We have previously demonstrated multimodal optical coherence tomography and autofluorescence imaging (OCT-AFI) in the distal airways of the lung. To combine the two modalities into a single-fiber endoscope, we use double-clad fibers, which causes additional blurred OCT images from the fibers' higher-order modes. Recently, we established multipath contrast imaging (MCI) which leverages these higher-order images to elucidate angular backscattering of tissue. MCI can be generated retroactively; we seek to re-evaluate images from our in vivo OCT-AFI lung cancer study. Early MCI results demonstrate high contrast in healthy tissue compared to blood, and for a histologically confirmed adenocarcinoma.
KEYWORDS: Biopsy, Optical coherence tomography, Tissues, Lung, In vivo imaging, Auto-fluorescence imaging, Diagnostics, Yield improvement, Visualization, Real time imaging
Significance: Diagnosis of suspicious lung nodules requires precise collection of relevant biopsies for histopathological analysis. Using optical coherence tomography and autofluorescence imaging (OCT-AFI) to improve diagnostic yield in parts of the lung inaccessible to larger imaging methods may allow for reducing complications related to the alternative of computed tomography-guided biopsy.
Aim: Feasibility of OCT-AFI combined with a commercially available lung biopsy needle was demonstrated for visualization of needle puncture sites in airways with diameters as small as 1.9 mm.
Approach: A miniaturized OCT-AFI imaging stylet was developed to be inserted through an 18G biopsy needle. We present design considerations and procedure development for image-guided biopsy. Ex vivo and in vivo porcine studies were performed to demonstrate the feasibility of the procedure and the device.
Results: OCT-AFI scans were obtained ex vivo and in vivo. Discrimination of pullback site is clear.
Conclusions: Use of the device is shown to be feasible in vivo. Images obtained show the stylet is effective at providing structural information at the puncture site that can be used to assess the diagnostic potential of the sample prior to collection.
Lung Cancer screening trials have demonstrated significant mortality reduction. Low-Dose Computed Tomography (LDCT) screening can frequently discover many small nodules in at risk participants. However classification of these, sub-cm nodules as cancerous or benign is a challenging task even for expert clinicians.
We use machine learning (ML) and deep learning (CNN) techniques to differentiate, sub-cm cancerous and benign nodules. Data for this study is drawn from a screening study (PanCan) from which we selected 612 distinct nodules (140 cancerous, and ~size matched 472 benign). Both methods demonstrated a ~80% accuracy, whereas currently used measures (size) had a 68% accuracy.
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