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Adequate tumor yield in core-needle biopsy (CNB) specimens is essential in lung cancer for accurate histological diagnosis, molecular testing for therapeutic decision-making, and tumor biobanking for research. Insufficient tumor sampling in CNB is common, primarily due to inadvertent sampling of tumor-associated fibrosis or atelectatic lung, leading to repeat procedures and delayed diagnosis. Currently, there is no method for rapid, non-destructive intraprocedural assessment of CNBs. Polarization-sensitive optical coherence tomography (PS-OCT) is a high-resolution, volumetric imaging technique that has the potential to meet this clinical need. PS-OCT detects endogenous tissue properties, including birefringence from collagen, and degree of polarization uniformity (DOPU) indicative of tissue depolarization. Here, PS-OCT birefringence and DOPU measurements were used to quantify the amount of tumor, fibrosis, and normal lung parenchyma in 42 fresh, intact lung CNB specimens. PS-OCT results were compared to and validated against matched histology in a blinded assessment. Linear regression analysis showed strong correlations between PS-OCT and matched histology for quantification of tumors, fibrosis, and normal lung parenchyma in CNBs. PS-OCT distinguished CNBs with low tumor content from those with higher tumor content with high sensitivity and specificity. This study demonstrates the potential of PS-OCT as a method for rapid, non-destructive, label-free intra-procedural tumor yield assessment., Competing Interests: LPH: Boehringer Ingelheim, Pliant Therapeutics, LX Medical, and Biogen Idec. MJS: NinePoint Medical, LX Medical., (© 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.)

We propose a novel suite of algorithms for automatically segmenting the airway lumen and mucus in endobronchial optical coherence tomography (OCT) data sets, as well as a novel approach for quantifying the contents of the mucus. Mucus and lumen were segmented using a robust, multi-stage algorithm that requires only minimal input regarding sheath geometry. The algorithm performance was highly accurate in a wide range of airway and noise conditions. Mucus was classified using mean backscattering intensity and grey level co-occurrence matrix (GLCM) statistics. We evaluated our techniques in vivo in asthmatic and non-asthmatic volunteers.