Overview of established and emerging immunohistochemical biomarkers and their role in correlative studies in MRI Review
| Authors: | Alvi, E.; Gupta, R.; Borok, R. Z.; Escobar-Hoyos, L.; Shroyer, K. R. |
| Review Title: | Overview of established and emerging immunohistochemical biomarkers and their role in correlative studies in MRI |
| Abstract: | Clinical practice in radiology and pathology requires professional expertise and many years of training to visually evaluate and interpret abnormal phenotypic features in medical images and tissue sections to generate diagnoses that guide patient management and treatment. Recent advances in digital image analysis methods and machine learning have led to significant interest in extracting additional information from medical and digital whole-slide images in radiology and pathology, respectively. This has led to significant interest and research in radiomics and pathomics to correlate phenotypic features of disease with image analytics in order to identify image-based biomarkers. The expanding role of big data in radiology and pathology parallels the development and role of immunohistochemistry (IHC) in the daily practice of pathology. IHC methods were initially developed to provide additional information to help classify tumors and then transformed into an indispensable tool to guide treatment in many types of cancer. IHC markers are used in daily practice to identify specific types of cells and highlight their distributions in tissues in order to distinguish benign from neoplastic cells, determine tumor origin, subclassify neoplasms, and support and confirm diagnoses. In this regard, radiomics, pathomics, and IHC methods are very similar since they enable the extraction of image-based features to characterize various properties of diseases. Due to the dramatic advancements in recent radiomics research, we provide a brief overview of the role of established and emerging IHC biomarkers in various tumor types that have been correlated with radiologic biomarkers to improve diagnostic accuracy, predict prognosis, guide patient management, and select treatment strategies. Level of Evidence: 5. Technical Efficacy: Stage 3. J. Magn. Reson. Imaging 2020;51:341–354. © 2019 International Society for Magnetic Resonance in Medicine |
| Keywords: | immunohistochemistry; vasculotropin; cancer survival; protein expression; histopathology; review; drug efficacy; nuclear magnetic resonance imaging; diagnostic accuracy; biological marker; prostate specific antigen; unindexed drug; melanoma; breast cancer; image analysis; epidermal growth factor receptor 2; gelatinase b; tumor differentiation; pathology; bladder cancer; monoclonal antibody; prostate cancer; prostate specific membrane antigen; lung adenocarcinoma; radiology; biomarker; glioblastoma; nuclear magnetic resonance spectroscopy; pancreas adenocarcinoma; pancreatectomy; ependymoma; oligodendroglioma; protein p63; cancer classification; cytokeratin 5; cytokeratin 6; estrogen receptor; astrocytoma; dynamic contrast-enhanced magnetic resonance imaging; diffusion weighted imaging; mri; predictive value; cd83 antigen; uterine cervix carcinoma; transcription factor cdx2; gelatinase a; classical hodgkin lymphoma; hairy cell leukemia; programmed death 1 receptor; non small cell lung cancer; mucin 2; b raf kinase inhibitor; cell cycle protein 20; feature extraction; ihc; crizotinib; vemurafenib; acid phosphatase prostate isoenzyme; cancer prognosis; dabrafenib; trametinib; cytokeratin 17; nivolumab; susceptibility weighted imaging; multiparametric magnetic resonance imaging; human; priority journal; pembrolizumab; squamous cell lung carcinoma; durvalumab; atezolizumab; homeobox protein nkx 2.1 |
| Journal Title: | Journal of Magnetic Resonance Imaging |
| Volume: | 51 |
| Issue: | 2 |
| ISSN: | 1053-1807 |
| Publisher: | Wiley Blackwell |
| Date Published: | 2020-02-01 |
| Start Page: | 341 |
| End Page: | 354 |
| Language: | English |
| DOI: | 10.1002/jmri.26763 |
| PUBMED: | 31041822 |
| PROVIDER: | scopus |
| DOI/URL: | |
| Notes: | Review -- Export Date: 3 February 2020 -- Source: Scopus |
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