Differentiation of hepatocellular adenoma by subtype and hepatocellular carcinoma in non-cirrhotic liver by fractal analysis of perfusion MRI

Hepatocellular adenoma update: diagnosis, molecular classification, and clinical course

Hepatocellular adenomas (HCA) are acquired focal liver lesions, that occur mainly in young-to-middle-aged women who are on long-term estrogen-containing contraceptives or young men after prolonged use of anabolic steroids. Furthermore, distinct underlying diseases, such as obesity, metabolic dysfunction-associated steatotic liver disease, glycogen storage disease, etc. are considered risk factors. The 2017 Bordeaux classification, in particular Nault et al, divided HCAs into eight subtypes according to their pheno- and genotypic characteristics. This includes HCAs with hepatocyte-nuclear-factor (HNF1-alpha mutation), HCAs with β-catenin mutation, and HCAs without either of these genetic mutations, which are further subdivided into HCAs with and without inflammatory cells. HCAs should no longer be classified as purely benign without histologic workup since three of the eight subtypes are considered high-risk lesions, requiring adequate management: malignant transformation of the pure (ßex3-HCA) and mixed inflammatory/β-catenin exon 3 (ßex3-IHCA) adenomas, as well as potential bleeding of the sonic hedgehog HCA and pure (ßex7/8-HCA) and mixed inflammatory/β-catenin exon 7/8 (ßex7/8-IHCA). Elective surgery is recommended for any HCA in a male, or for any HCA exceeding 5 cm. Although MRI can classify up to 80% of adenomas, if findings are equivocal, biopsy remains the reference standard for adenoma subtype.

I thought it was a hemangioma! A pictorial essay about common and uncommon liver hemangiomas' mimickers

Focal liver lesions are frequently encountered during imaging studies, and hemangiomas represent the most common solid liver lesion. Liver hemangiomas usually show characteristic imaging features that enable characterization without the need for biopsy or follow-up. On the other hand, there are many benign and malignant liver lesions that may show one or more imaging features resembling hemangiomas that radiologists must be aware of. In this article we will review the typical imaging features of liver hemangiomas and will show a series of potential liver hemangiomas' mimickers, giving radiologists some hints for improving differential diagnoses. CRITICAL RELEVANCE STATEMENT: The knowledge of imaging features of potential liver hemangiomas mimickers is fundamental to avoid misinterpretation. KEY POINTS: Liver hemangiomas typically show imaging features that enable avoiding a biopsy. Many benign and malignant liver lesions show imaging features resembling hemangiomas. Radiologists must know the potentially misleading imaging features of hemangiomas' mimickers.

Comparing Texture Analysis of Apparent Diffusion Coefficient MRI in Hepatocellular Adenoma and Hepatocellular Carcinoma

AIM

This study aimed to assess the effectiveness of using MRI-apparent diffusion coefficient (ADC) map-driven radiomics to differentiate between hepatocellular adenoma (HCA) and hepatocellular carcinoma (HCC) features.

MATERIALS AND METHODS

The study involved 55 patients with liver tumors (20 with HCA and 35 with HCC), featuring 106 lesions equally distributed between hepatic carcinoma and hepatic adenoma who underwent texture analysis on ADC map MR images. The analysis identified several imaging features that significantly differed between the HCA and HCC groups. Four classification models were compared for distinguishing HCA from HCC including linear support vector machine (linear-SVM), radial basis function SVM (RBF-SVM), random forest (RF), and k-nearest neighbor (KNN).

RESULTS

The k-nearest neighbor (KNN) classifier displayed the top accuracy (0.89) and specificity (0.90). Linear-SVM and KNN classifiers showcased the leading sensitivity (0.88) for both, with the KNN classifier achieving the highest precision (0.9). In comparison, the conventional interpretation had lower sensitivity (70.1%) and specificity (77.9%).

CONCLUSION

The study found that utilizing ADC maps for texture analysis in MR images is a viable method to differentiate HCA from HCC, yielding promising results in identified texture features.

Applications of Dynamic Contrast-Enhanced Ultrasound in Differential Diagnosis of Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma in Non-cirrhotic Liver

OBJECTIVE

The aim of the work described here was to investigate the value of dynamic contrast enhanced ultrasound (DCE-US) and quantitative analysis in pre-operative differential diagnosis of intrahepatic cholangiocarcinoma (ICC) and hepatocellular carcinoma (HCC) in non-cirrhotic liver.

METHODS

In this retrospective study, patients with histopathologically proven ICC and HCC lesions in non-cirrhotic liver were included. All patients underwent contrast-enhanced ultrasound (CEUS) examinations with an Acuson Sequoia unit (Siemens Healthineers, Mountain View, CA, USA) unit or LOGIQ E20 (GE Healthcare, Milwaukee, WI, USA) within 1 wk before surgery. SonoVue (Bracco, Milan, Italy) was used as the contrast agent. B-mode ultrasound (BMUS) features and CEUS enhancement patterns were analyzed. DCE-US analysis was performed by VueBox software (Bracco). Two regions of interest (ROIs) were set in the center of the focal liver lesions and their surrounding liver parenchyma. Time-intensity curves (TICs) were generated, and quantitative perfusion parameters were obtained and compared between the ICC and HCC groups using the Student t-test or Mann-Whitney U-test.

