title: Assessing the utility of autofluorescence-based pulmonary optical endomicroscopy to predict the malignant potential of solitary pulmonary nodules in humans.
abstract: 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.
authors.affiliation: University of Edinburgh, School of Informatics, Edinburgh, EH8 9AB, UK.
authors.forename: Sohan
authors.initials: S
authors.lastname: Seth
authors.affiliation: University of Edinburgh, Queens Medical Research Institute, MRC Center for Inflammation Research, Pulmonary Molecular Imaging Group, Edinburgh, EH14 4TJ, UK.
authors.forename: Ahsan R
authors.initials: AR
authors.lastname: Akram
authors.affiliation: Heriot-Watt University, School of Engineering and Physical Sciences, Edinburgh, EH14 4AS, UK.
authors.forename: Paul
authors.initials: P
authors.lastname: McCool
authors.affiliation: Community Health Network, Community South Hospital, Indianapolis, IN 46227, USA.
authors.forename: Jody
authors.initials: J
authors.lastname: Westerfeld
authors.affiliation: Community Health Network, Community South Hospital, Indianapolis, IN 46227, USA.
authors.forename: David
authors.initials: D
authors.lastname: Wilson
authors.affiliation: Heriot-Watt University, School of Engineering and Physical Sciences, Edinburgh, EH14 4AS, UK.
authors.forename: Stephen
authors.initials: S
authors.lastname: McLaughlin
authors.affiliation: University of Edinburgh, Queens Medical Research Institute, MRC Center for Inflammation Research, Pulmonary Molecular Imaging Group, Edinburgh, EH14 4TJ, UK.
authors.forename: Kevin
authors.initials: K
authors.lastname: Dhaliwal
authors.affiliation: University of Edinburgh, School of Informatics, Edinburgh, EH8 9AB, UK.
authors.forename: Christopher K I
authors.initials: CK
authors.lastname: Williams
mesh_terms: D015331:Cohort Studies; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D000069550:Machine Learning; D008297:Male; D061848:Optical Imaging / Q000379:methods*; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed