The Gold Standard Paradox in Digital Image Analysis: Manual Versus Automated Scoring as Ground Truth


Novel therapeutics often target complex cellular mechanisms. Increasingly, quantitative methods like digital tissue image analysis (tIA) are required to evaluate correspondingly complex biomarkers to elucidate subtle phenotypes that can inform treatment decisions with these targeted therapies. These tIA systems need a gold standard, or reference method, to establish analytical validity. Conventional, subjective histopathologic scores assigned by an experienced pathologist are the gold standard in anatomic pathology and are an attractive reference method. The pathologist’s score can establish the ground truth to assess a tIA solution’s analytical performance. The paradox of this validation strategy, however, is that tIA is often used to assist pathologists to score complex biomarkers because it is more objective and reproducible than manual evaluation alone by overcoming known biases in a human’s visual evaluation of tissue, and because it can generate endpoints that cannot be generated by a human observer.

To discuss common visual and cognitive traps known in traditional pathology-based scoring paradigms that may impact characterization of tIA-assisted scoring accuracy, sensitivity, and specificity.

This manuscript reviews the current literature from the past decades available for traditional subjective pathology scoring paradigms and known cognitive and visual traps relevant to these scoring paradigms.

Awareness of the gold standard paradox is necessary when using traditional pathologist scores to analytically validate a tIA tool because image analysis is used specifically to overcome known sources of bias in visual assessment of tissue sections.

Published in Archives of Pathology & Laboratory Medicine
2017 May 30

Authors: Famke Aeffner, Kristin Wilson, Nathan T. Martin, Joshua C. Black, Cris L. Luengo Hendriks, Brad Bolon, Daniel G. Rudmann, Roberto Gianani, Sally R. Koegler, Joseph Krueger, and G. Dave Young

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