Detection and delineation of squamous neoplasia with hyperspectral imaging in a mouse model of tongue carcinogenesis

Guolan Lu, Dongsheng Wang, Xulei Qin, Susan Muller, Xu Wang, Amy Y. Chen, Zhuo Georgia Chen, Baowei Fei

Research output: Contribution to journalArticlepeer-review

27 Scopus citations


Hyperspectral imaging (HSI) holds the potential for the noninvasive detection of cancers. Oral cancers are often diagnosed at a late stage when treatment is less effective and the mortality and morbidity rates are high. Early detection of oral cancer is, therefore, crucial in order to improve the clinical outcomes. To investigate the potential of HSI as a noninvasive diagnostic tool, an animal study was designed to acquire hyperspectral images of in vivo and ex vivo mouse tongues from a chemically induced tongue carcinogenesis model. A variety of machine-learning algorithms, including discriminant analysis, ensemble learning, and support vector machines, were evaluated for tongue neoplasia detection using HSI and were validated by the reconstructed pathological gold-standard maps. The diagnostic performance of HSI, autofluorescence imaging, and fluorescence imaging were compared in this study. Color-coded prediction maps were generated to display the predicted location and distribution of premalignant and malignant lesions. This study suggests that hyperspectral imaging combined with machine-learning techniques can provide a noninvasive tool for the quantitative detection and delineation of squamous neoplasia.

Original languageEnglish (US)
Article numbere201700078
JournalJournal of Biophotonics
Issue number3
StatePublished - Mar 2018
Externally publishedYes


  • 4NQO-induced tongue carcinogenesis model
  • ensemble LDA
  • hyperspectral imaging
  • linear discriminant analysis
  • linear support vector machine
  • random forest
  • squamous neoplasia
  • supervised learning

ASJC Scopus subject areas

  • Chemistry(all)
  • Materials Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Engineering(all)
  • Physics and Astronomy(all)


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