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The lung is one of the most common sites of metastases, with approximately 50% of patients with extrathoracic cancer exhibiting pulmonary metastases. Correct identification of the metastatic status of a lung lesion is vital to therapeutic planning and better prognosis. However, currently available diagnostic techniques, such as conventional radiography and low dose computed tomography (LDCT), may fail to identify metastatic lesions. Alternative techniques such as Raman spectroscopy (RS) are hence being extensively explored for correct diagnosis of metastasis. The current ex vivo study aims to evaluate the ability of a fiber optic-based Raman system to distinguish breast cancer metastasis in lung from primary breast and lung tumor in animal models. In this study, spectra were acquired from normal breast, primary breast tumor, normal lung, primary lung tumor, and breast cancer metastasis in lung tissues and analyzed using principal component analysis and principal component-linear discriminant analysis. Breast cancer metastasis in lung could be classified with 71% classification efficiency. Approximately 6% breast metastasis spectra were misclassified with breast tumor, probably due to the presence of breast cancer cells in metastasized lungs. Test prediction results show 64% correct prediction of breast metastasis, while 13% breast metastasis spectra were wrongly predicted as breast tumor, suggesting the possible influence of breast cancer cells. Thus, findings of this study, the first of such explorations, demonstrate the potential of RS in classifying breast metastasis in lungs from primary lung and primary breast tumor. Prospective evaluation on a larger cohort with better multivariate analysis, combined with LDCT and recently developed real-time in vivo probes, RS can play a significant role in nonsurgical screening of lesions, which can lead to individualized therapeutic regimes and improved prognoses.
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