Development and Evaluation of an Automated Multimodal Mobile Detection of Oral Cancer Imaging System to Aid in Risk-Based Management of Oral Mucosal Lesions

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.

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Publication Details

Journal
Cancer Prevention Research
Published
2025-01-16
DOI
https://doi.org/10.1158/1940-6207.capr-24-0253
Citations
6
Primary Topic
Head and Neck Cancer Studies
Type
article
Field-Weighted Citation Impact
11.09
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article

Development and Evaluation of an Automated Multimodal Mobile Detection of Oral Cancer Imaging System to Aid in Risk-Based Management of Oral Mucosal Lesions

Imran S. Vohra, Ann M. Gillenwater, Hawraa Badaoui, Jackson B. Coole et al.
6 citations
Cancer Prevention Research
Head and Neck Cancer Studies
11.09
article

Development and Evaluation of an Automated Multimodal Mobile Detection of Oral Cancer Imaging System to Aid in Risk-Based Management of Oral Mucosal Lesions

Imran S. Vohra, Ann M. Gillenwater, Hawraa Badaoui, Jackson B. Coole, Jennifer Carns, Alex Kortum, Richard A. Schwarz, Nancy Bass, Rachel Giese, David Brenes, Rebecca Richards‐Kortum, Nadarajah Vigneswaran, Ruchika Mitbander, Safia Durab, Ida Varghese, Anthony B. Clayton, Sibyl Anderson, Loganayaki Anandasivam
article en
6 citations

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.

Cancer Prevention ResearchVol. 18(4)
The University of Texas MD Anderson Cancer Center (US), The University of Texas at San Antonio Health Science Center (US), Brownsville Public Library (US), Community Health Center (US), Community Development Corporation of Brownsville (US), Rice University (US), The University of Texas Health Science Center at Houston (US)
Openalex Percentile: Top 2%
Head and Neck Cancer Studies
11.09
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