Immunohistochemical pitfalls in the diagnosis of hepatocellular adenomas and focal nodular hyperplasia: accurate understanding of diverse staining patterns is essential for diagnosis and risk assessment

Kakar, Sanjay; Torbenson, Michael; Jain, Dhanpat; Wu, Tsung Teh; Yeh, Matthew M.; Ferrell, Linda D.

doi:10.1038/modpathol.2014.121

Cited by 7 publications

(3 citation statements)

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“…However, our results suggest limited reliability of GS and β-catenin immunohistochemistry in predicting β-catenin mutations. The utility of both markers has been challenged by other researchers [ 10 , 21 , 22 , 29 , 30 ]. Lagana et al showed strong GS positivity in over 50 % of HCA and Joseph et al described patchy to diffuse GS staining in 23 % of IHCA, but no correlation with molecular studies were available in these studies [ 22 ].…”

Section: Discussionmentioning

confidence: 99%

“…Upregulation of a downstream CTNNB1 target gene, glutamine synthase (GS), is seen in b-HCA, as well as nuclear β-catenin staining. It has been postulated that the immunophenotypic subtypes closely parallel specific histologic features and molecular alterations, but limitations have been observed by numerous studies and detailed studies correlating morphology, immunohistochemical profile and mutation analysis are lacking [ 3 , 12 , 20 , 21 ]. The aim of our study was to apply the HCA classification system based on histologic features and immunohistochemical profiles and correlate the findings with molecular analysis.…”

Section: Introductionmentioning

confidence: 99%

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Hepatocellular adenoma classification: a comparative evaluation of immunohistochemistry and targeted mutational analysis

et al. 2016

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BackgroundFour subtypes of hepatocellular adenomas (HCA) are recognized: hepatocyte-nuclear-factor-1α mutated (H-HCA), β-catenin-mutated type with upregulation of glutamine synthetase (b-HCA), inflammatory type (IHCA) with serum-amyloid-A overexpression, and unclassified type. Subtyping may be useful since b-HCA appear to have higher risk of malignant transformation. We sought to apply subtype analysis and assess histological atypia, correlating these with next-generation sequencing analysis.MethodsTwenty-six HCA were stained with serum amyloid A (SAA), liver fatty acid-binding protein (LFABP), glutamine synthetase (GS), and β-catenin IHC, followed by analysis with a targeted multiplex sequencing panel.ResultsBy IHC, 4 HCA (15.4 %) were classified as b-HCA, 11 (42.3 %) as IHCA, 9 (34.6 %) as H-HCA, and two (7.7 %) unclassifiable. Eight HCA (30.8 %) showed atypia (3 b-HCA, 4 IHCA and 1 H-HCA). Targeted sequencing confirmed HNF1A mutations in all H-HCA, confirming reliability of LFABP IHC in identifying these lesions. CTNNB1 mutations were detected in 1 of 4 (25 %) of GS/β-catenin-positive cases, suggesting that positive GS stain does not always correlate with CTNNB1 mutations.ConclusionsImmunohistochemistry does not consistently identify b-HCA. Mutational analysis improves the diagnostic accuracy of β-catenin-mutated HCA and is an important tool to assess risk of malignancy in HCA.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hepatocellular adenoma classification: a comparative evaluation of immunohistochemistry and targeted mutational analysis

et al. 2016

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“…The above-mentioned artefact is to be taken in the sense that it refers to an altered state of the tissue and its structures (relatively to its living state) i.e., the structures it exhibits are not naturally present in the living state of the tissue but are rather the product of a series of preparation steps (Hardy, 1899); throughout the rest of the paper, the definition of artefact is narrowed down to (image) degradations. Knowledge of the steps relative to tissue preparation and diverse staining patterns is not only essential for diagnosis and risk assessment-and this is still an active area of research (Kakar et al, 2015)-but also for all subsequent image analysis steps. In the following, we briefly describe the different stages of FFPE sections preparation.…”

Section: Paraffin Sectionsmentioning

confidence: 99%

A Survey of Methods for 3D Histology Reconstruction

Pichat

Iglesias

Yousry³

et al. 2018

Medical Image Analysis

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Histology permits the observation of otherwise invisible structures of the internal topography of a specimen. Although it enables the investigation of tissues at a cellular level, it is invasive and breaks topology due to cutting. Three-dimensional (3D) reconstruction was thus introduced to overcome the limitations of single-section studies in a dimensional scope. 3D reconstruction finds its roots in embryology, where it enabled the visualisation of spatial relationships of developing systems and organs, and extended to biomedicine, where the observation of individual, stained sections provided only partial understanding of normal and abnormal tissues. However, despite bringing visual awareness, recovering realistic reconstructions is elusive without prior knowledge about the tissue shape. 3D medical imaging made such structural ground truths available. In addition, combining non-invasive imaging with histology unveiled invaluable opportunities to relate macroscopic information to the underlying microscopic properties of tissues through the establishment of spatial correspondences; image registration is one technique that permits the automation of such a process and we describe reconstruction methods that rely on it. It is thereby possible to recover the original topology of histology and lost relationships, gain insight into what affects the signals used to construct medical images (and characterise them), or build high resolution anatomical atlases. This paper reviews almost three decades of methods for 3D histology reconstruction from serial sections, used in the study of many different types of tissue. We first summarise the process that produces digitised sections from a tissue specimen in order to understand the peculiarity of the data, the associated artefacts and some possible ways to minimise them. We then describe methods for 3D histology reconstruction with and without the help of 3D medical imaging, along with methods of validation and some applications. We finally attempt to identify the trends and challenges that the field is facing, many of which are derived from the cross-disciplinary nature of the problem as it involves the collaboration between physicists, histolopathologists, computer scientists and physicians.

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