Evidence map›Paper›PMID 40604303›Full record

ArticleNpj imaging2024

TME-analyzer: a new interactive and dynamic image analysis tool that identified immune cell distances as predictors for survival of triple negative breast cancer patients.

Hayri E Balcioglu, Rebecca Wijers, Marcel Smid, Dora Hammerl, Anita M Trapman-Jansen, Astrid Oostvogels, Mieke Timmermans, John W M Martens, Reno Debets

Abstract read
In one paragraph

Article in Npj imaging, 2024. 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

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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. ALOA, a pipeline for preliminary analysis of spatial profiling imaging data.Computational and structural biotechnology journal · 2024
    Article
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.

Hayri E BalciogluLaboratory of Tumor Immunology, Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands. h.balcioglu@erasmusmc.nl.
Rebecca WijersLaboratory of Tumor Immunology, Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.
Marcel SmidLaboratory of Translational Cancer Genomics, Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.
Dora HammerlLaboratory of Tumor Immunology, Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.
Anita M Trapman-JansenLaboratory of Translational Cancer Genomics, Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.
Astrid OostvogelsLaboratory of Tumor Immunology, Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.
Mieke TimmermansLaboratory of Translational Cancer Genomics, Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.
John W M MartensLaboratory of Translational Cancer Genomics, Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.
Reno DebetsLaboratory of Tumor Immunology, Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands. j.debets@erasmusmc.nl.

Funding

Erasmus MC Daniel den Hoed Foundation Awarded to Reno Debets, 2017
6 · The paper itself

Abstract

Spatial distribution of intra-tumoral immune cell populations is considered a critical determinant of tumor evolution and response to therapy. The accurate and systemic search for contexture-based predictors would be accelerated by methods that allow interactive visualization and interrogation of tumor micro-environments (TME), independent of image acquisition platforms. To this end, we have developed the TME-Analyzer, a new image analysis tool, which we have benchmarked against 2 software tools regarding densities and networks of immune effector cells using multiplexed immune-fluorescent images of triple negative breast cancer (TNBC). With the TME-Analyzer we have identified a 10-parameter classifier, predominantly featuring cellular distances, that significantly predicted overall survival, and which was validated using multiplexed ion beam time of flight images from an independent cohort. In conclusion, the TME-Analyzer enabled accurate interactive analysis of the spatial immune phenotype from different imaging platforms as well as enhanced utility and aided the discovery of contextual predictors towards the survival of TNBC patients.

Identifiers

PMID40604303
PMCPMC12118654

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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.