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Detection and delineation of squamous neoplasia with hyperspectral imaging in a mouse model of tongue carcinogenesis
Author(s) -
Lu Guolan,
Wang Dongsheng,
Qin Xulei,
Muller Susan,
Wang Xu,
Chen Amy Y.,
Chen Zhuo Georgia,
Fei Baowei
Publication year - 2018
Publication title -
journal of biophotonics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.877
H-Index - 66
eISSN - 1864-0648
pISSN - 1864-063X
DOI - 10.1002/jbio.201700078
Subject(s) - hyperspectral imaging , tongue , autofluorescence , cancer imaging , cancer detection , cancer , gold standard (test) , medical imaging , carcinogenesis , artificial intelligence , medicine , pathology , radiology , computer science , fluorescence , physics , quantum mechanics
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.