Assessing the utility of autofluorescence-based pulmonary optical endomicroscopy to predict the malignant potential of solitary pulmonary nodules in humans

Solitary pulmonary nodules are common, often incidental findings on chest CT scans. The investigation of pulmonary nodules is time-consuming and often leads to protracted follow-up with ongoing radiological surveillance, however, clinical calculators that assess the risk of the nodule being malignan...

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Veröffentlicht in:Scientific reports 2016-08, Vol.6 (1), p.31372-31372, Article 31372
Hauptverfasser: Seth, Sohan, Akram, Ahsan R., McCool, Paul, Westerfeld, Jody, Wilson, David, McLaughlin, Stephen, Dhaliwal, Kevin, Williams, Christopher K. I.
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container_issue 1
container_start_page 31372
container_title Scientific reports
container_volume 6
creator Seth, Sohan
Akram, Ahsan R.
McCool, Paul
Westerfeld, Jody
Wilson, David
McLaughlin, Stephen
Dhaliwal, Kevin
Williams, Christopher K. I.
description Solitary pulmonary nodules are common, often incidental findings on chest CT scans. The investigation of pulmonary nodules is time-consuming and often leads to protracted follow-up with ongoing radiological surveillance, however, clinical calculators that assess the risk of the nodule being malignant exist to help in the stratification of patients. Furthermore recent advances in interventional pulmonology include the ability to both navigate to nodules and also to perform autofluorescence endomicroscopy. In this study we assessed the efficacy of incorporating additional information from label-free fibre-based optical endomicrosopy of the nodule on assessing risk of malignancy. Using image analysis and machine learning approaches, we find that this information does not yield any gain in predictive performance in a cohort of patients. Further advances with pulmonary endomicroscopy will require the addition of molecular tracers to improve information from this procedure.
doi_str_mv 10.1038/srep31372
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subjects 639/624
692/308
692/4028
Automation
Bronchoscopy
Cohort Studies
Female
Humanities and Social Sciences
Humans
Image Interpretation, Computer-Assisted - methods
Image processing
Learning algorithms
Lung cancer
Lung Neoplasms - diagnostic imaging
Lung nodules
Machine Learning
Male
Malignancy
Medical imaging
multidisciplinary
Optical Imaging - methods
Pathology
Patients
Risk assessment
Science
Solitary Pulmonary Nodule - diagnostic imaging
Surveillance
Tomography, X-Ray Computed
Tracers
title Assessing the utility of autofluorescence-based pulmonary optical endomicroscopy to predict the malignant potential of solitary pulmonary nodules in humans
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