified as definitely HCC to modify CE US LI-RADS. Diagnostic performance was assessed with specificity, sensitivity, and PPV.

RESULTS

The sensitivity, specificity, and PPV of LR-5 as a predictor of HCC were 64.7%, 97.8%, and 98.9%, respectively. 32.1% patients with solitary liver nodule had AFP greater than 200 ng/ml, of which 98.8% were HCC (25.8%, 7.5%, 2.5% assigned to LR-M, LR-4, LR-3, respectively) and 1.2% were Combined Hepatocellular Cholangiocarcinoma. After modification, the sensitivity increased to 79.6% (P < 0.001), while specificity and PPV remained high (96.6% and 98.7%, P > 0.050).

CONCLUSION

The combination of CE US LI-RADS and AFP for diagnosing HCC improved diagnostic sensitivity significantly, while maintaining high PPV and specificity in patients with the solitary liver nodule.

Development and validation of a non-invasive model for diagnosing HBV-related liver cirrhosis

BACKGROUND

Liver cirrhosis is closely related to the abnormal liver function and occurrence of liver cancer. Accurate non-invasive assessment of liver cirrhosis is of great significance for preventing disease progression and treatment decision-making. We aim to develop and validate a non-invasive diagnostic model for liver cirrhosis in patients with chronic hepatitis B (CHB).

METHODS

From July 2015 to April 2017, seven-hundred fifty-four patients with primary HBV-related liver cancer who underwent hepatectomy were reprospectively recruited. All patients were examined with 2D-SWE and serologic testing preoperatively, which were utilized for measurement of liver stiffness and serum fibrosis models. The stage of liver fibrosis was evaluated using a resected liver specimen. Least absolute shrinkage and selection operator (Lasso) regression was used for feature selection and binary logistic regression analysis was chosen to build a diagnostic model, which was presented as a nomogram and evaluated for calibration, discrimination and clinical usefulness. The performance of noninvasive model was then prospectively validated in an independent cohort (361 patients) by the ROC curve analysis.

RESULTS

The diagnostic model, which consists of 5 selected clinical characteristics (PIII-NP, IV-C, Hyaluronan, Platelet and Liver stiffness), showed the strongest correlation with liver fibrosis stage (ρ = 0.702, P < 0.05). Compared with APRI, FIB-4, King's Score, and Forns Index, the model presented the optimal discrimination and the best predictive performance with the highest AUC in the training cohort (0.866, 95%CI 0.840-0.892, P < 0.05) and validation cohorts (0.852, 95%CI 0.813-0.890, P < 0.05). Decision curve analysis demonstrated that nomogram based on the model was extremely useful for diagnosing cirrhosis in patients with chronic hepatitis B.

CONCLUSION

This study proposes a non-invasive diagnostic model that incorporates the clinical predictors which can be conveniently used in the individualized diagnosis of HBV-related liver cirrhosis.

Biomarkers in Glycogen Storage Diseases: An Update

Glycogen storage diseases (GSDs) are a group of 19 hereditary diseases caused by a lack of one or more enzymes involved in the synthesis or degradation of glycogen and are characterized by deposits or abnormal types of glycogen in tissues. Their frequency is very low and they are considered rare diseases. Except for X-linked type IX, the different types are inherited in an autosomal recessive pattern. In this study we reviewed the literature from 1977 to 2020 concerning GSDs, biomarkers, and metabolic imbalances in the symptoms of some GSDs. Most of the reported studies were performed with very few patients. Classification of emerging biomarkers between different types of diseases (hepatics GSDs, McArdle and PDs and other possible biomarkers) was done for better understanding. Calprotectin for hepatics GSDs and urinary glucose tetrasaccharide for Pompe disease have been approved for clinical use, and most of the markers mentioned in this review only need clinical validation, as a final step for their routine use. Most of the possible biomarkers are implied in hepatocellular adenomas, cardiomyopathies, in malfunction of skeletal muscle, in growth retardation, neutropenia, osteopenia and bowel inflammation. However, a few markers have lost interest due to a great variability of results, which is the case of biotinidase, actin alpha 2, smooth muscle, aorta and fibroblast growth factor receptor 4. This is the first review published on emerging biomarkers with a potential application to GSDs.

Gadoxetic acid-enhanced MRI of macrotrabecular-massive hepatocellular carcinoma and its prognostic implications

BACKGROUND & AIMS

Despite the clinical and genetic significance of macrotrabecular-massive hepatocellular carcinoma (MTM-HCC), its characteristics on imaging have not been described. This study aimed to characterise MTM-HCC on gadoxetic acid-enhanced MRI and to evaluate the diagnostic accuracy and prognostic value of these imaging characteristics.

