Evidence map›Paper›PMID 28878011›Full record

ArticleScience translational medicine2017

Nondestructive tissue analysis for ex vivo and in vivo cancer diagnosis using a handheld mass spectrometry system.

Jialing Zhang, John Rector, John Q Lin, Jonathan H Young, Marta Sans, Nitesh Katta, Noah Giese, Wendong Yu, Chandandeep Nagi, James Suliburk and 10 more

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Science translational medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 166 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
166citing papers in PubMed, 1 pooled it
23.2field-weighted citation impact, top 1% 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

166 citing papers in PubMed, 1 synthesis or guideline pooled it, 385 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Metabolite ratiomics: Rapid diagnosis and classification of gliomas for intraoperative application during brain surgery.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
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  14. Recipient plasma metabolomics as a predictor of heart transplant severe primary graft dysfunction.American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons · 2026
    Article
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  19. Applications of DESI and DART Mass Spectrometry in Forensic Science.Journal of the American Society for Mass Spectrometry · 2025
    Review
  20. Review

106 more citing papers are in PubMed but not listed here.

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

20 authors at 3 institutions in 1 country.

Jialing ZhangDepartment of Chemistry, University of Texas at Austin, Austin, TX 78712, USA.ORCID http://orcid.org/0000-0001-9146-668X
John RectorDepartment of Chemistry, University of Texas at Austin, Austin, TX 78712, USA.ORCID http://orcid.org/0000-0003-3452-8076
John Q LinDepartment of Chemistry, University of Texas at Austin, Austin, TX 78712, USA.
Jonathan H YoungDepartment of Chemistry, University of Texas at Austin, Austin, TX 78712, USA.ORCID http://orcid.org/0000-0003-3365-2702
Marta SansDepartment of Chemistry, University of Texas at Austin, Austin, TX 78712, USA.ORCID http://orcid.org/0000-0002-7756-5101
Nitesh KattaDepartment of Biomedical Engineering, University of Texas at Austin, Austin, TX 78712, USA.ORCID http://orcid.org/0000-0001-8097-5489
Noah GieseDepartment of Chemistry, University of Texas at Austin, Austin, TX 78712, USA.ORCID http://orcid.org/0000-0002-0027-2239
Wendong YuDepartment of Pathology and Immunology, Baylor College of Medicine, Houston, TX 77030, USA.
Chandandeep NagiDepartment of Pathology and Immunology, Baylor College of Medicine, Houston, TX 77030, USA.
James SuliburkDepartment of Surgery, Baylor College of Medicine, Houston, TX 77030, USA.ORCID http://orcid.org/0000-0002-6421-269X
Jinsong LiuDepartment of Pathology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Alena BensussanDepartment of Chemistry, University of Texas at Austin, Austin, TX 78712, USA.ORCID http://orcid.org/0000-0002-1721-6356
Rachel J DeHoogDepartment of Chemistry, University of Texas at Austin, Austin, TX 78712, USA.ORCID http://orcid.org/0000-0003-1252-965X
Kyana Y GarzaDepartment of Chemistry, University of Texas at Austin, Austin, TX 78712, USA.ORCID http://orcid.org/0000-0002-7746-7317
Benjamin LudolphDepartment of Chemistry, University of Texas at Austin, Austin, TX 78712, USA.ORCID http://orcid.org/0000-0001-6052-9713
Anna G SoraceDepartment of Internal Medicine, Dell Medical School, University of Texas at Austin, Austin, TX 78712, USA.ORCID http://orcid.org/0000-0002-3640-3966
Anum SyedDepartment of Biomedical Engineering, University of Texas at Austin, Austin, TX 78712, USA.ORCID http://orcid.org/0000-0002-4200-534X
Aydin ZahedivashDepartment of Biomedical Engineering, University of Texas at Austin, Austin, TX 78712, USA.ORCID http://orcid.org/0000-0001-6835-1139
Thomas E MilnerDepartment of Biomedical Engineering, University of Texas at Austin, Austin, TX 78712, USA.
Livia S EberlinDepartment of Chemistry, University of Texas at Austin, Austin, TX 78712, USA. liviase@utexas.edu.ORCID http://orcid.org/0000-0002-3885-3215
The University of Texas at Austin · USBaylor College of Medicine · USThe University of Texas MD Anderson Cancer Center · US

Funding

Comprehensive Training Program in Imaging Science and InformaticsT32EB007507 · NIBIB · UNIVERSITY OF TEXAS AT AUSTIN · PI MARKEY, MIA K, RYLANDER, HENRY GRADY · 2009 to 2024
$2.7M
Mass Spectrometry Imaging for Clinical Diagnosis and Prognosis of Human CancersR00CA190783 · NCI · UNIVERSITY OF TEXAS AT AUSTIN · PI SCHIAVINATO EBERLIN, LIVIA · 2016 to 2018
$677k
NCI NIH HHS R00 CA190783NIBIB NIH HHS T32 EB007507
6 · The paper itself

Abstract

Conventional methods for histopathologic tissue diagnosis are labor- and time-intensive and can delay decision-making during diagnostic and therapeutic procedures. We report the development of an automated and biocompatible handheld mass spectrometry device for rapid and nondestructive diagnosis of human cancer tissues. The device, named MasSpec Pen, enables controlled and automated delivery of a discrete water droplet to a tissue surface for efficient extraction of biomolecules. We used the MasSpec Pen for ex vivo molecular analysis of 20 human cancer thin tissue sections and 253 human patient tissue samples including normal and cancerous tissues from breast, lung, thyroid, and ovary. The mass spectra obtained presented rich molecular profiles characterized by a variety of potential cancer biomarkers identified as metabolites, lipids, and proteins. Statistical classifiers built from the histologically validated molecular database allowed cancer prediction with high sensitivity (96.4%), specificity (96.2%), and overall accuracy (96.3%), as well as prediction of benign and malignant thyroid tumors and different histologic subtypes of lung cancer. Notably, our classifier allowed accurate diagnosis of cancer in marginal tumor regions presenting mixed histologic composition. Last, we demonstrate that the MasSpec Pen is suited for in vivo cancer diagnosis during surgery performed in tumor-bearing mouse models, without causing any observable tissue harm or stress to the animal. Our results provide evidence that the MasSpec Pen could potentially be used as a clinical and intraoperative technology for ex vivo and in vivo cancer diagnosis.

Indexed as

Organ SpecificityAnimalsDisease Models, AnimalFemaleHumansIntraoperative CareMass SpectrometryMice, NudeMolecular Diagnostic TechniquesNeoplasmsPrincipal Component Analysis

Identifiers

PMID28878011
PMCPMC5830136
OpenAlexW2753124759

What OpenQuestion holds

Textmetadata
LicenceTDM
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.