Evidence map›Paper›PMID 34053013›Full record

ArticleInternational journal of computer assisted radiology and surgery2021

Metabolomics patterns of breast cancer tumors using mass spectrometry imaging.

Rachel L Theriault, Martin Kaufmann, Kevin Y M Ren, Sonal Varma, Randy E Ellis

Abstract readMulticenter Study
PubMed Publisher
In one paragraph

Article in International journal of computer assisted radiology and surgery, 2021. 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
1.4field-weighted citation impact, top 21% 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

4 citing papers in PubMed, 14 citations in OpenAlex.

  1. Review
  2. Article
  3. Metabolic heterogeneity in cancer.Nature metabolism · 2024
    Review
  4. Review
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

5 authors at 1 institution in 1 country.

Rachel L TheriaultQueen's University, Kingston, ON, K7L 3N6, Canada.
Martin KaufmannQueen's University, Kingston, ON, K7L 3N6, Canada.
Kevin Y M RenQueen's University, Kingston, ON, K7L 3N6, Canada.
Sonal VarmaQueen's University, Kingston, ON, K7L 3N6, Canada.
Randy E EllisQueen's University, Kingston, ON, K7L 3N6, Canada. ellis@queensu.ca.
Queen's University · CA

Funding

Natural Sciences and Engineering Research Council of Canada RGPIN-2018-04430
6 · The paper itself

Abstract

purposeIntraoperative assessment of surgical margins is important for reducing the rate of revisions in breast conserving surgery for palpable malignant tumors. The hypothesis was that metabolomics methods, based on mass spectrometry, could find patterns of relative abundances of molecules that distinguish clusters of benign tissue and cancer in surgical resections.

methodsExcisions from 8 patients were used to acquire 112,317 mass spectrometry signals by desorption electrospray ionization. A process of nonnegative matrix factorization and graph decomposition produced clusters that were approximated as affine spaces. Each signal's distance to the affine space of a cluster was used to visualize the clustering.

resultsThe distance maps were superior to binary clustering in identifying cancer regions. They were particularly effective at finding cancer regions that were discontinuously distributed within benign tissue.

conclusionsDesorption electrospray ionization mass spectrometry, which has been shown to be useful intraoperatively, can acquire signals that distinguish malignant from benign breast tissue in surgically excised tumors. The method may be suitable for real-time surgical decisions based on cancer margins.

Indexed as

MetabolomicsBreastBreast NeoplasmsFemaleHumansMastectomyMiddle AgedSpectrometry, Mass, Electrospray IonizationBreast cancerMass spectrometry imagingMetabolomics

Identifiers

PMID34053013
OpenAlexW3164246580

What OpenQuestion holds

Textmetadata
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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.