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Lung carcinoma is the most prevalent type of cancer in the world, and it is responsible for more deaths than other types
of cancer. During diagnosis, a pathologist primarily aims to differentiate small cell carcinoma from non-small cell
carcinoma on biopsy and cytology specimens, which is time consuming due to the time required for tissue processing
and staining. To speed up the diagnostic process, we investigated the feasibility of using coherent anti-Stokes Raman
scattering (CARS) microscopy as a label-free strategy to image lung lesions and differentiate subtypes of lung cancers.
Different mouse lung cancer models were developed by injecting human lung cancer cell lines, including
adenocarcinoma, squamous cell carcinoma, and small cell carcinoma, into lungs of the nude mice. CARS images were
acquired from normal lung tissues and different subtypes of cancer lesions ex vivo using intrinsic contrasts from
symmetric CH2 bonds. These images showed good correlation with the hematoxylin and eosin (H&E) stained sections
from the same tissue samples with regard to cell size, density, and cell-cell distance. These features are routinely used in
diagnosing lung lesions. Our results showed that the CARS technique is capable of providing a visualizable platform to
differentiate different kinds of lung cancers using the same pathological features without histological staining and thus
has the potential to serve as a more efficient examination tool for diagnostic pathology. In addition, incorporating with
suitable fiber-optic probes would render the CARS technique as a promising approach for in vivo diagnosis of lung
cancer.
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