Evidence map›Paper›PMID 39294437›Full record

ArticleBritish journal of cancer2024

Testing of rapid evaporative mass spectrometry for histological tissue classification and molecular diagnostics in a multi-site study.

Martin Kaufmann, Pierre-Maxence Vaysse, Adele Savage, Loes F S Kooreman, Natasja Janssen, Sonal Varma, Kevin Yi Mi Ren, Shaila Merchant, Cecil Jay Engel, Steven W M Olde Damink and 15 more

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in British journal of cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 2 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

6 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Review
  4. Article
  5. Review
  6. 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

25 authors.

Martin KaufmannDepartment of Surgery, Queen's University, Kingston, ON, Canada.
Pierre-Maxence VaysseMaastricht MultiModal Molecular Imaging (M4I) Institute, Division of Imaging Mass Spectrometry, Maastricht University, Maastricht, NL, Netherlands.
Adele SavageDivision of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK.
Loes F S KooremanDepartment of Pathology, MUMC+, Maastricht, NL, Netherlands.
Natasja JanssenSchool of Computing, Queen's University, Kingston, ON, Canada.
Sonal VarmaDepartment of Pathology, Queen's University, Kingston, ON, Canada.
Kevin Yi Mi RenDepartment of Pathology, Queen's University, Kingston, ON, Canada.
Shaila MerchantDepartment of Surgery, Queen's University, Kingston, ON, Canada.
Cecil Jay EngelDepartment of Surgery, Queen's University, Kingston, ON, Canada.
Steven W M Olde DaminkDepartment of Surgery, Maastricht University Medical Center + (MUMC+), Maastricht, NL, Netherlands.
Marjolein L SmidtDepartment of Surgery, Maastricht University Medical Center + (MUMC+), Maastricht, NL, Netherlands.
Sami ShoushaImperial NHS Trust, London, UK.
Hemali ChauhanDivision of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK.
Evdoxia KaraliSignalling and Cancer Metabolism Team, The Institute of Cancer Research, London, UK.ORCID http://orcid.org/0000-0002-1196-3553
Emine KazancSignalling and Cancer Metabolism Team, The Institute of Cancer Research, London, UK.
George PoulogiannisSignalling and Cancer Metabolism Team, The Institute of Cancer Research, London, UK.ORCID http://orcid.org/0000-0002-0529-8614
Gabor FichtingerSchool of Computing, Queen's University, Kingston, ON, Canada.
Boglárka TauberQamcom Research & Technology Central Europe, Budapest, Hungary.
Daniel R LeffDepartment of Surgery and Cancer, Biosurgery and Surgical Technology, Imperial College London, London, UK.ORCID http://orcid.org/0000-0002-5310-1046
Steven D PringleWaters Corporation, Wilmslow, UK.
John F RudanDepartment of Surgery, Queen's University, Kingston, ON, Canada.
Ron M A HeerenMaastricht MultiModal Molecular Imaging (M4I) Institute, Division of Imaging Mass Spectrometry, Maastricht University, Maastricht, NL, Netherlands.ORCID http://orcid.org/0000-0002-6533-7179
Tiffany Porta SiegelMaastricht MultiModal Molecular Imaging (M4I) Institute, Division of Imaging Mass Spectrometry, Maastricht University, Maastricht, NL, Netherlands.
Zoltán TakátsDivision of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK.
Júlia BalogDivision of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK. Julia_balog@waters.com.ORCID http://orcid.org/0000-0003-4914-8797

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWhile REIMS technology has successfully been demonstrated for the histological identification of ex-vivo breast tumor tissues, questions regarding the robustness of the approach and the possibility of tumor molecular diagnostics still remain unanswered. In the current study, we set out to determine whether it is possible to acquire cross-comparable REIMS datasets at multiple sites for the identification of breast tumors and subtypes.

methodsA consortium of four sites with three of them having access to fresh surgical tissue samples performed tissue analysis using identical REIMS setups and protocols. Overall, 21 breast cancer specimens containing pathology-validated tumor and adipose tissues were analyzed and results were compared using uni- and multivariate statistics on normal, WT and PIK3CA mutant ductal carcinomas.

resultsStatistical analysis of data from standards showed significant differences between sites and individual users. However, the multivariate classification models created from breast cancer data elicited 97.1% and 98.6% correct classification for leave-one-site-out and leave-one-patient-out cross validation. Molecular subtypes represented by PIK3CA mutation gave consistent results across sites.

conclusionsThe results clearly demonstrate the feasibility of creating and using global classification models for a REIMS-based margin assessment tool, supporting the clinical translatability of the approach.

Indexed as

Breast NeoplasmsClass I Phosphatidylinositol 3-KinasesMutationCarcinoma, Ductal, BreastFemaleHumansMass SpectrometryPathology, MolecularClass I Phosphatidylinositol 3-KinasesPIK3CA protein, human

Identifiers

PMID39294437
PMCPMC11473823

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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.