METHODS

We enrolled 3 independent cohorts from 2 tertiary care centres. The 3 cohorts consisted of a total of 476 patients who underwent gadoxetic acid-enhanced MRI and surgical resection for treatment-naïve single HCCs. Independent review of histopathology and MRI by 2 reviewers was performed for each cohort, and inter-reader agreement was evaluated. Based on the result of MRI review in the training cohort (cohort 1), we developed 2 diagnostic criteria for MTM-HCC and evaluated their prognostic significance. The diagnostic performance and prognostic significance were validated in 2 validation cohorts (cohorts 2 and 3).

RESULTS

We developed 2 diagnostic MRI criteria (MRIC) for MTM-HCC: MRIC-1, ≥20% arterial phase hypovascular component; MRIC-2, ≥50% hypovascular component and 2 or more ancillary findings (intratumoural artery, arterial phase peritumoural enhancement, and non-smooth tumour margin). MRIC-1 showed high sensitivity and negative predictive value (88% and 95% in the training cohort, and 88% and 97% in the pooled validation cohorts, respectively), whereas MRIC-2 demonstrated moderate sensitivity and high specificity (47% and 94% in the training cohort, and 46% and 96% in the pooled validation cohorts, respectively). MRIC-2 was an independent poor prognostic factor for overall survival in both training and pooled validation cohorts.

CONCLUSIONS

Using gadoxetic acid-enhanced MRI findings, including an arterial phase hypovascular component, we could stratify the probability of MTM-HCC and non-invasively obtain prognostic information.

LAY SUMMARY

Macrotrabecular-massive hepatocellular carcinoma (MTM-HCC) is a histopathologic subtype of HCC characterised by aggressive biological behaviour and poor prognosis. We developed imaging criteria based on liver MRI that could be used for the non-invasive diagnosis of MTM-HCC. HCCs showing imaging findings of MTM-HCC were associated with poor outcomes after hepatic resection.

Hepatocellular carcinoma: Clinicopathologic associations amidst marked phenotypic heterogeneity

BACKGROUND

Hepatocellular carcinoma (HCC) is characterized by marked phenotypic and molecular heterogeneity. Clinico-morphologic phenotypes and associations are important surrogate markers of molecular aberrations; therefore have immense relevance for targeted therapy. There is paucity of published literature on critical analysis of HCC heterogeneity and morphological alliance.

AIMS

To assess the heterogeneity and dominance of histomorphological features, and to explore clinicopathological associations in HCC.

METHODS

Retrospective cross-sectional study of 217 HCC tissue specimens was performed for the assessment of prevalence of major histological patterns, cytological features, and clinicopathological correlation.

RESULTS

Homogeneous architecture with a single dominant histological pattern was a rarity. Single pattern constituting ≥50 % of the tumour was found in less than 1/5th of the cases. Macrotrabecular HCC represented 9.2 % of cases. The simultaneous presence of 2-3 patterns or atypical variants and/ or cytological characteristics was recorded in 25 % and 30 % respectively. Significant clinicopathological associations: Pseudoglandular with microtrabecular pattern-cholestasis, showed better differentiation and early-stage; macrotrabecular pattern frequently occurred with pleomorphic giant cells, higher tumour stage, higher AFP levels; solid pattern often showed clear cells. Noticeable mutual exclusions were MD bodies with microtrabecular and pseudoglandular patterns; Compact pattern with neutrophilic clusters and cholestasis. Larger tumours were significantly more heterogeneous; however, heterogeneity did not correlate with outcome CONCLUSIONS: HCC displays immense heterogeneity with an amalgamation of different histomorphological patterns and features; nevertheless, there are certain reproducible associations and omissions. Tumor biopsies agree fairly well with large specimens. Characterization of phenotypic heterogeneity, dominance, associations, and exclusions in individual patients provides vital information.

Changing role of histopathology in the diagnosis and management of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common and fatal cancer in the world. HCC frequently presents with advanced disease, has a high recurrence rate and limited treatment options, which leads to very poor prognosis. This warrants urgent improvement in the diagnosis and treatment. Liver biopsy plays very important role in the diagnosis and prognosis of HCC, but with technical advancements and progression in the field of imaging, clinical guidelines have restricted the role of biopsy to very limited situations. Biopsy also has its own problems of needle tract seeding of tumor, small risk of complications, technical and sampling errors along with interpretative errors. Despite this, tissue analysis is often required because imaging is not always specific, limited expertise and lack of advanced imaging in many centers and limitations of imaging in the diagnosis of small, mixed and other variant forms of HCC. In addition, biopsy confirmation is often required for clinical trials of new drugs and targeted therapies. Tissue biomarkers along with certain morphological features, phenotypes and immune-phenotypes that serve as important prognostic and outcome predictors and as decisive factors for therapy decisions, add to the continuing role of histopathology. Advancements in cancer biology and development of molecular classification of HCC with clinic pathological correlation, lead to discovery of HCC phenotypic surrogates of prognostic and therapeutically significant molecular signatures. Thus tissue characteristics and morphology based correlates of molecular subtypes provide invaluable information for management and prognosis. This review thus focuses on the importance of histopathology and resurgence of role of biopsy in the diagnosis, management and prognostication of HCC.