Development and Evaluation of an Automated Multimodal Mobile Detection of Oral Cancer 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, which are associated with an increased risk of 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 using 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 and 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. Prevention Relevance: Our research focuses on improving the early detection of oral precancers/cancers in primary dental care settings with a novel mobile platform that can be used by front-line providers to aid in assessing whether a patient has an oral mucosal condition that requires further follow-up with an oral cancer specialist.

Figure 1.

mDOC imaging system. A, Prototype mDOC device. B, Diagram of mDOC system and components. C, Image of United States Air Force resolution target taken with mDOC in WL imaging mode. Group 3 element 4 is resolved (see inset), indicating transverse resolution of 44 μm. D, Screenshots from the custom mDOC Android application that guides the clinician through the data collection process.

Figure 2.

AUC-ROC results for the WL, AF, and MM datasets using image inputs. AUC-ROC results on the final training set and performance on the holdout test set are shown. The best performing model on the holdout test set was the multimodal model. MM: multimodal.

Figure 3.

ROC curve and scatterplot distribution for the best performing model trained on the MM dataset. A, ROC curve of validation on the holdout test set with the optimal Se/Sp called out. B, mDOC probability of referral distribution categorized by expert referral decision on the holdout test set. Error bars indicate the 95% CI. CI, confidence interval; MM, Multimodal; Se, sensitivity; Sp, specificity. *Refer for reasons other than suspicion of precancer/cancer.

Figure 4.

Representative mDOC images from the holdout test dataset. Left to right: WL image; AF image; Grad-CAM++ attention map overlaid on the original WL image. A, Clinically normal lateral tongue. Expert referral decision: Do not refer. mDOC referral decision: Do not refer (score = 0.02). B, Lichen planus on the left buccal mucosa. Expert referral decision: Refer for reasons other than suspicion of precancer/cancer. mDOC referral decision: Refer (score = 0.95). C, Proliferative verrucous leukoplakia on the left buccal mucosa. Expert referral decision: Refer for suspicion of precancer/cancer. mDOC referral decision: Refer (score = 0.64). *Note: the attention map indicates the relative contribution of areas of the image to the mDOC automated prediction.