Toward optical spectroscopy‐guided lung biopsy: Demonstration of tissue‐type classification

• Published in: Journal of biophotonics 2021-10, Vol.14 (10), p.e202100132-n/a
• Main Authors: Rodriguez‐Diaz, Eladio, Kaanan, Samer, Vanley, Christopher, Qureshi, Tauseef, Bigio, Irving J
• Format: Article
• Language: English
• Subjects: Alveoli; Biopsy; Bronchi; Bronchoscopy; Bronchus; Elastic scattering; Intelligence; Learning algorithms; Lesions; lung; Lung cancer; Lungs; Machine learning; Pulmonary lesions; Radiology; robotics; Sampling; Spectroscopy; Spectrum analysis; surgical equipment; Surgical instruments; Tissues
• ISSN: 1864-063X; 1864-0648
• DOI: 10.1002/jbio.202100132

The diagnostic yield of standard tissue‐sampling modalities of suspected lung cancers, whether by bronchoscopy or interventional radiology, can be nonoptimal, varying with the size and location of lesions. What is needed is an insitu sensor, integrated in the biopsy tool, to objectively distinguish among tissue types in real time, not to replace biopsy with an optical diagnostic, but to verify that the sampling tool is properly located within the target lesion. We investigated the feasibility of elastic scattering spectroscopy (ESS), coupled with machine learning, to distinguish lung lesions from the various nearby tissue types, in a study with freshly‐excised lung tissues from surgical resections. Optical spectra were recorded with an ESS fiberoptic probe in different areas of the resected pulmonary tissues, including benign‐margin tissue sites as well as the periphery and core of the lesion. An artificial‐intelligence model was used to analyze, retrospectively, 2032 measurements from excised tissues of 35 patients. With high accuracy, ESS was able to distinguish alveolar tissue from bronchi, alveolar tissue from lesions, and bronchi from lesions. This ex vivo study indicates promise for ESS fiberoptic probes to be integrated with surgical intervention tools, to improve reliability of pulmonary lesion targeting. We investigated the feasibility of elastic scattering spectroscopy (ESS), coupled with machine learning, to distinguish lung lesions from the various nearby tissue types. Optical spectra were recorded with an ESS fiberoptic probe in different areas of freshly‐excised lung tissues from surgical resections. A machine learning model was used to analyze, retrospectively, 2032 measurements from excised tissues of 35 patients. With high accuracy, ESS was able to distinguish alveolar tissue from bronchi, alveolar tissue from lesions, and bronchi from lesions.