Evidence map›Paper›PMID 35429321›Full record

ReviewBreast cancer research and treatment2022

Standardization of the tumor-stroma ratio scoring method for breast cancer research.

Sophie C Hagenaars, Kiki M H Vangangelt, Gabi W Van Pelt, Zsófia Karancsi, Rob A E M Tollenaar, Andrew R Green, Emad A Rakha, Janina Kulka, Wilma E Mesker

Abstract readReview
In one paragraph

Review in Breast cancer research and treatment, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing 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

29 citing papers in PubMed.

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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

9 authors.

Sophie C HagenaarsDepartment of Surgery, Leiden University Medical Centre, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.
Kiki M H VangangeltDepartment of Surgery, Leiden University Medical Centre, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.
Gabi W Van PeltDepartment of Surgery, Leiden University Medical Centre, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.
Zsófia Karancsi2Nd Department of Pathology, Semmelweis University, Budapest, Hungary.
Rob A E M TollenaarDepartment of Surgery, Leiden University Medical Centre, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.
Andrew R GreenAcademic Unit for Translational Medical Sciences, School of Medicine, Nottingham Breast Cancer Research Center, The University of Nottingham, Nottingham, UK.
Emad A RakhaAcademic Unit for Translational Medical Sciences, School of Medicine, Nottingham Breast Cancer Research Center, The University of Nottingham, Nottingham, UK.
Janina Kulka2Nd Department of Pathology, Semmelweis University, Budapest, Hungary.
Wilma E MeskerDepartment of Surgery, Leiden University Medical Centre, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands. w.e.mesker@lumc.nl.ORCID http://orcid.org/0000-0001-5533-4778

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe tumor-stroma ratio (TSR) has repeatedly proven to be correlated with patient outcomes in breast cancer using large retrospective cohorts. However, studies validating the TSR often show variability in methodology, thereby hampering comparisons and uniform outcomes.

methodThis paper provides a detailed description of a simple and uniform TSR scoring method using Hematoxylin and Eosin (H&E)-stained core biopsies and resection tissue, specifically focused on breast cancer. Possible histological challenges that can be encountered during scoring including suggestions to overcome them are reported. Moreover, the procedure for TSR estimation in lymph nodes, scoring on digital images and the automatic assessment of the TSR using artificial intelligence are described.

conclusionDigitized scoring of tumor biopsies and resection material offers interesting future perspectives to determine patient prognosis and response to therapy. The fact that the TSR method is relatively easy, quick, and cheap, offers great potential for its implementation in routine diagnostics, but this requires high quality validation studies.

Indexed as

Breast NeoplasmsArtificial IntelligenceFemaleHumansPrognosisReference StandardsResearch DesignRetrospective StudiesStromal CellsArtificial intelligenceBiomarkerBreast cancerProtocolTumor microenvironmentTumor-stroma ratio

Identifiers

PMID35429321
PMCPMC9114083

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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.