Literature DB >> 21971321

Digital image analysis in pathology: benefits and obligation.

Arvydas Laurinavicius1, Aida Laurinaviciene, Darius Dasevicius, Nicolas Elie, Benoît Plancoulaine, Catherine Bor, Paulette Herlin.   

Abstract

Pathology has recently entered the era of personalized medicine. This brings new expectations for the accuracy and precision of tissue-based diagnosis, in particular, when quantification of histologic features and biomarker expression is required. While for many years traditional pathologic diagnosis has been regarded as ground truth, this concept is no longer sufficient in contemporary tissue-based biomarker research and clinical use. Another major change in pathology is brought by the advancement of virtual microscopy technology enabling digitization of microscopy slides and presenting new opportunities for digital image analysis. Computerized vision provides an immediate benefit of increased capacity (automation) and precision (reproducibility), but not necessarily the accuracy of the analysis. To achieve the benefit of accuracy, pathologists will have to assume an obligation of validation and quality assurance of the image analysis algorithms. Reference values are needed to measure and control the accuracy. Although pathologists' consensus values are commonly used to validate these tools, we argue that the ground truth can be best achieved by stereology methods, estimating the same variable as an algorithm is intended to do. Proper adoption of the new technology will require a new quantitative mentality in pathology. In order to see a complete and sharp picture of a disease, pathologists will need to learn to use both their analogue and digital eyes.

Mesh:

Year:  2012        PMID: 21971321      PMCID: PMC4605791          DOI: 10.3233/ACP-2011-0033

Source DB:  PubMed          Journal:  Anal Cell Pathol (Amst)        ISSN: 2210-7177            Impact factor:   2.916


  24 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.  Image analysis of immunohistochemistry is superior to visual scoring as shown for patient outcome of esophageal adenocarcinoma.

Authors:  Annette Feuchtinger; Tabitha Stiehler; Uta Jütting; Goran Marjanovic; Birgit Luber; Rupert Langer; Axel Walch
Journal:  Histochem Cell Biol       Date:  2014-08-26       Impact factor: 4.304

3.  A New Classification of Benign, Premalignant, and Malignant Endometrial Tissues Using Machine Learning Applied to 1413 Candidate Variables.

Authors:  Michael J Downing; David J Papke; Svitlana Tyekucheva; George L Mutter
Journal:  Int J Gynecol Pathol       Date:  2020-07       Impact factor: 2.762

4.  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

5.  Novel nuclear localization of fatty acid synthase correlates with prostate cancer aggressiveness.

Authors:  Allison A Madigan; Kevin J Rycyna; Anil V Parwani; Yeipyeng J Datiri; Ahmed M Basudan; Kathryn M Sobek; Jessica L Cummings; Per H Basse; Dean J Bacich; Denise S O'Keefe
Journal:  Am J Pathol       Date:  2014-06-05       Impact factor: 4.307

6.  Expression of cell cycle markers is predictive of the response to primary systemic therapy of locally advanced breast cancer.

Authors:  Tímea Tőkés; Anna-Mária Tőkés; Gyöngyvér Szentmártoni; Gergő Kiszner; Lilla Madaras; Janina Kulka; Tibor Krenács; Magdolna Dank
Journal:  Virchows Arch       Date:  2016-03-30       Impact factor: 4.064

7.  Image Analysis-based Assessment of PD-L1 and Tumor-Associated Immune Cells Density Supports Distinct Intratumoral Microenvironment Groups in Non-small Cell Lung Carcinoma Patients.

Authors:  Edwin R Parra; Carmen Behrens; Jaime Rodriguez-Canales; Heather Lin; Barbara Mino; Jorge Blando; Jianjun Zhang; Don L Gibbons; John V Heymach; Boris Sepesi; Stephen G Swisher; Annikka Weissferdt; Neda Kalhor; Julie Izzo; Humam Kadara; Cesar Moran; Jack J Lee; Ignacio I Wistuba
Journal:  Clin Cancer Res       Date:  2016-06-01       Impact factor: 12.531

8.  An international Ki67 reproducibility study.

Authors:  Mei-Yin C Polley; Samuel C Y Leung; Lisa M McShane; Dongxia Gao; Judith C Hugh; Mauro G Mastropasqua; Giuseppe Viale; Lila A Zabaglo; Frédérique Penault-Llorca; John M S Bartlett; Allen M Gown; W Fraser Symmans; Tammy Piper; Erika Mehl; Rebecca A Enos; Daniel F Hayes; Mitch Dowsett; Torsten O Nielsen
Journal:  J Natl Cancer Inst       Date:  2013-11-07       Impact factor: 13.506

9.  An automated computational image analysis pipeline for histological grading of cardiac allograft rejection.

Authors:  Eliot G Peyster; Sara Arabyarmohammadi; Andrew Janowczyk; Sepideh Azarianpour-Esfahani; Miroslav Sekulic; Clarissa Cassol; Luke Blower; Anil Parwani; Priti Lal; Michael D Feldman; Kenneth B Margulies; Anant Madabhushi
Journal:  Eur Heart J       Date:  2021-06-21       Impact factor: 35.855

10.  Perspectives on Complexity, Chaos and Thermodynamics in Environmental Pathology.

Authors:  Maurizio Manera
Journal:  Int J Environ Res Public Health       Date:  2021-05-27       Impact factor: 3.390

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