Journal Highlight: Feasibility of desorption electrospray ionization mass spectrometry for diagnosis of oral tongue squamous cell carcinoma

Skip to Navigation

Ezine

  • Published: Jan 15, 2018
  • Author: spectroscopyNOW
  • Channels: Chemometrics & Informatics
thumbnail image: Journal Highlight: Feasibility of desorption electrospray ionization mass spectrometry for diagnosis of oral tongue squamous cell carcinoma

DESI-MS has been used to differentiate oral tongue squamous cell carcinoma from adjacent normal epithelium using PCA-LDA of the spectra, the m/z 700–900 prediction model showing a 91% accuracy rate.

Feasibility of desorption electrospray ionization mass spectrometry for diagnosis of oral tongue squamous cell carcinoma

Rapid Communications in Mass Spectrometry, 2018, 32, 133-141
Cedric D'Hue, Michael Moore, Don-John Summerlin, Alan Jarmusch, Clint Alfaro, Avinash Mantravadi, Arnaud Bewley, D. Gregory Farwell and R. Graham Cooks

Abstract: Desorption electrospray ionization mass spectrometry (DESI-MS) has demonstrated utility in differentiating tumor from adjacent normal tissue in both urologic and neurosurgical specimens. We sought to evaluate if this technique had similar accuracy in differentiating oral tongue squamous cell carcinoma (SCC) from adjacent normal epithelium due to current issues with late diagnosis of SCC in advanced stages. Fresh frozen samples of SCC and adjacent normal tissue were obtained by surgical resection. Resections were analyzed using DESI-MS sometimes by a blinded technologist. Normative spectra were obtained for separate regions containing SCC or adjacent normal epithelium. Principal Component Analysis and Linear Discriminant Analysis (PCA-LDA) of spectra were used to predict SCC versus normal tongue epithelium. Predictions were compared with pathology to assess accuracy in differentiating oral SCC from adjacent normal tissue. Initial PCA score and loading plots showed clear separation of SCC and normal epithelial tissue using DESI-MS. PCA-LDA resulted in accuracy rates of 95% for SCC versus normal and 93% for SCC, adjacent normal and normal. Additional samples were blindly analyzed with PCA-LDA pixel-by-pixel predicted classifications as SCC or normal tongue epithelial tissue and compared against histopathology. The m/z 700–900 prediction model showed a 91% accuracy rate. DESI-MS accurately differentiated oral SCC from adjacent normal epithelium. Classification of all typical tissue types and pixel predictions with additional classifications should increase confidence in the validation model.

  • This paper is free to view for all users registered on spectroscopyNOW.com until the end of March 2018.
    After this time, you can purchase it using Pay-Per-View on Wiley Online Library.

Follow us on Twitter!

Social Links

Share This Links

Bookmark and Share

Microsites

Suppliers Selection
Societies Selection

Banner Ad

Click here to see
all job opportunities

Copyright Information

Interested in separation science? Visit our sister site separationsNOW.com

Copyright © 2018 John Wiley & Sons, Inc. All Rights Reserved