The ability to monitor disease progression over time is critical to inform patient care and prognosis, especially in usual interstitial pneumonia (UIP), the histopathological pattern seen in idiopathic pulmonary fibrosis (IPF). HRCT is limited in resolution to detect disease changes on a microscopic level, and surgical lung biopsy (SLB) has high risk of morbidity and mortality precluding its use to assess progression. Endobronchial optical coherence tomography (EB-OCT) is a bronchoscopic, minimally-invasive, high-resolution imaging method that accurately detects microscopic ILD features and is repeatable. Here, we evaluate the utility of repeat EB-OCT for monitoring microscopic disease progression in UIP/IPF.
Idiopathic pulmonary fibrosis (IPF) is a progressive, fatal type of interstitial lung disease (ILD) characterized by abnormal fibrotic scarring of lung parenchyma. We have demonstrated the use of endobronchial optical coherence tomography (EB-OCT) as a minimally-invasive approach for in vivo diagnosis of ILD in patients with high sensitivity and specificity. Here, we investigate the feasibility of EB-OCT elastography to measure the microscopic mechanical properties of normal and fibrotic lung parenchymal tissue in ex vivo porcine lung and in vivo in human subjects with ILD.
Idiopathic pulmonary fibrosis (IPF) is a fatal form of interstitial lung disease (ILD), characterized by abnormal collagen deposition. Computed tomography imaging lacks the resolution to accurately distinguish and quantify fibrosis distribution at the microscopic level, and surgical biopsy methods are invasive. We demonstrate the feasibility of polarization sensitive endobronchial optical coherence tomography (PS EB-OCT) for quantitative in vivo microscopic assessment of fibrotic ILDs. PS EB-OCT was able to accurately distinguish fibrosis distribution patterns in IPF and non-IPF ILDs, independently compared against surgical biopsy. These findings support the potential of PS EB-OCT as a minimally-invasive method for assessment of ILD.
Idiopathic pulmonary fibrosis (IPF) is a fatal form of fibrotic interstitial lung disease (ILD). Early diagnosis of IPF is essential, however, resolution limitations of HRCT prohibit identification and monitoring of early microanatomic alterations. Developing precise imaging biomarkers using quantitative imaging features and artificial intelligence has significant potential for early diagnosis of IPF and non IPF ILDs, as well as for monitoring disease progression and therapeutic response. We demonstrate the feasibility of a deep learning-based algorithm for accurate segmentation and classification of salient microscopic ILD imaging features on endobronchial optical coherence tomography (EB-OCT) imaging.
Inadequacy of tumor tissue in transthoracic core needle biopsy (CNB) and transbronchial biopsy specimens, often due to contamination by fibrosis and normal lung elements, precludes accurate diagnosis, tumor subtyping and molecular testing, and impedes biobanking for research. Frozen section and touch prep methods are not utilized for adequacy assessment because they are destructive and consume tissue. We investigate polarization sensitive optical coherence tomography (PS-OCT), including birefringence and degree of depolarization uniformity metrics, for non-destructive, volumetric quantification of tumor yield in lung CNB specimens. PS-OCT distinguishes tumor, fibrosis and lung parenchyma with high accuracy, demonstrating potential for rapid CNB adequacy assessment.
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