4.7 Article

Visible-absorption spectroscopy as a biomarker to predict treatment response and prognosis of surgically resected esophageal cancer

Journal

SCIENTIFIC REPORTS
Volume 6, Issue -, Pages -

Publisher

NATURE PUBLISHING GROUP
DOI: 10.1038/srep33414

Keywords

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Funding

  1. Ministry of Science and Technology [NSC99-2627-B002-002, NSC99-2627-B033-003, NSC99-2627-B-239-001]
  2. Ministry of Health and Welfare [DOH101-TD-PB-111-NSC003, DOH101-TD-PB-111-NSC011, DOH101-TD-PB-111-NSC012]
  3. National Research Program for Biopharmaceuticals [101TMP1009-1, 101TMP1009-2, 101TMP1009-3]
  4. Taiwan Health Foundation of Taiwan ROC

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The application of optical absorption spectra in prognostic prediction has hardly been investigated. We developed and evaluated a novel two dimensional absorption spectrum measurement system (TDAS) for use in early diagnosis, evaluating response to chemoradiation, and making prognostic prediction. The absorption spectra of 120 sets of normal and tumor tissues from esophageal cancer patients were analyzed with TDAS ex-vivo. We demonstrated the cancerous tissue, the tissue from patients with a poor concurrent chemoradiotherapy (CCRT) response, and the tissue from patients with an early disease progression each had a readily identifiable common spectral signature. Principal component analysis (PCA) classified tissue spectra into distinct groups, demonstrating the feasibility of using absorption spectra in differentiating normal and tumor tissues, and in predicting CCRT response, poor survival and tumor recurrence (efficiencies of 75%, 100% and 85.7% respectively). Multivariate analysis revealed that patients identified as having poor-response, poor-survival and recurrence spectral signatures were correlated with increased risk of poor response to CCRT (P = 0.012), increased risk of death (P = 0.111) and increased risk of recurrence (P = 0.030) respectively. Our findings suggest that optical absorption microscopy has great potential to be a useful tool for pre-operative diagnosis and prognostic prediction of esophageal cancer.

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