Evidence map›Paper›PMID 29901523›Full record

ArticleInternational journal of gynecological pathology : official journal of the International Society of Gynecological Pathologists2019

Ovarian Carcinoma Histotype: Strengths and Limitations of Integrating Morphology With Immunohistochemical Predictions.

Martin Köbel, Li Luo, Xin Grevers, Sandra Lee, Angela Brooks-Wilson, C Blake Gilks, Nhu D Le, Linda S Cook

Abstract read
In one paragraph

Article in International journal of gynecological pathology : official journal of the International Society of Gynecological Pathologists, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed.

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  10. Deep learning-based histotype diagnosis of ovarian carcinoma whole-slide pathology images.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2022
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Martin KöbelDepartments of Pathology and Laboratory Medicine (M.K., S.L.) Community Health Sciences (L.S.C.), University of Calgary Department of Cancer Epidemiology and Prevention Research, Alberta Health Services-Cancer Control Alberta (X.G., L.S.C.), Calgary, Alberta Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver; and Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby (A.B.-W.) Department of Pathology and Laboratory Medicine, University of British Columbia (C.B.G.) Cancer Control Research, BC Cancer Research Centre (N.D.L.), Vancouver, British Columbia, Canada Department of Internal Medicine, Division of Epidemiology, Biostatistics and Preventive Medicine, UNM Comprehensive Cancer Center, University of New Mexico, Albuquerque, New Mexico (L.L., L.S.C.).
Li Luo
Xin Grevers
Sandra Lee
Angela Brooks-Wilson
C Blake Gilks
Nhu D Le
Linda S Cook

Funding

WOMEN'S CANCERS RESEARCH PROGRAMP30CA118100 · NCI · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · PI Yolanda Sanchez · 2005 to 2026
$57.1M
Mitochondrial DNA and Ovarian Cancer Risk and SurvivalR01CA160669 · NCI · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · PI COOK, LINDA S · 2012 to 2016
$2.2M
NCI NIH HHS P30 CA118100NCI NIH HHS R01 CA160669
6 · The paper itself

Abstract

Ovarian carcinoma histotypes are critical for research and patient management and currently assigned by a combination of histomorphology +/- ancillary immunohistochemistry (IHC). We aimed to validate the previously described IHC algorithm (Calculator of Ovarian carcinoma Subtype/histotype Probability version 3, COSPv3) in an independent population-based cohort, and to identify problem areas for IHC predictions. Histotype was abstracted from cancer registries for eligible ovarian carcinoma cases diagnosed from 2002 to 2011 in Alberta and British Columbia, Canada. Slides were reviewed according to World Health Organization 2014 criteria, tissue microarrays were stained with and scored for the 8 COSPv3 IHC markers, and COSPv3 histotype predictions were calculated. Discordant cases for review and COSPv3 prediction were arbitrated by integrating morphology with IHC results. The integrated histotype (N=880) was then used to identify areas of inferior COSPv3 performance. Review histotype and integrated histotype demonstrated 93% agreement suggesting that IHC information revises expert review in up to 7% of cases. There was also 93% agreement between COSPv3 prediction and integrated histotype. COSPv3 errors predominated in 4 areas: endometrioid carcinoma (EC) versus clear cell (N=23), EC versus low-grade serous (N=15), EC versus high-grade serous (N=11), and high-grade versus low-grade serous (N=6). Most problems were related to Napsin A-negative clear cell, WT1-positive EC, and p53 IHC wild-type high-grade serous carcinomas. Although 93% of COSPv3 prediction accuracy was validated, some histotyping required integration of morphology with ancillary test results. Awareness of these limitations will avoid overreliance on IHC and misclassification of histotypes for research and clinical management.

Indexed as

AlgorithmsBiomarkers, TumorCanadaCohort StudiesFemaleHumansImmunohistochemistryLogistic ModelsNeoplasm GradingOvarian NeoplasmsOvaryPractice Guidelines as TopicRegistriesTissue Array AnalysisBiomarkers, Tumor

Identifiers

PMID29901523
PMCPMC6291360

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Registered trials

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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.