A novel technology to integrate imaging and clinical markers for non-invasive diagnosis of lung cancer

Abstract This study presents a non-invasive, automated, clinical diagnostic system for early diagnosis of lung cancer that integrates imaging data from a single computed tomography scan and breath bio-markers obtained from a single exhaled breath to quickly and accurately classify lung nodules. CT i...

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Autores principales:	Ahmed Shaffie, Ahmed Soliman, Xiao-An Fu, Michael Nantz, Guruprasad Giridharan, Victor van Berkel, Hadil Abu Khalifeh, Mohammed Ghazal, Adel Elmaghraby, Ayman El-baz
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2d567499a10d48f7a06cf8aef5a82a5e
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spelling	oai:doaj.org-article:2d567499a10d48f7a06cf8aef5a82a5e2021-12-02T13:34:33ZA novel technology to integrate imaging and clinical markers for non-invasive diagnosis of lung cancer10.1038/s41598-021-83907-52045-2322https://doaj.org/article/2d567499a10d48f7a06cf8aef5a82a5e2021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-83907-5https://doaj.org/toc/2045-2322Abstract This study presents a non-invasive, automated, clinical diagnostic system for early diagnosis of lung cancer that integrates imaging data from a single computed tomography scan and breath bio-markers obtained from a single exhaled breath to quickly and accurately classify lung nodules. CT imaging and breath volatile organic compounds data were collected from 47 patients. Spherical Harmonics-based shape features to quantify the shape complexity of the pulmonary nodules, 7th-Order Markov Gibbs Random Field based appearance model to describe the spatial non-homogeneities in the pulmonary nodule, and volumetric features (size) of pulmonary nodules were calculated from CT images. 27 VOCs in exhaled breath were captured by a micro-reactor approach and quantied using mass spectrometry. CT and breath markers were input into a deep-learning autoencoder classifier with a leave-one-subject-out cross validation for nodule classification. To mitigate the limitation of a small sample size and validate the methodology for individual markers, retrospective CT scans from 467 patients with 727 pulmonary nodules, and breath samples from 504 patients were analyzed. The CAD system achieved 97.8% accuracy, 97.3% sensitivity, 100% specificity, and 99.1% area under curve in classifying pulmonary nodules.Ahmed ShaffieAhmed SolimanXiao-An FuMichael NantzGuruprasad GiridharanVictor van BerkelHadil Abu KhalifehMohammed GhazalAdel ElmaghrabyAyman El-bazNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ahmed Shaffie Ahmed Soliman Xiao-An Fu Michael Nantz Guruprasad Giridharan Victor van Berkel Hadil Abu Khalifeh Mohammed Ghazal Adel Elmaghraby Ayman El-baz A novel technology to integrate imaging and clinical markers for non-invasive diagnosis of lung cancer
description	Abstract This study presents a non-invasive, automated, clinical diagnostic system for early diagnosis of lung cancer that integrates imaging data from a single computed tomography scan and breath bio-markers obtained from a single exhaled breath to quickly and accurately classify lung nodules. CT imaging and breath volatile organic compounds data were collected from 47 patients. Spherical Harmonics-based shape features to quantify the shape complexity of the pulmonary nodules, 7th-Order Markov Gibbs Random Field based appearance model to describe the spatial non-homogeneities in the pulmonary nodule, and volumetric features (size) of pulmonary nodules were calculated from CT images. 27 VOCs in exhaled breath were captured by a micro-reactor approach and quantied using mass spectrometry. CT and breath markers were input into a deep-learning autoencoder classifier with a leave-one-subject-out cross validation for nodule classification. To mitigate the limitation of a small sample size and validate the methodology for individual markers, retrospective CT scans from 467 patients with 727 pulmonary nodules, and breath samples from 504 patients were analyzed. The CAD system achieved 97.8% accuracy, 97.3% sensitivity, 100% specificity, and 99.1% area under curve in classifying pulmonary nodules.
format	article
author	Ahmed Shaffie Ahmed Soliman Xiao-An Fu Michael Nantz Guruprasad Giridharan Victor van Berkel Hadil Abu Khalifeh Mohammed Ghazal Adel Elmaghraby Ayman El-baz
author_facet	Ahmed Shaffie Ahmed Soliman Xiao-An Fu Michael Nantz Guruprasad Giridharan Victor van Berkel Hadil Abu Khalifeh Mohammed Ghazal Adel Elmaghraby Ayman El-baz
author_sort	Ahmed Shaffie
title	A novel technology to integrate imaging and clinical markers for non-invasive diagnosis of lung cancer
title_short	A novel technology to integrate imaging and clinical markers for non-invasive diagnosis of lung cancer
title_full	A novel technology to integrate imaging and clinical markers for non-invasive diagnosis of lung cancer
title_fullStr	A novel technology to integrate imaging and clinical markers for non-invasive diagnosis of lung cancer
title_full_unstemmed	A novel technology to integrate imaging and clinical markers for non-invasive diagnosis of lung cancer
title_sort	novel technology to integrate imaging and clinical markers for non-invasive diagnosis of lung cancer
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/2d567499a10d48f7a06cf8aef5a82a5e
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A novel technology to integrate imaging and clinical markers for non-invasive diagnosis of lung cancer

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