Evidence map›Paper›PMID 33141910›Full record

ArticleClinical chemistry2020

Distinguishing Non-Small Cell Lung Cancer Subtypes in Fine Needle Aspiration Biopsies by Desorption Electrospray Ionization Mass Spectrometry Imaging.

Alena V Bensussan, John Lin, Chunxiao Guo, Ruth Katz, Savitri Krishnamurthy, Erik Cressman, Livia S Eberlin

Open access · bronzeAbstract read
In one paragraph

Article in Clinical chemistry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
2.1field-weighted citation impact, top 12% 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

20 citing papers in PubMed, 36 citations in OpenAlex.

  1. Article
  2. Lipidomic profiling of endometrial cancer using desorption electrospray ionization mass spectrometry imaging.Proceedings of the National Academy of Sciences of the United States of America · 2025
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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

7 authors at 2 institutions in 1 country.

Alena V BensussanDepartment of Chemistry, The University of Texas at Austin, Austin, TX.
John LinDepartment of Chemistry, The University of Texas at Austin, Austin, TX.
Chunxiao GuoDepartment of Interventional Radiology, Division of Diagnostic Imaging, MD Anderson Cancer Center, Houston, TX.
Ruth KatzDepartment of Pathology, Division of Pathology and Laboratory Medicine, MD Anderson Cancer Center, Houston, TX.
Savitri KrishnamurthyDepartment of Pathology, Division of Pathology and Laboratory Medicine, MD Anderson Cancer Center, Houston, TX.
Erik CressmanDepartment of Interventional Radiology, Division of Diagnostic Imaging, MD Anderson Cancer Center, Houston, TX.
Livia S EberlinDepartment of Chemistry, The University of Texas at Austin, Austin, TX.
The University of Texas MD Anderson Cancer Center · USThe University of Texas at Austin · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDistinguishing adenocarcinoma and squamous cell carcinoma subtypes of non-small cell lung cancers is critical to patient care. Preoperative minimally-invasive biopsy techniques, such as fine needle aspiration (FNA), are increasingly used for lung cancer diagnosis and subtyping. Yet, histologic distinction of lung cancer subtypes in FNA material can be challenging. Here, we evaluated the usefulness of desorption electrospray ionization mass spectrometry imaging (DESI-MSI) to diagnose and differentiate lung cancer subtypes in tissues and FNA samples.

methodsDESI-MSI was used to analyze 22 normal, 26 adenocarcinoma, and 25 squamous cell carcinoma lung tissues. Mass spectra obtained from the tissue sections were used to generate and validate statistical classifiers for lung cancer diagnosis and subtyping. Classifiers were then tested on DESI-MSI data collected from 16 clinical FNA samples prospectively collected from 8 patients undergoing interventional radiology guided FNA.

resultsVarious metabolites and lipid species were detected in the mass spectra obtained from lung tissues. The classifiers generated from tissue sections yielded 100% accuracy, 100% sensitivity, and 100% specificity for lung cancer diagnosis, and 73.5% accuracy for lung cancer subtyping for the training set of tissues, per-patient. On the validation set of tissues, 100% accuracy for lung cancer diagnosis and 94.1% accuracy for lung cancer subtyping were achieved. When tested on the FNA samples, 100% diagnostic accuracy and 87.5% accuracy on subtyping were achieved per-slide.

conclusionsDESI-MSI can be useful as an ancillary technique to conventional cytopathology for diagnosis and subtyping of non-small cell lung cancers.

Indexed as

AdenocarcinomaBiopsy, Fine-NeedleCarcinoma, Non-Small-Cell LungCarcinoma, Squamous CellHumansLungLung NeoplasmsSpectrometry, Mass, Electrospray Ionizationadenocarcinomadesorption electrospray ionizationimaginglungmass spectrometrysquamous cell carcinomasubtypes

Identifiers

PMID33141910
PMCPMC7608526
OpenAlexW3097167541

What OpenQuestion holds

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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.