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      Different approaches for interpretation and reporting of immunohistochemistry analysis results in the bone tissue – a review

      research-article
      ,
      Diagnostic Pathology
      BioMed Central
      Immunohistochemistry, Score, Grading, Semiquantitative, Bone

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          Abstract

          Background

          Immunohistochemistry (IHC) is a well-established, widely accepted method in both clinical and experimental parts of medical science. It allows receiving valuable information about any process in any tissue, and especially in bone. Each year the amount of data, received by IHC, grows in geometric progression. But the lack of standardization, especially on the post-analytical stage (interpreting and reporting of results), makes the comparison of the results of different studies impossible.

          Methods

          Comprehensive PubMED literature search with a combination of search words “immunohistochemistry” and “scoring system” was performed and 773 articles describing IHC results were identified. After further manual analysis 120 articles were selected for detailed evaluation of used approaches.

          Results

          Six major approaches to the interpretation and presentation of IHC analysis results were identified, analyzed and described.

          Conclusions

          The overview of the existing approaches in evaluation and interpretation of IHC data, which are provided in the article, can be used in bone tissue research and for either better understanding of existing scoring systems or developing a new one. Standard multiparametric, semiquantitative IHC scoring systems should simplify and clarify the process of interpretation and reporting of received data.

          Virtual slides

          The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/13000_2014_221

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          Most cited references115

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          Effect size, confidence interval and statistical significance: a practical guide for biologists.

          Null hypothesis significance testing (NHST) is the dominant statistical approach in biology, although it has many, frequently unappreciated, problems. Most importantly, NHST does not provide us with two crucial pieces of information: (1) the magnitude of an effect of interest, and (2) the precision of the estimate of the magnitude of that effect. All biologists should be ultimately interested in biological importance, which may be assessed using the magnitude of an effect, but not its statistical significance. Therefore, we advocate presentation of measures of the magnitude of effects (i.e. effect size statistics) and their confidence intervals (CIs) in all biological journals. Combined use of an effect size and its CIs enables one to assess the relationships within data more effectively than the use of p values, regardless of statistical significance. In addition, routine presentation of effect sizes will encourage researchers to view their results in the context of previous research and facilitate the incorporation of results into future meta-analysis, which has been increasingly used as the standard method of quantitative review in biology. In this article, we extensively discuss two dimensionless (and thus standardised) classes of effect size statistics: d statistics (standardised mean difference) and r statistics (correlation coefficient), because these can be calculated from almost all study designs and also because their calculations are essential for meta-analysis. However, our focus on these standardised effect size statistics does not mean unstandardised effect size statistics (e.g. mean difference and regression coefficient) are less important. We provide potential solutions for four main technical problems researchers may encounter when calculating effect size and CIs: (1) when covariates exist, (2) when bias in estimating effect size is possible, (3) when data have non-normal error structure and/or variances, and (4) when data are non-independent. Although interpretations of effect sizes are often difficult, we provide some pointers to help researchers. This paper serves both as a beginner's instruction manual and a stimulus for changing statistical practice for the better in the biological sciences.
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            Bone histomorphometry: standardization of nomenclature, symbols, and units. Report of the ASBMR Histomorphometry Nomenclature Committee.

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              Estrogen receptor analyses. Correlation of biochemical and immunohistochemical methods using monoclonal antireceptor antibodies.

              Attempts at histochemical localization of estrogen receptor with anti-steroid antibody or some fluoresceinated estrogens have given unacceptable sensitivities and specificities when compared with biochemical methods or clinical response. In the present study a monoclonal antibody against estrogen receptor (H222 Sp gamma) was used on cryostat sections of freshly frozen breast tumors with a peroxidase-antiperoxidase immunoperoxidase technique. Biochemical receptor analyses were by dextran-coated charcoal analyses. Tumors from three separate cohorts of patients were studied as follows: population A, 62 primary breast cancers from 1983; population B, 72 primary lesions stored from 1976 to 1983; and population C, 23 patients with metastases, treated with hormonal therapy. Distinct staining was seen in the cell nucleus. A semiquantitative relationship was seen between histochemical score assessment of staining and biochemical assay in each cohort. The sensitivity and specificity using a threshold of 75 for the histochemical score and more than 20 femtomoles/mg of protein for dextran-coated charcoal analyses were as follows: population A, specificity, 89%, and sensitivity, 95%; population B, specificity, 94%, and sensitivity 88%; and for population C, the comparison was with objective clinical response yielding specificity, 89%, and sensitivity, 93%.
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                Author and article information

                Contributors
                fedchenko.nick@gmail.com
                janin.reifenrath@tiho-hannover.de
                Journal
                Diagn Pathol
                Diagn Pathol
                Diagnostic Pathology
                BioMed Central (London )
                1746-1596
                29 November 2014
                29 November 2014
                2014
                : 9
                : 1
                : 221
                Affiliations
                [ ]Small Animal Clinic, University of Veterinary Medicine, Foundation, Bünteweg 9, 30559 Hannover, Germany
                [ ]Department of Pathological Anatomy and Forensic Medicine, SE “Dnipropetrovsk Medical Academy of Health Ministry of Ukraine”, Dzerginskogo st. 9, 49044 Dnipropetrovsk, Ukraine
                Article
                221
                10.1186/s13000-014-0221-9
                4260254
                25432701
                0e71fb66-b1e1-4883-b908-d920758fb3e9
                © Fedchenko and Reifenrath; licensee BioMed Central Ltd. 2014

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 17 September 2014
                : 10 November 2014
                Categories
                Review
                Custom metadata
                © The Author(s) 2014

                Pathology
                immunohistochemistry,score,grading,semiquantitative,bone
                Pathology
                immunohistochemistry, score, grading, semiquantitative, bone

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