In Journal of biophotonics
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 in-situ 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. This article is protected by copyright. All rights reserved.
Rodriguez-Diaz Eladio, Kaanan Samer, Vanley Christopher, Qureshi Tauseef, Bigio Irving J
Lung, Machine Learning, Robotics, Spectroscopy, Surgical equipment