Bedoukian   RussellIPM   RussellIPM   Piezoelectric Micro-Sprayer


Home
Animal Taxa
Plant Taxa
Semiochemicals
Floral Compounds
Semiochemical Detail
Semiochemicals & Taxa
Synthesis
Control
Invasive spp.
References

Abstract

Guide

Alphascents
Pherobio
InsectScience
E-Econex
Counterpart-Semiochemicals
Print
Email to a Friend
Kindly Donate for The Pherobase

« Previous AbstractImproving lung cancer diagnosis by combining exhaled-breath data and clinical parameters    Next AbstractNatural variation in wild tomato trichomes; selecting metabolites that contribute to insect resistance using a random forest approach »

Chest


Title:Diagnosing Non-Small Cell Lung Cancer by Exhaled Breath Profiling Using an Electronic Nose: A Multicenter Validation Study
Author(s):Kort S; Brusse-Keizer M; Schouwink H; Citgez E; de Jongh FH; van Putten JWG; van den Borne B; Kastelijn EA; Stolz D; Schuurbiers M; van den Heuvel MM; van Geffen WH; van der Palen J;
Address:"Department of Respiratory Medicine, Medisch Spectrum Twente Enschede, Enschede, The Netherlands. Electronic address: s.kort@mst.nl. Medical School Twente, Enschede, The Netherlands; Universiteit of Twente, Faculty of Behavioural Management and Social Sciences, Enschede, The Netherlands. Department of Respiratory Medicine, Medisch Spectrum Twente Enschede, Enschede, The Netherlands. Department of Respiratory Medicine, Medisch Spectrum Twente Enschede, Enschede, The Netherlands; Universiteit of Twente, Faculty of Behavioural Management and Social Sciences, Enschede, The Netherlands. Department of Respiratory Medicine, Martini Ziekenhuis, Groningen, The Netherlands. Department of Respiratory Medicine, Catharina Ziekenhuis, Eindhoven, The Netherlands. Department of Respiratory Medicine, Sint Antonius Ziekenhuis, Utrecht, The Netherlands. Clinic for Pulmonary Medicine and Respiratory Cell Research, Universitatspital Basel, Basel, Switzerland; Clinic for Respiratory Medicine, Medical Center, University of Freiburg, Faculty of Medicine, Freiburg, Germany. Department of Respiratory Medicine, Radboud UMC, Nijmegen, The Netherlands. Department of Respiratory Medicine, Medisch Centrum Leeuwarden, Leeuwarden, The Netherlands"
Journal Title:Chest
Year:2023
Volume:20221013
Issue:3
Page Number:697 - 706
DOI: 10.1016/j.chest.2022.09.042
ISSN/ISBN:1931-3543 (Electronic) 0012-3692 (Linking)
Abstract:"BACKGROUND: Despite the potential of exhaled breath analysis of volatile organic compounds to diagnose lung cancer, clinical implementation has not been realized, partly due to the lack of validation studies. RESEARCH QUESTION: This study addressed two questions. First, can we simultaneously train and validate a prediction model to distinguish patients with non-small cell lung cancer from non-lung cancer subjects based on exhaled breath patterns? Second, does addition of clinical variables to exhaled breath data improve the diagnosis of lung cancer? STUDY DESIGN AND METHODS: In this multicenter study, subjects with non-small cell lung cancer and control subjects performed 5 min of tidal breathing through the aeoNose, a handheld electronic nose device. A training cohort was used for developing a prediction model based on breath data, and a blinded cohort was used for validation. Multivariable logistic regression analysis was performed, including breath data and clinical variables, in which the formula and cutoff value for the probability of lung cancer were applied to the validation data. RESULTS: A total of 376 subjects formed the training set, and 199 subjects formed the validation set. The full training model (including exhaled breath data and clinical parameters from the training set) were combined in a multivariable logistic regression analysis, maintaining a cut off of 16% probability of lung cancer, resulting in a sensitivity of 95%, a specificity of 51%, and a negative predictive value of 94%; the area under the receiver-operating characteristic curve was 0.87. Performance of the prediction model on the validation cohort showed corresponding results with a sensitivity of 95%, a specificity of 49%, a negative predictive value of 94%, and an area under the receiver-operating characteristic curve of 0.86. INTERPRETATION: Combining exhaled breath data and clinical variables in a multicenter, multi-device validation study can adequately distinguish patients with lung cancer from subjects without lung cancer in a noninvasive manner. This study paves the way to implement exhaled breath analysis in the daily practice of diagnosing lung cancer. CLINICAL TRIAL REGISTRATION: The Netherlands Trial Register; No.: NL7025; URL: https://trialregister.nl/trial/7025"
Keywords:"Humans *Carcinoma, Non-Small-Cell Lung/diagnosis *Lung Neoplasms/diagnosis Electronic Nose Predictive Value of Tests Exhalation Breath Tests/methods *Volatile Organic Compounds/analysis exhaled breath lung cancer validation;"
Notes:"MedlineKort, Sharina Brusse-Keizer, Marjolein Schouwink, Hugo Citgez, Emanuel de Jongh, Frans H van Putten, Jan W G van den Borne, Ben Kastelijn, Elisabeth A Stolz, Daiana Schuurbiers, Milou van den Heuvel, Michel M van Geffen, Wouter H van der Palen, Job eng Multicenter Study Research Support, Non-U.S. Gov't 2022/10/16 Chest. 2023 Mar; 163(3):697-706. doi: 10.1016/j.chest.2022.09.042. Epub 2022 Oct 13"

 
Back to top
 
Citation: El-Sayed AM 2024. The Pherobase: Database of Pheromones and Semiochemicals. <http://www.pherobase.com>.
© 2003-2024 The Pherobase - Extensive Database of Pheromones and Semiochemicals. Ashraf M. El-Sayed.
Page created on 03-07-2024