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Journal of Biomedical Optics • Open Access

On-the-spot lung cancer differential diagnosis by label-free, molecular vibrational imaging and knowledge-based classification
Author(s): Liang S. Gao; Fuhai Li; Yaliang Yang; Jiong Xing; Ahmad A. Hammoudi; Hong Zhao; Yubo Fan; Kelvin K. Wong; Zhiyong Wang; Stephen T. Wong; Michael J. Thrall; Philip T. Cagle; Yehia Massoud

Paper Abstract

We report the development and application of a knowledge-based coherent anti-Stokes Raman scattering (CARS) microscopy system for label-free imaging, pattern recognition, and classification of cells and tissue structures for differentiating lung cancer from non-neoplastic lung tissues and identifying lung cancer subtypes. A total of 1014 CARS images were acquired from 92 fresh frozen lung tissue samples. The established pathological workup and diagnostic cellular were used as prior knowledge for establishment of a knowledge-based CARS system using a machine learning approach. This system functions to separate normal, non-neoplastic, and subtypes of lung cancer tissues based on extracted quantitative features describing fibrils and cell morphology. The knowledge-based CARS system showed the ability to distinguish lung cancer from normal and non-neoplastic lung tissue with 91% sensitivity and 92% specificity. Small cell carcinomas were distinguished from nonsmall cell carcinomas with 100% sensitivity and specificity. As an adjunct to submitting tissue samples to routine pathology, our novel system recognizes the patterns of fibril and cell morphology, enabling medical practitioners to perform differential diagnosis of lung lesions in mere minutes. The demonstration of the strategy is also a necessary step toward in vivo point-of-care diagnosis of precancerous and cancerous lung lesions with a fiber-based CARS microendoscope.

Paper Details

Date Published: 1 September 2011
PDF: 11 pages
J. Biomed. Opt. 16(9) 096004 doi: 10.1117/1.3619294
Published in: Journal of Biomedical Optics Volume 16, Issue 9
Show Author Affiliations
Liang S. Gao, Rice Univ. (United States)
Fuhai Li, Methodist Hospital Research Institute (United States)
Yaliang Yang, Methodist Hospital Research Institute (United States)
Jiong Xing, Methodist Hospital Research Institute (United States)
Ahmad A. Hammoudi, Methodist Hospital Research Institute (United States)
Hong Zhao, Methodist Hospital Research Institute (United States)
Yubo Fan, Methodist Hospital Research Institute (United States)
Kelvin K. Wong, Methodist Hospital Research Institute (United States)
Zhiyong Wang, Methodist Hospital Research Institute (United States)
Stephen T. Wong, Methodist Hospital Research Institute (United States)
Michael J. Thrall, Methodist Hospital Research Institute (United States)
Philip T. Cagle, Methodist Hospital Research Institute (United States)
Yehia Massoud, Rice Univ. (United States)


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