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

Maurice B Loughrey, Peter Bankhead, Helen G Coleman, Ryan S Hagan, Stephanie Craig, Amy M B McCorry, Ronan T Gray, Stephen McQuaid, Philip D Dunne, Peter W Hamilton, Jacqueline A James, Manuel Salto-Tellez

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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.

Original languageEnglish
JournalHistopathology
Early online date25 Mar 2018
DOIs
Publication statusEarly online date - 25 Mar 2018

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