On-the-spot lung cancer differential diagnosis by label-free, molecular vibrational imaging and knowledge-based classification

Liang Gao, Fuhai Li, Michael J. Thrall, Yaliang Yang, Jiong Xing, Ahmad A. Hammoudi, Hong Zhao, Yehia Massoud, Philip T. Cagle, Yubo Fan, Kelvin K. Wong, Zhiyong Wang, Stephen T.C. Wong

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article number096004
JournalJournal of biomedical optics
Issue number9
StatePublished - Sep 2011


  • coherent anti-Stokes Raman scattering microscopy
  • differential diagnosis
  • lung cancer
  • machine learning

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Biomedical Engineering


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