BACKGROUND: We report the results of a clinical in vivo study to evaluate the potential of fluorescence spectroscopy for differential diagnosis of oral mucosal malignant and potentially malignant lesions. MATERIALS AND METHODS: The study involved 26 healthy volunteers and 144 patients enrolled for routine medical examination of the oral cavity at the outpatient department of the Tata Memorial Hospital, Mumbai. In vivo autofluorescence spectra were recorded using a N2 laser based portable fluorimeter developed in-house. The different tissue sites investigated belonged to either of the four histopathologic categories: 1) squamous cell carcinoma (SCC), 2) oral sub-mucous fibrosis (OSMF), 3) leukoplakia (LP) and 4) normal squamous tissue. A multivariate statistical algorithm capable of direct multi-class classification was used to predict pathological designations. RESULTS: With respect to histopathology as the "gold standard", the diagnostic algorithm was found to provide an accuracy of 82, 76, 81 and 85% based on leave-one-patient-out cross-validation in classifying the oral tissue spectra into four different pathology classes - SCC, OSMF, LP, and normal squamous tissue - respectively. When the algorithm was employed for delineating the normal oral tissues from all the abnormal oral tissues including SCC, OSMF and LP put together, a sensitivity of 98% and a specificity of 100% were obtained. CONCLUSION: The results suggest that it is possible to objectively classify the oral tissue into different pathology classes based on their in vivo autofluorescence spectra. Thus, the technique can potentially improve oral screening efforts in low resource settings where clinical expertise and resources are limited.
BACKGROUND: We report the results of a clinical in vivo study to evaluate the potential of fluorescence spectroscopy for differential diagnosis of oral mucosal malignant and potentially malignant lesions. MATERIALS AND METHODS: The study involved 26 healthy volunteers and 144 patients enrolled for routine medical examination of the oral cavity at the outpatient department of the Tata Memorial Hospital, Mumbai. In vivo autofluorescence spectra were recorded using a N2 laser based portable fluorimeter developed in-house. The different tissue sites investigated belonged to either of the four histopathologic categories: 1) squamous cell carcinoma (SCC), 2) oral sub-mucous fibrosis (OSMF), 3) leukoplakia (LP) and 4) normal squamous tissue. A multivariate statistical algorithm capable of direct multi-class classification was used to predict pathological designations. RESULTS: With respect to histopathology as the "gold standard", the diagnostic algorithm was found to provide an accuracy of 82, 76, 81 and 85% based on leave-one-patient-out cross-validation in classifying the oral tissue spectra into four different pathology classes - SCC, OSMF, LP, and normal squamous tissue - respectively. When the algorithm was employed for delineating the normal oral tissues from all the abnormal oral tissues including SCC, OSMF and LP put together, a sensitivity of 98% and a specificity of 100% were obtained. CONCLUSION: The results suggest that it is possible to objectively classify the oral tissue into different pathology classes based on their in vivo autofluorescence spectra. Thus, the technique can potentially improve oral screening efforts in low resource settings where clinical expertise and resources are limited.
Authors: Jonas Wizenty; Teresa Schumann; Donna Theil; Martin Stockmann; Johann Pratschke; Frank Tacke; Felix Aigner; Tilo Wuensch Journal: Molecules Date: 2020-04-30 Impact factor: 4.411