BACKGROUND: There is an important global need to improve early detection of oral cancer. Recent reports suggest that optical imaging technologies can aid in the identification of neoplastic lesions in the oral cavity; however, there is little data evaluating the use of optical imaging modalities in resource limited settings where oral cancer impacts patients disproportionately. In this article, we evaluate a simple, low-cost optical imaging system that is designed for early detection of oral cancer in resource limited settings. We report results of a clinical study conducted at Tata Memorial Hospital (TMH) in Mumbai, India using this system as a tool to improve detection of oral cancer and its precursors. METHODS: Reflectance images with white light illumination and fluorescence images with 455 nm excitation were obtained from 261 sites in the oral cavity from 76 patients and 90 sites in the oral cavity from 33 normal volunteers. Quantitative image features were used to develop classification algorithms to identify neoplastic tissue, using clinical diagnosis of expert observers as the gold standard. RESULTS: Using the ratio of red to green autofluorescence, the algorithm identified tissues judged clinically to be cancer or clinically suspicious for neoplasia with a sensitivity of 90% and a specificity of 87%. CONCLUSIONS: Results suggest that the performance of this simple, objective low-cost system has potential to improve oral screening efforts, especially in low-resource settings.
BACKGROUND: There is an important global need to improve early detection of oral cancer. Recent reports suggest that optical imaging technologies can aid in the identification of neoplastic lesions in the oral cavity; however, there is little data evaluating the use of optical imaging modalities in resource limited settings where oral cancer impacts patients disproportionately. In this article, we evaluate a simple, low-cost optical imaging system that is designed for early detection of oral cancer in resource limited settings. We report results of a clinical study conducted at Tata Memorial Hospital (TMH) in Mumbai, India using this system as a tool to improve detection of oral cancer and its precursors. METHODS: Reflectance images with white light illumination and fluorescence images with 455 nm excitation were obtained from 261 sites in the oral cavity from 76 patients and 90 sites in the oral cavity from 33 normal volunteers. Quantitative image features were used to develop classification algorithms to identify neoplastic tissue, using clinical diagnosis of expert observers as the gold standard. RESULTS: Using the ratio of red to green autofluorescence, the algorithm identified tissues judged clinically to be cancer or clinically suspicious for neoplasia with a sensitivity of 90% and a specificity of 87%. CONCLUSIONS: Results suggest that the performance of this simple, objective low-cost system has potential to improve oral screening efforts, especially in low-resource settings.
Authors: Larissa Sweeny; Nichole R Dean; J Scott Magnuson; William R Carroll; Lisa Clemons; Eben L Rosenthal Journal: Otolaryngol Head Neck Surg Date: 2011-07-29 Impact factor: 3.497
Authors: Darren Roblyer; Cristina Kurachi; Vanda Stepanek; Richard A Schwarz; Michelle D Williams; Adel K El-Naggar; J Jack Lee; Ann M Gillenwater; Rebecca Richards-Kortum Journal: J Biomed Opt Date: 2010 Nov-Dec Impact factor: 3.170
Authors: Denise M Laronde; P M Williams; T G Hislop; Catherine Poh; Samson Ng; Chris Bajdik; Lewei Zhang; Calum MacAulay; Miriam P Rosin Journal: J Oral Pathol Med Date: 2013-06-10 Impact factor: 4.253