Evidence map›Paper›PMID 33120083›Full record

ArticleBreast (Edinburgh, Scotland)2020

Digital analysis of distant and cancer-associated mammary adipocytes.

Edoardo Isnaldi, François Richard, Maxim De Schepper, Delphine Vincent, Sophia Leduc, Marion Maetens, Tatjana Geukens, Giuseppe Floris, Ghizlane Rouas, Fatima Cardoso and 5 more

Open access · goldAbstract readComparative Study
In one paragraph

Article in Breast (Edinburgh, Scotland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
0.3field-weighted citation impact, top 38% of its field
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

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.

  1. Pooled it
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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

15 authors at 7 institutions in 4 countries.

Edoardo IsnaldiKU Leuven, Department of Oncology, Laboratory for Translational Breast Cancer Research, B-3000, Leuven, Belgium; Department of Internal Medicine and Medical Specialties, University of Genoa, IT-16132, Genoa, Italy.
François RichardKU Leuven, Department of Oncology, Laboratory for Translational Breast Cancer Research, B-3000, Leuven, Belgium.
Maxim De SchepperKU Leuven, Department of Oncology, Laboratory for Translational Breast Cancer Research, B-3000, Leuven, Belgium.
Delphine VincentUniversité Libre de Bruxelles, Institut Jules Bordet, J.C. Heuson Breast Cancer Translational Research Laboratory, B-1000, Brussels, Belgium.
Sophia LeducKU Leuven, Department of Oncology, Laboratory for Translational Breast Cancer Research, B-3000, Leuven, Belgium.
Marion MaetensKU Leuven, Department of Oncology, Laboratory for Translational Breast Cancer Research, B-3000, Leuven, Belgium.
Tatjana GeukensKU Leuven, Department of Oncology, Laboratory for Translational Breast Cancer Research, B-3000, Leuven, Belgium.
Giuseppe FlorisKU Leuven, Translational Cell and Tissue Research Unit, Department of Imaging and Pathology, B-3000, Leuven, Belgium; University Hospitals Leuven, Department of Pathology, B-3000, Leuven, Belgium.
Ghizlane RouasUniversité Libre de Bruxelles, Institut Jules Bordet, J.C. Heuson Breast Cancer Translational Research Laboratory, B-1000, Brussels, Belgium.
Fatima CardosoChampalimaud Clinical Center-Champalimaud Foundation, Breast Unit, P-1400-038, Lisbon, Portugal.
Christos SotiriouUniversité Libre de Bruxelles, Institut Jules Bordet, J.C. Heuson Breast Cancer Translational Research Laboratory, B-1000, Brussels, Belgium.
Gabriele ZoppoliDepartment of Internal Medicine and Medical Specialties, University of Genoa, IT-16132, Genoa, Italy; Ospedale Policlinico San Martino IRCCS per L'Oncologia, IT-16132, Genoa, Italy.
Denis LarsimontUniversité Libre de Bruxelles, Institut Jules Bordet, Pathology Department, B-1000, Brussels, Belgium.
Elia BiganzoliUnit of Medical Statistics, Biometry and Bioinformatics "Giulio A. Maccacaro", Department of Clinical Sciences and Community Health & DSRC, University of Milan, Campus Cascina Rosa, Fondazione IRCCS Istituto Nazionale Tumori, IT-20133, Milan, Italy.
Christine DesmedtKU Leuven, Department of Oncology, Laboratory for Translational Breast Cancer Research, B-3000, Leuven, Belgium. Electronic address: christine.desmedt@kuleuven.be.
KU Leuven · BEUniversité Libre de Bruxelles · BEBreast Cancer Research Foundation · USChampalimaud Foundation · PTOspedale Policlinico San Martino · ITUniversity of Genoa · ITUniversity of Milan · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Adipocytes and cancer-associated adipocytes (CAAs) are poorly investigated cells in the tumor microenvironment. Different image analysis software exist for identifying and measuring these cells using scanned hematoxylin and eosin (H&E)-stained slides. It is however unclear which one is the most appropriate for breast cancer (BC) samples. Here, we compared three software (AdipoCount, Adiposoft, and HALO®). HALO® outperformed the other methods with regard to adipocyte identification, (> 96% sensitivity and specificity). All software performed equally good with regard to area and diameter measurement (concordance correlation coefficients > 0.97 and > 0.96, respectively). We then analyzed a series of 10 BCE samples (n = 51 H&E slides) with HALO®. Distant adipocytes were defined >2 mm away from cancer cells or fibrotic region, whereas CAAs as the first three lines of adipocytes close to the invasive front. Intra-mammary heterogeneity was limited, implying that measuring a single region of ∼500 adipocytes provides a reliable estimation of the distribution of their size features. CAAs had smaller areas (median fold-change: 2.62) and diameters (median fold-change: 1.64) as compared to distant adipocytes in the same breast (both p = 0.002). The size of CAAs and distant adipocytes was associated with the body mass index (BMI) of the patient (area: rho = 0.89, p = 0.001; rho = 0.71, p = 0.027, diameter: rho = 0.87 p = 0.002; rho = 0.65 p = 0.049, respectively). To conclude, we demonstrate that quantifying adipocytes in BC sections is feasible by digital pathology using H&E sections, setting the basis for a standardized analysis of mammary adiposity in larger series of patients.

Indexed as

AdipocytesBody Mass IndexBreastBreast NeoplasmsCarcinoma, Ductal, BreastFemaleHumansImage Processing, Computer-AssistedMiddle AgedSoftwareTumor MicroenvironmentAdipocytesBreast cancerCancer-associated adipocytesDigital pathologyObesity

Identifiers

PMID33120083
PMCPMC7589564
OpenAlexW3093063356

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.