Evidence map›Paper›PMID 31231130›Full record

ArticleModern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc2019

Reproducibility of visual estimation of lung adenocarcinoma subtype proportions.

Joanne Wright, Andrew Churg, Masanori Kitaichi, Hui-Min Yang, Dallas Hyde, Eunhee Yi

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Article in Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Joanne WrightDepartments of Pathology, St Paul's Hospital, Vancouver General Hospital, University of British Columbia, Vancouver, BC, Canada. jlwright@mail.ubc.ca.
Andrew ChurgDepartments of Pathology, St Paul's Hospital, Vancouver General Hospital, University of British Columbia, Vancouver, BC, Canada.
Masanori KitaichiDepartment of Pathology, National Hospital Organization Minami Wakayama Medical Center, Tanabe City, Wakayama Prefecture, Japan.
Hui-Min YangDepartments of Pathology, St Paul's Hospital, Vancouver General Hospital, University of British Columbia, Vancouver, BC, Canada.
Dallas HydeCalifornia National Primate Research Center, Davis, CA, USA.
Eunhee YiDepartment of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In 2011, a new classification of lung adenocarcinoma was published. Since then there have been multiple studies regarding observer variability in predominant subtype determination, with levels of agreement generally in the weak to moderate range. In the 2011 and subsequent WHO 2015 classification, a recommendation was also made to visually assess and record the percentage of each subtype in 5% increments. The present study was initiated to determine the reproducibility of such gestalt assessments and to compare these data to a formal morphometric assessment. Five experienced pathologists reviewed multiple single images of 25 adenocarcinomas, taken at 2× and 10×, and estimated the percentage of lepidic, acinar, papillary, micropapillary, and solid components in 5% increments. After 2 months all the pathologists again reviewed the same images presented to them in a different order. We found that there was poor reproducibility within observers at 2× power using a 5% evaluation, but that this improved using 10% or 25% cutoffs. Use of 10× magnification allowed weak to moderate reproducibility at 5% increments, and this was again improved using 10% or 25% cutoffs. Correlation with morphometric assessment was poor except for the papillary and micropapillary subtypes. Differences among pathologists were generally low except for the acinar and, to a lesser degree, lepidic subtypes, which showed a wide spread of data. When estimating tumor subtype proportions, use of a 10× objective, and utilization of 10% or preferably 25% cutoffs provides a greater degree of consistency than a 5% cutoff.

Indexed as

Adenocarcinoma of LungHumansLung NeoplasmsObserver VariationPathology, ClinicalReproducibility of Results

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.