title: Automated classification of optical coherence tomography images for the diagnosis of oral malignancy in the hamster cheek pouch.
abstract: Most studies evaluating the potential of optical coherence tomography (OCT) for the diagnosis of oral cancer are based on visual assessment of OCT B-scans by trained experts. Human interpretation of the large pool of data acquired by modern high-speed OCT systems, however, can be cumbersome and extremely time consuming. Development of image analysis methods for automated and quantitative OCT image analysis could therefore facilitate the evaluation of such a large volume of data. We report automated algorithms for quantifying structural features that are associated with the malignant transformation of the oral epithelium based on image processing of OCT data. The features extracted from the OCT images were used to design a statistical classification model to perform the automated tissue diagnosis. The sensitivity and specificity of distinguishing malignant lesions from benign lesions were found to be 90.2% and 76.3%, respectively. The results of the study demonstrate the feasibility of using quantitative image analysis algorithms for extracting morphological features from OCT images to perform the automated diagnosis of oral malignancies in a hamster cheek pouch model.
authors.affiliation: Texas A&M University, Department of Biomedical Engineering, College Station, Texas 77843, United States.
authors.forename: Paritosh
authors.initials: P
authors.lastname: Pande
authors.affiliation: Texas A&M University, Department of Biomedical Engineering, College Station, Texas 77843, United States.
authors.forename: Sebina
authors.initials: S
authors.lastname: Shrestha
authors.affiliation: Texas A&M University, Department of Biomedical Engineering, College Station, Texas 77843, United States.
authors.forename: Jesung
authors.initials: J
authors.lastname: Park
authors.affiliation: Texas A&M University, Department of Biomedical Engineering, College Station, Texas 77843, United States.
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Serafino
authors.affiliation: University of Texas M.D. Anderson Cancer Center, Department of Carcinogenesis, Smithville, Texas 78957, United States.
authors.forename: Irma
authors.initials: I
authors.lastname: Gimenez-Conti
authors.affiliation: University of Texas M.D. Anderson Cancer Center, Department of Carcinogenesis, Smithville, Texas 78957, United States.
authors.forename: Jimi
authors.initials: J
authors.lastname: Brandon
authors.affiliation: Texas A&M University Health Science Center-Baylor College of Dentistry, Department of Diagnostic Sciences, 3302 Gaston Avenue, Dallas, Texas 75246, United States.
authors.forename: Yi-Shing
authors.initials: YS
authors.lastname: Cheng
authors.affiliation: Texas A&M University, Department of Biomedical Engineering, College Station, Texas 77843, United States.
authors.forename: Brian E
authors.initials: BE
authors.lastname: Applegate
authors.affiliation: Texas A&M University, Department of Biomedical Engineering, College Station, Texas 77843, United States.
authors.forename: Javier A
authors.initials: JA
authors.lastname: Jo
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D002610:Cheek; D006224:Cricetinae; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008647:Mesocricetus; D009061:Mouth Mucosa / Q000473:pathology*; D009062:Mouth Neoplasms / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D041623:Tomography, Optical Coherence / Q000379:methods*