Variant Hepatocellular Carcinoma Subtypes According to the 2019 WHO Classification: An Imaging-Focused Review

Loy, Liang Meng; Low, Hsien Min; Choi, Jin-Young; Rhee, Hyungjin; Wong, Chin Fong; Tan, Cher Heng

doi:10.2214/ajr.21.26982

Cited by 28 publications

(14 citation statements)

References 68 publications

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“…Recently, Brancato et al [ 14 ] and Yang et al [ 12 ] developed CEMRI-based radiomics models for predicting HCC differentiation, but neither of the studies investigated peritumoral radiomics, and the inclusion of a relatively limited number of patients ( n = 38–188) and the extraction of a restricted number of features (38–108 per sequence) were likely to decrease the reliability of the study findings, with AUC values ranging from 0.58 to 0.74. Our study concluded that the intratumoral model had a greater AUC for diagnosing pHCC than the clinical model; this can be attributed to the following factors: (1) radiomics, with its ability to provide a more thorough evaluation of tumor heterogeneity at the microscopic level by extracting numerous high-throughput features, is advantageous for accurately predicting pHCC which was identified based on a comprehensive assessment of heterogeneous pathologies [ 19 ]. (2) In contrast to clinical model that only relied on naked-eye features for qualitative evaluation, radiomics features served as quantitative imaging biomarkers for characterizing HCC biomedical behavior with better accuracy and more objective.…”

Section: Discussionmentioning

confidence: 99%

“…The tumor specimens were subjected to hematoxylin and eosin (HE) staining to determine the degree of HCC differentiation by a proficient pathologist with 12 years of experience who was blinded to the preoperative examinations. Based on the classification criteria [ 19 ], the tumors were categorized as well-, moderately, or poorly differentiated HCC (pHCC). When HCC tumors displayed various differentiation results, the predominant differentiation determined the final diagnosis.…”

Section: Methodsmentioning

confidence: 99%

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Multiparametric MRI-based intratumoral and peritumoral radiomics for predicting the pathological differentiation of hepatocellular carcinoma

Liu,

Wang,

Wang

et al. 2024

Insights Imaging

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Purpose To explore the predictive potential of intratumoral and multiregion peritumoral radiomics features extracted from multiparametric MRI for predicting pathological differentiation in hepatocellular carcinoma (HCC) patients. Methods A total of 265 patients with 277 HCCs (training cohort n = 193, validation cohort n = 84) who underwent preoperative MRI were retrospectively analyzed. The risk factors identified through stepwise regression analysis were utilized to construct a clinical model. Radiomics models based on MRI (arterial phase, portal venous phase, delayed phase) across various regions (entire tumor, Peri_5mm, Peri_10mm, Peri_20mm) were developed using the LASSO approach. The features obtained from the intratumoral region and the optimal peritumoral region were combined to design the IntraPeri fusion model. Model performance was assessed using the area under the curve (AUC). Results Larger size, non-smooth margins, and mosaic architecture were risk factors for poorly differentiated HCC (pHCC). The clinical model achieved AUCs of 0.77 and 0.73 in the training and validation cohorts, respectively, while the intratumoral model achieved corresponding AUC values of 0.92 and 0.82. The Peri_10mm model demonstrated superior performance to the Peri_5mm and Peri_20mm models, with AUC values of 0.87 vs. 0.84 vs. 0.73 in the training cohort and 0.80 vs. 0.77 vs. 0.68 in the validation cohort, respectively. The IntraPeri model exhibited remarkable AUC values of 0.95 and 0.86 in predicting pHCC in the training and validation cohorts, respectively. Conclusions Our study highlights the potential of a multiparametric MRI-based radiomic model that integrates intratumoral and peritumoral features as a tool for predicting HCC differentiation. Critical relevance statement Both clinical and multiparametric MRI-based radiomic models, particularly the intratumoral radiomic model, are non-invasive tools for predicting HCC differentiation. Importantly, the IntraPeri fusion model exhibited remarkable predictiveness for individualized HCC differentiation. Key points • Both the intratumoral radiomics model and clinical features were useful for predicting HCC differentiation. • The Peri_10mm radiomics model demonstrated better diagnostic ability than other peritumoral region-based models. • The IntraPeri radiomics fusion model outperformed the other models for predicting HCC differentiation. Graphical Abstract

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Multiparametric MRI-based intratumoral and peritumoral radiomics for predicting the pathological differentiation of hepatocellular carcinoma

Liu,

Wang,

Wang

et al. 2024

Insights Imaging

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“…For infections, 3D organoids provide a means to characterize pathogens and determine the response to infection. They demonstrated that lung organoids that were derived from cancer cells show greater susceptibility to infection by the influenza A virus with a reduced innate immune response compared to organoids from healthy tissue (66,67).…”

Section: Infectious Diseasementioning

confidence: 99%

Deciphering the underlying mechanism of liver diseases through utilization of multicellular hepatic spheroid models