RESULTS

From November 2020 to February 2022, patients with histopathologically confirmed ICC (n = 30) and HCC (n = 24) lesions in non-cirrhotic liver were included. During the arterial phase (AP) of CEUS, ICC lesions exhibited heterogeneous hyperenhancement (13/30, 43.3%), heterogeneous hypo-enhancement (2/30, 6.7 %) and rim-like hyperenhancement (15/30, 50.0%), whereas all HCC lesions exhibited heterogeneous hyperenhancement (24/24, 100.0%) (p < 0.05). Subsequently, most of the ICC lesions exhibited AP wash-out (83.3%, 25/30), whereas a few cases exhibited wash-out in the portal venous phase (PVP) (15.7%, 5/30). In contrast, HCC lesions exhibited AP wash-out (41.7%, 10/24), PVP wash-out (41.7%, 10/24) and a small part of late phase wash-out (16.7%, 4/24) (p < 0.05). Compared with those of HCC lesions, TICs of ICCs revealed earlier and lower enhancement during the AP, faster decline during the PVP and reduced area under the curve. The combined area under the receiver operating characteristic curve (AUROC) of all significant parameters was 0.946, with 86.7% sensitivity, 95.8% specificity and 90.7% accuracy in differential diagnosis between ICC and HCC lesions in non-cirrhotic liver, which improved the diagnostic efficacy of CEUS (58.3% sensitivity, 90.0% specificity and 75.9% accuracy).

CONCLUSION

ICC and HCC lesions in non-cirrhotic liver might exhibit some overlap of CEUS features in diagnosis. DCE-US with quantitative analysis would be helpful in pre-operative differential diagnosis.

Fractal nature of human gastrointestinal system: Exploring a new era

The morphological complexity of cells and tissues, whether normal or pathological, is characterized by two primary attributes: Irregularity and self-similarity across different scales. When an object exhibits self-similarity, its shape remains unchanged as the scales of measurement vary because any part of it resembles the whole. On the other hand, the size and geometric characteristics of an irregular object vary as the resolution increases, revealing more intricate details. Despite numerous attempts, a reliable and accurate method for quantifying the morphological features of gastrointestinal organs, tissues, cells, their dynamic changes, and pathological disorders has not yet been established. However, fractal geometry, which studies shapes and patterns that exhibit self-similarity, holds promise in providing a quantitative measure of the irregularly shaped morphologies and their underlying self-similar temporal behaviors. In this context, we explore the fractal nature of the gastrointestinal system and the potential of fractal geometry as a robust descriptor of its complex forms and functions. Additionally, we examine the practical applications of fractal geometry in clinical gastroenterology and hepatology practice.

Diagnostic Values of the Liver Imaging Reporting and Data System in the Detection and Characterization of Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis

This review was undertaken to assess the diagnostic value of the Liver Imaging Reporting and Data System (LI-RADS) in patients with a high risk of hepatocellular carcinoma (HCC). Google Scholar, PubMed, Web of Science, Embase, PROQUEST, and Cochrane Library, as the international databases, were searched with appropriate keywords. Using the binomial distribution formula, the variance of all studies was calculated, and using Stata version 16 (StataCorp LLC, College Station, TX, USA), the obtained data were analyzed. Using a random-effect meta-analysis approach, we determined the pooled sensitivity and specificity. Utilizing the funnel plot and Begg's and Egger's tests, we assessed publication bias. The results exhibited pooled sensitivity and pooled specificity of 0.80% and 0.89%, respectively, with a 95% confidence interval (CI) of 0.76-0.84 and 0.87-0.92, respectively. The 2018 version of LI-RADS showed the greatest sensitivity (0.83%; 95% CI 0.79-0.87; I ² = 80.6%; P < 0.001 for heterogeneity; T ² = 0.001). The maximum pooled specificity was detected in LI-RADS version 2014 (American College of Radiology, Reston, VA, USA; 93.0%; 95% CI 89.0-96.0; I ² = 81.7%; P < 0.001 for heterogeneity; T ² = 0.001). In this review, the results of estimated sensitivity and specificity were satisfactory. Therefore, this strategy can serve as an appropriate tool for identifying HCC.

Radiomics: A radiological evidence-based artificial intelligence technique to facilitate personalized precision medicine in hepatocellular carcinoma

The high postoperative recurrence rates in hepatocellular carcinoma (HCC) remain a major hurdle in its management. Appropriate staging and treatment selection may alleviate the extent of fatal recurrence. However, effective methods to preoperatively evaluate pathophysiologic and molecular characteristics of HCC are lacking. Imaging plays a central role in HCC diagnosis and stratification due to the non-invasive diagnostic criteria. Vast and crucial information is hidden within image data. Other than providing a morphological sketch for lesion diagnosis, imaging could provide new insights to describe the pathophysiological and genetic landscape of HCC. Radiomics aims to facilitate diagnosis and prognosis of HCC using artificial intelligence techniques to harness the immense information contained in medical images. Radiomics produces a set of archetypal and robust imaging features that are correlated to key pathological or molecular biomarkers to preoperatively risk-stratify HCC patients. Inferred with outcome data, comprehensive combination of radiomic, clinical and/or multi-omics data could also improve direct prediction of response to treatment and prognosis. The evolution of radiomics is changing our understanding of personalized precision medicine in HCC management. Herein, we review the key techniques and clinical applications in HCC radiomics and discuss current limitations and future opportunities to improve clinical decision making.