Evidence map›Paper›PMID 30143750›Full record

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

An international multicenter study to evaluate reproducibility of automated scoring for assessment of Ki67 in breast cancer.

David L Rimm, Samuel C Y Leung, Lisa M McShane, Yalai Bai, Anita L Bane, John M S Bartlett, Jane Bayani, Martin C Chang, Michelle Dean, Carsten Denkert and 20 more

Abstract readMulticenter Study
PubMed Publisher
In one paragraph

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 47 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
47citing papers in PubMed, 1 pooled it
–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

47 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  20. ERBB2 mutation is associated with sustained tumor cell proliferation after short-term preoperative endocrine therapy in early lobular breast cancer.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

30 authors.

David L RimmDepartment of Pathology, Yale University School of Medicine, New Haven, CT, USA. david.rimm@yale.edu.
Samuel C Y LeungDepartment of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada.
Lisa M McShaneBiometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD, USA.ORCID http://orcid.org/0000-0001-8195-3206
Yalai BaiDepartment of Pathology, Yale University School of Medicine, New Haven, CT, USA.
Anita L BaneDepartment of Pathology and Molecular Medicine, Juravinski Hospital and Cancer Centre, McMaster University, Hamilton, ON, Canada.
John M S BartlettTransformative Pathology, Ontario Institute for Cancer Research, Toronto, ON, Canada.
Jane BayaniTransformative Pathology, Ontario Institute for Cancer Research, Toronto, ON, Canada.
Martin C ChangSinai Health System, University of Toronto, Toronto, ON, Canada.
Michelle DeanTranslational Laboratories, Alberta Health Services, Tom Baker Cancer Centre, Calgary, AB, Canada.
Carsten DenkertInstitut für Pathologie and German Cancer Consortium (DKTK), Charité Campus Mitte, Berlin, Germany.
Emeka K EnwereTranslational Laboratories, Alberta Health Services, Tom Baker Cancer Centre, Calgary, AB, Canada.
Chad GalderisiMolecularMD, Portland, OR, USA.
Abhi GholapOptra Technologies, NeoPro SEZ, Blue Ridge, Hinjewadi, India.
Judith C HughDepartment of Laboratory Medicine and Pathology, University of Alberta, Edmonton, AB, Canada.
Anagha JadhavOptra Technologies, NeoPro SEZ, Blue Ridge, Hinjewadi, India.
Elizabeth N KornagaTranslational Laboratories, Alberta Health Services, Tom Baker Cancer Centre, Calgary, AB, Canada.
Arvydas LaurinaviciusNational Center of Pathology, Vilnius University Hospital Santara Clinics, Vilnius University, Vilnius, Lithuania.
Richard LevensonDepartment of Medical Pathology and Laboratory Medicine, University of California Davis Medical Center, Sacramento, CA, USA.
Joema LimaTransformative Pathology, Ontario Institute for Cancer Research, Toronto, ON, Canada.
Keith MillerCancer Diagnostic Quality Assurance Services CIC, Poundbury Cancer Institute, Poundbury, DT, UK.
Liron PantanowitzDepartment of Pathology, University of Pittsburgh, Pittsburgh, PA, USA.
Tammy PiperBiomarkers & Companion Diagnostics Group, Edinburgh Cancer Research Centre, Edinburgh, UK.
Jason RuanDepartment of Medical Pathology and Laboratory Medicine, University of California Davis Medical Center, Sacramento, CA, USA.
Malini SrinivasanDepartment of Pathology, University of Pittsburgh, Pittsburgh, PA, USA.
Shakeel VirkDepartment of Pathology and Molecular Medicine, Queen's University, Kingston, ON, Canada.
Ying WuDepartment of Pathology and Molecular Medicine, Juravinski Hospital and Cancer Centre, McMaster University, Hamilton, ON, Canada.
Hua YangDepartment of Laboratory Medicine and Pathology, University of Alberta, Edmonton, AB, Canada.
Daniel F HayesBreast Oncology Program, Department of Internal Medicine, University of Michigan Comprehensive Cancer Center, Ann Arbor, MI, USA.
Torsten O NielsenDepartment of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada.
Mitch DowsettInstitute of Cancer Research, London, UK.

Funding

Cancer Research UK 16591
6 · The paper itself

Abstract

The nuclear proliferation biomarker Ki67 has potential prognostic, predictive, and monitoring roles in breast cancer. Unacceptable between-laboratory variability has limited its clinical value. The International Ki67 in Breast Cancer Working Group investigated whether Ki67 immunohistochemistry can be analytically validated and standardized across laboratories using automated machine-based scoring. Sets of pre-stained core-cut biopsy sections of 30 breast tumors were circulated to 14 laboratories for scanning and automated assessment of the average and maximum percentage of tumor cells positive for Ki67. Seven unique scanners and 10 software platforms were involved in this study. Pre-specified analyses included evaluation of reproducibility between all laboratories (primary) as well as among those using scanners from a single vendor (secondary). The primary reproducibility metric was intraclass correlation coefficient between laboratories, with success considered to be intraclass correlation coefficient >0.80. Intraclass correlation coefficient for automated average scores across 16 operators was 0.83 (95% credible interval: 0.73-0.91) and intraclass correlation coefficient for maximum scores across 10 operators was 0.63 (95% credible interval: 0.44-0.80). For the laboratories using scanners from a single vendor (8 score sets), intraclass correlation coefficient for average automated scores was 0.89 (95% credible interval: 0.81-0.96), which was similar to the intraclass correlation coefficient of 0.87 (95% credible interval: 0.81-0.93) achieved using these same slides in a prior visual-reading reproducibility study. Automated machine assessment of average Ki67 has the potential to achieve between-laboratory reproducibility similar to that for a rigorously standardized pathologist-based visual assessment of Ki67. The observed intraclass correlation coefficient was worse for maximum compared to average scoring methods, suggesting that maximum score methods may be suboptimal for consistent measurement of proliferation. Automated average scoring methods show promise for assessment of Ki67 scoring, but requires further standardization and subsequent clinical validation.

Indexed as

Biomarkers, TumorBreast NeoplasmsFemaleHumansImage Processing, Computer-AssistedImmunohistochemistryKi-67 AntigenReproducibility of ResultsBiomarkers, TumorKi-67 Antigen

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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.