Development and Evaluation of an Automated Multimodal Mobile Detection of Oral Cancer (mDOC) Imaging System to Aid in Risk-based Management of Oral Mucosal Lesions.

Abstract

Oral cancer is a major global health problem. It is commonly diagnosed at an advanced stage although often preceded by clinically visible oral mucosal lesions, termed oral potentially malignant disorders associated with an increased risk for oral cancer development. There is an unmet clinical need for effective screening tools to assist front-line healthcare providers to determine which patients should be referred to an oral cancer specialist for evaluation. This study reports the development and evaluation of the mobile Detection of Oral Cancer (mDOC) imaging system and an automated algorithm that generates a referral recommendation from mDOC images. mDOC is a smartphone-based autofluorescence and white light imaging tool that captures images of the oral cavity. Data were collected with mDOC from a total of 332 oral sites in a study of 29 healthy volunteers and 120 patients seeking care for an oral mucosal lesion. A multimodal image classification algorithm was developed to generate a recommendation of "Refer" or "Do Not Refer" from mDOC images, using expert clinical referral decision as the ground truth label. A referral algorithm was developed using cross-validation methods on 80% of the dataset, then retrained and evaluated on a separate holdout test set. Referral decisions generated in the holdout test set had a sensitivity of 93.9% and a specificity of 79.3% with respect to expert clinical referral decisions. The mDOC system has the potential to be utilized in community physicians' and dentists' offices to help identify patients who need further evaluation by an oral cancer specialist.