Quantitative diagnosis of tongue cancer from histological images in an animal model

Guolan Lu, Xulei Qin, Dongsheng Wang, Susan Muller, Hongzheng Zhang, Amy Chen, Zhuo G. Chen, Baowei Fei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

We developed a chemically-induced oral cancer animal model and a computer aided method for tongue cancer diagnosis. The animal model allows us to monitor the progress of the lesions over time. Tongue tissue dissected from mice was sent for histological processing. Representative areas of hematoxylin and eosin stained tissue from tongue sections were captured for classifying tumor and non-Tumor tissue. The image set used in this paper consisted of 214 color images (114 tumor and 100 normal tissue samples). A total of 738 color, texture, morphometry and topology features were extracted from the histological images. The combination of image features from epithelium tissue and its constituent nuclei and cytoplasm has been demonstrated to improve the classification results. With ten iteration nested cross validation, the method achieved an average sensitivity of 96.5% and a specificity of 99% for tongue cancer detection. The next step of this research is to apply this approach to human tissue for computer aided diagnosis of tongue cancer.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2016
Subtitle of host publicationDigital Pathology
EditorsAnant Madabhushi, Metin N. Gurcan
PublisherSPIE
ISBN (Electronic)9781510600263
DOIs
StatePublished - 2016
Event4th Medical Imaging 2016: Digital Pathology - San Diego, United States
Duration: Mar 2 2016Mar 3 2016

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume9791
ISSN (Print)1605-7422

Conference

Conference4th Medical Imaging 2016: Digital Pathology
CountryUnited States
CitySan Diego
Period3/2/163/3/16

Keywords

  • 4NQO-induced oral cancer
  • Tongue cancer diagnosis
  • computer aided diagnosis
  • histological image classification
  • random forest
  • squamous cell carcinoma

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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