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.

Image computing for fibre-bundle endomicroscopy: A review

“…(Leonovych et al, 2018;Perperidis et al, 2016) Cancerous nodules in airways and distal lung. (He et al, 2012;Rakotomamonjy et al, 2014;Seth et al, 2016)…”

“…For organs/structures that do not exhibit any auto-fluorescence at the imaging wavelengths, fluorescence dyes such as methylene blue and fluorescein, and molecular probes (He et al, 2012) were employed to generate the necessary fluorescent signal. Other less adopted descriptors employed as discriminative features include (i) spatial frequency based features extracted at Fourier domain (Srivastava et al, 2005;Srivastava et al, 2008), (ii) fractal analysis (Ştefănescu et al, 2016), (iii) Scattering transform (Rakotomamonjy et al, 2014;Seth et al, 2016), (iv) Fast Retina Keypoint (FREAK) (Wan et al, 2015), (v)…”

“…Feature spaces combining two or more of the above descriptors are also frequent, with descriptors customarily extracted from the whole image, yet in some cases, regular or randomly distributed sub-windows/patches have been used, either on their own, or in conjunction to the whole image feature space. A number of well-established classifiers have been assessed, including (i) k-Nearest Neighbours (kNN) (André et al, 2012b;Desir et al, 2010;Hebert et al, 2012;Saint-Réquier et al, 2009;Srivastava et al, 2005;Srivastava et al, 2008), (ii) Linear and Quadratic Discriminant Analysis (LDA and QDA) (Leonovych et al, 2018;Srivastava et al, 2005;Srivastava et al, 2008), (iii) Support Vector Machines (SVM) and their adaptation with Recursive Feature Elimination (SVM-RFE) (Desir et al, 2010;Desir et al, 2012b;Jaremenko et al, 2015;Leonovych et al, 2018;Petitjean et al, 2009;Rakotomamonjy et al, 2014;Saint-Réquier et al, 2009;Vo et al, 2017;Wan et al, 2015;Zubiolo et al, 2014), (iv) Random Forests (RF) and variants such as Extremely Randomised Trees (ET) (Desir et al, 2012a;Heutte et al, 2016;Jaremenko et al, 2015;Leonovych et al, 2018;Seth et al, 2016;Vo et al, 2017), (v) Gaussian Mixture Models (GMM) (He et al, 2012;Perperidis et al, 2016), (vi) Boosted Cascade of Classifiers (Hebert et al, 2012), (vii) Neural Networks (NN) (Ştefănescu et al, 2016), (viii) Gaussian Processes Classifiers (GPC), and (ix) Lasso Generalised Linear Models (GLM) (Seth et al, 2016). Most studies employed leave-k-out and k-fold cross validation to assess the predictive capacity of the proposed methodology on limited, pre-annotated frames.…”

Estimating Bacterial and Cellular Load in FCFM Imaging

“…FCFM imaging and the autofluorescence of the human lung have been used to explore 'abnormalities' without the need of invasive approaches. For example, Seth et al, attempt to predict the malignancy of solitary pulmonary nodules from FCFM images [1]. Yserbyt et al, use FCFM to show that the presence of cells, which autofluoresce as well, can be a sign of acute cellular rejection (ACR) [6] (Yserbyt et al, estimate the cellular load in FCFM images manually [6]).…”

“…Fibered confocal fluorescence microscopy (FCFM) is a popular method for in vivo imaging of the distal lung where a fiber optic bundle (with thousands of cores) is inserted through a bronchoscope, and guided to the distal lung due to its small diameter (less than 1 mm) [1]. FCFM imaging works by recording the number of emitted photons at each core of the optical fiber bundle, and later translating these values into pixel intensities to achieve a 'smooth' image.…”