Novel quantitative analysis of autofluorescence images for oral cancer screening. (May 2017)

• Record Type: Journal Article
• Authors: Huang, Tze-Ta; Huang, Jehn-Shyun; Wang, Yen-Yun; Chen, Ken-Chung; Wong, Tung-Yiu; Chen, Yi-Chun; Wu, Che-Wei; Chan, Leong-Perng; Lin, Yi-Chu; Kao, Yu-Hsun; Nioka, Shoko; Yuan, Shyng-Shiou F.; Chung, Pau-Choo
• Is Part Of: Oral oncology. Volume 68(2017)
• Page Start: 20
• Page End: 26
• Publication Date: 2017-05
• Subjects: Autofluorescence -- VELscope® -- Quantitative analysis -- Oral cancer; Mouth -- Cancer -- Periodicals; Mouth -- Tumors -- Periodicals; Mouth Diseases -- Periodicals; Mouth Neoplasms -- Periodicals; Bouche -- Cancer -- Périodiques; Bouche -- Tumeurs -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9943105
• DOI: 10.1016/j.oraloncology.2017.03.003
• Languages: English
• ISSNs: 1368-8375

Abstract

Highlights: Developed quantitative analysis of autofluorescence images for oral cancer screen. Quadratic discriminant analysis was utilized to compute a boundary. Oral cancer and precancer lesions can be discriminated from normal oral mucosa. Abstract: Objectives: VELscope® was developed to inspect oral mucosa autofluorescence. However, its accuracy is heavily dependent on the examining physician's experience. This study was aimed toward the development of a novel quantitative analysis of autofluorescence images for oral cancer screening. Materials and methods: Patients with either oral cancer or precancerous lesions and a control group with normal oral mucosa were enrolled in this study. White light images and VELscope® autofluorescence images of the lesions were taken with a digital camera. The lesion in the image was chosen as the region of interest (ROI). The average intensity and heterogeneity of the ROI were calculated. A quadratic discriminant analysis (QDA) was utilized to compute boundaries based on sensitivity and specificity. Results: 47 oral cancer lesions, 54 precancerous lesions, and 39 normal oral mucosae controls were analyzed. A boundary of specificity of 0.923 and a sensitivity of 0.979 between the oral cancer lesions and normal oral mucosae were validated. The oral cancer and precancerous lesions could also be differentiated from normal oral mucosae with a specificity of 0.923 and a sensitivity of 0.970. Conclusion: The novel quantitative analysis of the intensity and heterogeneity of VELscope® autofluorescence images used in this study in combination with a QDA classifier can be used to differentiate oral cancer and precancerous lesions from normal oral mucosae.