Literature DB >> 29575153

Validation of the systematic scoring of immunohistochemically stained tumour tissue microarrays using QuPath digital image analysis.

Maurice B Loughrey1,2, Peter Bankhead3, Helen G Coleman3,4, Ryan S Hagan3, Stephanie Craig1, Amy M B McCorry3, Ronan T Gray4, Stephen McQuaid1,2, Philip D Dunne3, Peter W Hamilton3,5, Jacqueline A James1,2, Manuel Salto-Tellez1,2.   

Abstract

AIMS: Output from biomarker studies involving immunohistochemistry applied to tissue microarrays (TMA) is limited by the lack of an efficient and reproducible scoring methodology. In this study, we examine the functionality and reproducibility of biomarker scoring using the new, open-source, digital image analysis software, QuPath. METHODS AND
RESULTS: Three different reviewers, with varying experience of digital pathology and image analysis, applied an agreed QuPath scoring methodology to CD3 and p53 immunohistochemically stained TMAs from a colon cancer cohort (n = 661). Manual assessment was conducted by one reviewer for CD3. Survival analyses were conducted and intra- and interobserver reproducibility assessed. Median raw scores differed significantly between reviewers, but this had little impact on subsequent analyses. Lower CD3 scores were detected in cases who died from colorectal cancer compared to control cases, and this finding was significant for all three reviewers (P-value range = 0.002-0.02). Higher median p53 scores were generated among cases who died from colorectal cancer compared with controls (P-value range = 0.04-0.12). The ability to dichomotise cases into high versus low expression of CD3 and p53 showed excellent agreement between all three reviewers (kappa score range = 0.82-0.93). All three reviewers produced dichotomised expression scores that resulted in very similar hazard ratios for colorectal cancer-specific survival for each biomarker. Results from manual and QuPath methods of CD3 scoring were comparable, but QuPath scoring revealed stronger prognostic stratification.
CONCLUSIONS: Scoring of immunohistochemically stained tumour TMAs using QuPath is functional and reproducible, even among users of limited experience of digital pathology images, and more accurate than manual scoring.
© 2018 John Wiley & Sons Ltd.

Entities:  

Keywords:  QuPath; colorectal cancer; digital pathology; image analysis; tissue microarray

Mesh:

Substances:

Year:  2018        PMID: 29575153     DOI: 10.1111/his.13516

Source DB:  PubMed          Journal:  Histopathology        ISSN: 0309-0167            Impact factor:   5.087


  23 in total

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Journal:  Cancer Immunol Immunother       Date:  2020-07-04       Impact factor: 6.968

2.  Prognostic Significance of Immune Cell Populations Identified by Machine Learning in Colorectal Cancer Using Routine Hematoxylin and Eosin-Stained Sections.

Authors:  Juha P Väyrynen; Mai Chan Lau; Koichiro Haruki; Sara A Väyrynen; Jeffrey A Meyerhardt; Marios Giannakis; Shuji Ogino; Jonathan A Nowak; Andressa Dias Costa; Jennifer Borowsky; Melissa Zhao; Kenji Fujiyoshi; Kota Arima; Tyler S Twombly; Junko Kishikawa; Simeng Gu; Saina Aminmozaffari; Shanshan Shi; Yoshifumi Baba; Naohiko Akimoto; Tomotaka Ugai; Annacarolina Da Silva; Mingyang Song; Kana Wu; Andrew T Chan; Reiko Nishihara; Charles S Fuchs
Journal:  Clin Cancer Res       Date:  2020-05-21       Impact factor: 12.531

3.  Simultaneous and Spatially-Resolved Analysis of T-Lymphocytes, Macrophages and PD-L1 Immune Checkpoint in Rare Cancers.

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4.  Optimal settings and clinical validation for automated Ki67 calculation in neuroendocrine tumors with open source informatics (QuPath).

Authors:  Rima Pai; Susan Karki; Rakhee Agarwal; Steven Sieber; Samuel Barasch
Journal:  J Pathol Inform       Date:  2022-09-21

5.  Comparison of manual and automated digital image analysis systems for quantification of cellular protein expression.

Authors:  T Jagomast; C Idel; L Klapper; P Kuppler; L Proppe; S Beume; M Falougy; D Steller; S G Hakim; A Offermann; M C Roesch; K L Bruchhage; S Perner; J Ribbat-Idel
Journal:  Histol Histopathol       Date:  2022-02-11       Impact factor: 2.130

6.  Preparation, construction and high-throughput automated analysis of human brain tissue microarrays for neurodegenerative disease drug development.

Authors:  Malvindar K Singh-Bains; Nasim F Mehrabi; Adelie Y S Tan; Richard L M Faull; Mike Dragunow
Journal:  Nat Protoc       Date:  2021-03-19       Impact factor: 13.491

7.  Research Strategies for Low-Survival Cancers.

Authors:  Caroline Conway; Denis M Collins; Amanda McCann; Kellie Dean
Journal:  Cancers (Basel)       Date:  2021-01-30       Impact factor: 6.639

8.  The adaptive immune and immune checkpoint landscape of neoadjuvant treated esophageal adenocarcinoma using digital pathology quantitation.

Authors:  Matthew P Humphries; Stephanie G Craig; Rafal Kacprzyk; Natalie C Fisher; Victoria Bingham; Stephen McQuaid; Graeme I Murray; Damian McManus; Richard C Turkington; Jacqueline James; Manuel Salto-Tellez
Journal:  BMC Cancer       Date:  2020-06-01       Impact factor: 4.430

9.  An integrative histopathologic clustering model based on immuno-matrix elements to predict the risk of death in malignant mesothelioma.

Authors:  Marcelo Luiz Balancin; Walcy Rosolia Teodoro; Cecilia Farhat; Tomas Jurandir de Miranda; Aline Kawassaki Assato; Neila Aparecida de Souza Silva; Ana Paula Velosa; Roberto Falzoni; Alexandre Muxfeldt Ab'Saber; Anja C Roden; Vera Luiza Capelozzi
Journal:  Cancer Med       Date:  2020-05-11       Impact factor: 4.452

10.  Improving the Diagnostic Accuracy of the PD-L1 Test with Image Analysis and Multiplex Hybridization.

Authors:  Matthew P Humphries; Victoria Bingham; Fatima Abdullahi Sidi; Stephanie G Craig; Stephen McQuaid; Jacqueline James; Manuel Salto-Tellez
Journal:  Cancers (Basel)       Date:  2020-04-29       Impact factor: 6.575

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