Kim

Lee

Seo

2023

BMB Rep

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Hepatocellular carcinoma (HCC) is a very common form of cancer worldwide and is often fatal.Although the histopathology of HCC is characterized by metabolic pathophysiology, fibrosis, and cirrhosis, the focus of treatment has been on eliminating HCC. Recently, three-dimensional (3D) multicellular hepatic spheroid (MCHS) models have provided a) new therapeutic strategies for progressive fibrotic liver diseases, such as antifibrotic and anti-inflammatory drugs, b) molecular targets, and c) treatments for metabolic dysregulation. MCHS models provide a potent anti-cancer tool because they can mimic a) tumor complexity and heterogeneity, b) the 3D context of tumor cells, and c) the gradients of physiological parameters that are characteristic of tumors in vivo.However, the information provided by an MCTS model must always be considered in the context of tumors in vivo. This mini-review summarizes what is known about tumor HCC heterogeneity and complexity and the advances provided by MCHS models for innovations in drug development to combat liver diseases. clinical utility of drugs that proved effective in vitro is low. Also, assays of liver function, including cell polarization, albumin synthesis, bile secretion, and urea synthesis, are better when they are performed in three-dimensional (3D) cell cultures that more accurately reflect the normal context of liver cells (7). Similarly, 3D cells provide a better model system for cancer drug discovery.The interactions between the tumor and the tumor microenvironment (TME) play a critical role in cell differentiation, tumorigenesis, metastasis, and the efficacy of drug therapies. Therefore it was important to develop a 3D TME platform to discover new therapeutics for liver cancer drug discovery. These platforms are complex and expensive to produce; however, the recently developed multicellular tumor spheroid (MCTS) models provide a new platform for highthroughput screening (HTS) of drugs to treat a variety of diseases, including cancers and infections.The development of 3D multicellular hepatic spheroid (MCHS) models has had a major impact on drug discovery and the identification of novel targets for the treatment of HCC and fibrosis. MCHS models should greatly increase the number of potential new drugs to treat HCC and reduce the time to drug discovery.In this review, we describe the influence of components of the TME on tumorigenesis, fibrogenesis, and chemoresistance in HCC, as well as advanced strategies that exploit 3D multicellular spheroid models to identify novel cancer targets and therapeutics.

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“…According to the 2019 WHO classification, eight subtypes defined by molecular characteristics such as steatohepatitic, clear cell, macrotrabecular-massive, scirrhous, chromophobe, fibrolamellar, neutrophil-rich, and lymphocyte-rich HCCs were identified [ 18 ]. Due to their unique cellular features, these subtypes may not demonstrate APHE and washout appearance, creating challenges in imaging diagnosis of HCC [ 19 , 20 ]. By contrast, sarcomatoid HCC is now classified under the category of undifferentiated primary liver cancer.…”

Section: Dynamics Of Contrast Enhancementmentioning

confidence: 99%

Imaging Diagnosis of Various Hepatocellular Carcinoma Subtypes and Its Hypervascular Mimics: Differential Diagnosis Based on Conventional Interpretation and Artificial Intelligence

Minami

Nishida

Kudo³

2022

Liver Cancer

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Background. Hepatocellular carcinoma (HCC) is unique among malignancies, and its characteristics on contrast imaging modalities allow for a highly accurate diagnosis. The radiological differentiation of focal liver lesions is playing an increasingly important role, and the Liver Imaging Reporting and Data System (LI-RADS) adopts a combination of major features including arterial phase hyperenhancement (APHE) and washout pattern. Summary. Specific HCCs such as well or poorly differentiated type, subtypes including fibrolamellar or sarcomatoid and combined hepatocellular-cholangiocarcinoma does not often demonstrate APHE and washout appearance. Meanwhile, hypervascular liver metastases and hypervascular intrahepatic cholangiocarcinoma (ICC) can demonstrate APHE and washout. There are still other hypervascular malignant liver tumors (i.e., angiosarcoma, epithelioid hemangioendothelioma) and hypervascular benign liver lesions (i.e., adenoma, focal nodular hyperplasia, angiomyolipoma, flash filling hemangioma, reactive lymphoid hyperplasia, inflammatory lesion, arterioportal shunt), which need to be distinguished from HCC. When a patient has chronic liver disease, differential diagnosis of hypervascular liver lesions can be even more complicated. Meanwhile, artificial intelligence (AI) in medicine has been widely explored, and recent advancement in the field of deep learning has provided promising performance for the analysis of medical images. Especially, radiological imaging data contain diagnostic, prognostic, and predictive information which AI can extract. The AI researches have demonstrated high accuracy (over 90% accuracy) for classifying lesions with typical imaging features from some hepatic lesions. AI system has a potential to implement in clinical routine as decision support tools. However, for the differential diagnosis of many types of hypervascular liver lesions, further large-scale clinical validation still is required. Key Messages. Clinicians should be aware of the histopathological features, imaging characteristics and differential diagnoses of hypervascular liver lesions to a precise diagnosis and the more valuable treatment plan. We need to be familiar with such atypical cases to prevent a diagnostic delay, but AI based tools also need to lean a large number of typical and atypical cases.

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Variant Hepatocellular Carcinoma Subtypes According to the 2019 WHO Classification: An Imaging-Focused Review

Cited by 28 publications

References 68 publications

Multiparametric MRI-based intratumoral and peritumoral radiomics for predicting the pathological differentiation of hepatocellular carcinoma

Multiparametric MRI-based intratumoral and peritumoral radiomics for predicting the pathological differentiation of hepatocellular carcinoma

Deciphering the underlying mechanism of liver diseases through utilization of multicellular hepatic spheroid models

Imaging Diagnosis of Various Hepatocellular Carcinoma Subtypes and Its Hypervascular Mimics: Differential Diagnosis Based on Conventional Interpretation and Artificial Intelligence

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