Tze-Ta Huang1, Jehn-Shyun Huang1, Yen-Yun Wang2, Ken-Chung Chen1, Tung-Yiu Wong1, Yi-Chun Chen3, Che-Wei Wu4, Leong-Perng Chan5, Yi-Chu Lin4, Yu-Hsun Kao6, Shoko Nioka7, Shyng-Shiou F Yuan8, Pau-Choo Chung9. 1. Division of Oral and Maxillofacial Surgery, Department of Stomatology, National Cheng-Kung University Medical College and Hospital, Tainan, Taiwan; Institute of Oral Medicine, National Cheng-Kung University Medical College and Hospital, Tainan, Taiwan. 2. Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan; Translational Research Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan. 3. Institute of Oral Medicine, National Cheng-Kung University Medical College and Hospital, Tainan, Taiwan. 4. Department of Otorhinolaryngology-Head and Neck Surgery, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan; Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan. 5. Department of Otorhinolaryngology-Head and Neck Surgery, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan. 6. Department of Oral and Maxillofacial Surgery, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan. 7. Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA. 8. Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan; Translational Research Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan; Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan; Department of Obstetrics and Gynecology and Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan. Electronic address: yuanssf@ms33.hinet.net. 9. Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan. Electronic address: pcchung@eembox.ee.ncku.edu.tw.
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.
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.
Authors: Carlo Lajolo; Mariateresa Tranfa; Romeo Patini; Antonino Fiorino; Teresa Musarra; Roberto Boniello; Alessandro Moro Journal: Int J Environ Res Public Health Date: 2022-05-04 Impact factor: 4.614
Authors: Marco Cicciù; Gabriele Cervino; Luca Fiorillo; Cesare D'Amico; Giacomo Oteri; Giuseppe Troiano; Khrystyna Zhurakivska; Lorenzo Lo Muzio; Alan Scott Herford; Salvatore Crimi; Alberto Bianchi; Dario Di Stasio; Rosario Rullo; Gregorio Laino; Luigi Laino Journal: Dent J (Basel) Date: 2019-09-04