Quantitative label-free multimodality nonlinear optical imaging for in situ differentiation of cancerous lesions

Xiaoyun Xu; Xiaoyan Li; Jie Cheng; Zhengfan Liu; Michael J. Thrall; Xi Wang; Zhiyong Wang; Stephen T. C. Wong

doi:10.1117/12.2002633

8 March 2013 Quantitative label-free multimodality nonlinear optical imaging for in situ differentiation of cancerous lesions

Xiaoyun Xu, Xiaoyan Li, Jie Cheng, Zhengfan Liu, Michael J. Thrall, Xi Wang, Zhiyong Wang, Stephen T. C. Wong

Author Affiliations +

Proceedings Volume 8565, Photonic Therapeutics and Diagnostics IX; 85653A (2013) https://doi.org/10.1117/12.2002633
Event: SPIE BiOS, 2013, San Francisco, California, United States

Abstract

The development of real-time, label-free imaging techniques has recently attracted research interest for in situ differentiation of cancerous lesions from normal tissues. Molecule-specific intrinsic contrast can arise from label-free imaging techniques such as Coherent Anti-Stokes Raman Scattering (CARS), Two-Photon Excited AutoFluorescence (TPEAF), and Second Harmonic Generation (SHG), which, in combination, would hold the promise of a powerful label-free tool for cancer diagnosis. Among cancer-related deaths, lung carcinoma is the leading cause for both sexes. Although early treatment can increase the survival rate dramatically, lesion detection and precise diagnosis at an early stage is unusual due to its asymptomatic nature and limitations of current diagnostic techniques that make screening difficult. We investigated the potential of using multimodality nonlinear optical microscopy that incorporates CARS, TPEAF, and SHG techniques for differentiation of lung cancer from normal tissue. Cancerous and non-cancerous lung tissue samples from patients were imaged using CARS, TPEAF, and SHG techniques for comparison. These images showed good pathology correlation with hematoxylin and eosin (H and E) stained sections from the same tissue samples. Ongoing work includes imaging at various penetration depths to show three-dimensional morphologies of tumor cell nuclei using CARS, elastin using TPEAF, and collagen using SHG and developing classification algorithms for quantitative feature extraction to enable lung cancer diagnosis. Our results indicate that via real-time morphology analyses, a multimodality nonlinear optical imaging platform potentially offers a powerful minimally-invasive way to differentiate cancer lesions from surrounding non-tumor tissues in vivo for clinical applications.

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Xiaoyun Xu, Xiaoyan Li, Jie Cheng, Zhengfan Liu, Michael J. Thrall, Xi Wang, Zhiyong Wang, and Stephen T. C. Wong "Quantitative label-free multimodality nonlinear optical imaging for in situ differentiation of cancerous lesions", Proc. SPIE 8565, Photonic Therapeutics and Diagnostics IX, 85653A (8 March 2013); https://doi.org/10.1117/12.2002633

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