Literature DB >> 20350211

On the concept of objectivity in digital image analysis in pathology.

Paul J Tadrous1.   

Abstract

AIMS: The term 'objective' connotes a method that is based on facts and not influenced by personal opinions, perception or emotion. One often reads in the biomedical literature claims of objectivity for methods that use digital image analysis applied to histology. Since objective assessment of histology would represent a huge leap forward in scientific measurement and clinical diagnosis, such claims should be substantiated by strong evidence. This paper takes a selective look at the literature on image analysis to assess the definition of objectivity in image analysis and asks whether such a claim is ever justified.
METHODS: First, a brief background on the basic science of image analysis in histology details some of the controversies and opinions in the field. Then, a literature review of a subset of papers pertaining to image analysis in histology (with claims of objectivity) is conducted to determine what evidence exists for objectivity in these methods.
RESULTS: It was found that image analysis may have many benefits (speed, indefatigability, standardisation, etc.). However, algorithms are devised and implemented by human beings who make subjective decisions at each stage of the algorithm design and implementation process. Thus, image analysis methods can be seen as deterministic processes which 'objectively' implement the subjective decisions of the programmer. This indicates that 'inter-observer' variation in image analysis is equivalent to 'inter-algorithm' variation (which is rarely studied) and that a single computer algorithm's repeatability is of lesser importance than the repeatability of the image analysis method as a whole (including the block, slide and field selection and the method of tissue processing).
CONCLUSION: Repeatability and automaticity must not be confused with objectivity, but a lack of objectivity does not imply a lack of utility. Unless specific evidence of objectivity is provided, editors should insist that claims of objectivity in image analysis papers be either removed or justified prior to publication.

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Mesh:

Year:  2010        PMID: 20350211     DOI: 10.3109/00313021003641758

Source DB:  PubMed          Journal:  Pathology        ISSN: 0031-3025            Impact factor:   5.306


  16 in total

1.  Immunohistochemistry profiles of breast ductal carcinoma: factor analysis of digital image analysis data.

Authors:  Arvydas Laurinavicius; Aida Laurinaviciene; Valerijus Ostapenko; Darius Dasevicius; Sonata Jarmalaite; Juozas Lazutka
Journal:  Diagn Pathol       Date:  2012-03-16       Impact factor: 2.644

2.  Cryopreservation of amniotic membrane with and without glycerol additive.

Authors:  Malina Wagner; Peter Walter; Sabine Salla; Sandra Johnen; Niklas Plange; Stephan Rütten; Tamme W Goecke; Matthias Fuest
Journal:  Graefes Arch Clin Exp Ophthalmol       Date:  2018-04-05       Impact factor: 3.117

3.  A methodology for comprehensive breast cancer Ki67 labeling index with intra-tumor heterogeneity appraisal based on hexagonal tiling of digital image analysis data.

Authors:  Benoit Plancoulaine; Aida Laurinaviciene; Paulette Herlin; Justinas Besusparis; Raimundas Meskauskas; Indra Baltrusaityte; Yasir Iqbal; Arvydas Laurinavicius
Journal:  Virchows Arch       Date:  2015-10-19       Impact factor: 4.064

4.  Deep Learning-Based Histopathologic Assessment of Kidney Tissue.

Authors:  Meyke Hermsen; Thomas de Bel; Marjolijn den Boer; Eric J Steenbergen; Jesper Kers; Sandrine Florquin; Joris J T H Roelofs; Mark D Stegall; Mariam P Alexander; Byron H Smith; Bart Smeets; Luuk B Hilbrands; Jeroen A W M van der Laak
Journal:  J Am Soc Nephrol       Date:  2019-09-05       Impact factor: 10.121

5.  Quantifying histological features of cancer biospecimens for biobanking quality assurance using automated morphometric pattern recognition image analysis algorithms.

Authors:  Joshua D Webster; Eleanor R Simpson; Aleksandra M Michalowski; Shelley B Hoover; R Mark Simpson
Journal:  J Biomol Tech       Date:  2011-09

6.  What makes AI 'intelligent' and 'caring'? Exploring affect and relationality across three sites of intelligence and care.

Authors:  Giulia De Togni; Sonja Erikainen; Sarah Chan; Sarah Cunningham-Burley
Journal:  Soc Sci Med       Date:  2021-03-23       Impact factor: 4.634

7.  Membrane connectivity estimated by digital image analysis of HER2 immunohistochemistry is concordant with visual scoring and fluorescence in situ hybridization results: algorithm evaluation on breast cancer tissue microarrays.

Authors:  Aida Laurinaviciene; Darius Dasevicius; Valerijus Ostapenko; Sonata Jarmalaite; Juozas Lazutka; Arvydas Laurinavicius
Journal:  Diagn Pathol       Date:  2011-09-23       Impact factor: 2.644

8.  Digital immunohistochemistry wizard: image analysis-assisted stereology tool to produce reference data set for calibration and quality control.

Authors:  Benoît Plancoulaine; Aida Laurinaviciene; Raimundas Meskauskas; Indra Baltrusaityte; Justinas Besusparis; Paulette Herlin; Arvydas Laurinavicius
Journal:  Diagn Pathol       Date:  2014-12-19       Impact factor: 2.644

9.  Ki67/SATB1 ratio is an independent prognostic factor of overall survival in patients with early hormone receptor-positive invasive ductal breast carcinoma.

Authors:  Arvydas Laurinavicius; Andrew R Green; Aida Laurinaviciene; Giedre Smailyte; Valerijus Ostapenko; Raimundas Meskauskas; Ian O Ellis
Journal:  Oncotarget       Date:  2015-12-01

10.  A methodology to ensure and improve accuracy of Ki67 labelling index estimation by automated digital image analysis in breast cancer tissue.

Authors:  Arvydas Laurinavicius; Benoit Plancoulaine; Aida Laurinaviciene; Paulette Herlin; Raimundas Meskauskas; Indra Baltrusaityte; Justinas Besusparis; Darius Dasevicius; Nicolas Elie; Yasir Iqbal; Catherine Bor
Journal:  Breast Cancer Res       Date:  2014       Impact factor: 6.466

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