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数字图像分析中的金标准悖论:作为基本事实的人工评分与自动评分

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

作者信息

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

出版信息

Arch Pathol Lab Med. 2017 Sep;141(9):1267-1275. doi: 10.5858/arpa.2016-0386-RA. Epub 2017 May 30.

Abstract

CONTEXT

  • 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.

OBJECTIVE

  • 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.

DATA SOURCES

  • 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.

CONCLUSIONS

  • 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.
摘要

背景

新型疗法通常针对复杂的细胞机制。越来越需要诸如数字组织图像分析(tIA)之类的定量方法来评估相应复杂的生物标志物,以阐明可以为这些靶向疗法的治疗决策提供依据的细微表型。这些tIA系统需要一个金标准或参考方法来确立分析效度。由经验丰富的病理学家分配的传统主观组织病理学评分是解剖病理学中的金标准,也是一种有吸引力的参考方法。病理学家的评分可以确立基本事实,以评估tIA解决方案的分析性能。然而,这种验证策略的矛盾之处在于,tIA通常用于协助病理学家对复杂的生物标志物进行评分,因为它比单纯的人工评估更客观、更可重复,它克服了人类对组织视觉评估中已知的偏差,并且它可以生成人类观察者无法生成的终点。

目的

讨论传统的基于病理学的评分范式中已知的常见视觉和认知陷阱,这些陷阱可能会影响tIA辅助评分准确性;敏感性和特异性的特征描述。

数据来源

本手稿回顾了过去几十年中有关传统主观病理学评分范式以及与这些评分范式相关的已知认知和视觉陷阱的现有文献。

结论

在使用传统病理学家评分对tIA工具进行分析验证时,必须意识到金标准悖论,因为图像分析专门用于克服组织切片视觉评估中已知的偏差来源。

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