volume 18 issue 4 pages OF1-OF11

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

Ruchika Mitbander 1, 2
David R. Brenes 1, 2
Jackson B Coole 1, 2
Alex Kortum 1, 2
Imran S. Vohra 2, 3
Jennifer Carns 1, 2
Richard A. Schwarz 2, 3
Ida Varghese 4, 5
Safia Durab 4, 5
Sean Anderson 4, 5
Nancy E. Bass 4, 5
Ashlee D. Clayton 1, 2
Hawraa Badaoui 6, 7
Loganayaki Anandasivam 8, 9
Rachel Giese 10, 11
Ann Gillenwater 6, 7
N Vigneswaran 5, 12
Rebecca R. Richards-Kortum 2, 13
Publication typeJournal Article
Publication date2025-01-16
scimago Q1
wos Q3
SJR1.342
CiteScore5.8
Impact factor2.6
ISSN19406207, 19406215
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.

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GOST Copy
Mitbander R. et al. 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 // Cancer Prevention Research. 2025. Vol. 18. No. 4. p. OF1-OF11.
GOST all authors (up to 50) Copy
Mitbander R., Brenes D. R., Coole J. B., Kortum A., Vohra I. S., Carns J., Schwarz R. A., Varghese I., Durab S., Anderson S., Bass N. E., Clayton A. D., Badaoui H., Anandasivam L., Giese R., Gillenwater A., Vigneswaran N., Richards-Kortum R. R. 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 // Cancer Prevention Research. 2025. Vol. 18. No. 4. p. OF1-OF11.
RIS |
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RIS Copy
TY - JOUR
DO - 10.1158/1940-6207.capr-24-0253
UR - https://aacrjournals.org/cancerpreventionresearch/article/doi/10.1158/1940-6207.CAPR-24-0253/751120/Development-and-Evaluation-of-an-Automated
TI - 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
T2 - Cancer Prevention Research
AU - Mitbander, Ruchika
AU - Brenes, David R.
AU - Coole, Jackson B
AU - Kortum, Alex
AU - Vohra, Imran S.
AU - Carns, Jennifer
AU - Schwarz, Richard A.
AU - Varghese, Ida
AU - Durab, Safia
AU - Anderson, Sean
AU - Bass, Nancy E.
AU - Clayton, Ashlee D.
AU - Badaoui, Hawraa
AU - Anandasivam, Loganayaki
AU - Giese, Rachel
AU - Gillenwater, Ann
AU - Vigneswaran, N
AU - Richards-Kortum, Rebecca R.
PY - 2025
DA - 2025/01/16
PB - American Association for Cancer Research (AACR)
SP - OF1-OF11
IS - 4
VL - 18
SN - 1940-6207
SN - 1940-6215
ER -
BibTex |
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BibTex (up to 50 authors) Copy
@article{2025_Mitbander,
author = {Ruchika Mitbander and David R. Brenes and Jackson B Coole and Alex Kortum and Imran S. Vohra and Jennifer Carns and Richard A. Schwarz and Ida Varghese and Safia Durab and Sean Anderson and Nancy E. Bass and Ashlee D. Clayton and Hawraa Badaoui and Loganayaki Anandasivam and Rachel Giese and Ann Gillenwater and N Vigneswaran and Rebecca R. Richards-Kortum},
title = {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},
journal = {Cancer Prevention Research},
year = {2025},
volume = {18},
publisher = {American Association for Cancer Research (AACR)},
month = {jan},
url = {https://aacrjournals.org/cancerpreventionresearch/article/doi/10.1158/1940-6207.CAPR-24-0253/751120/Development-and-Evaluation-of-an-Automated},
number = {4},
pages = {OF1--OF11},
doi = {10.1158/1940-6207.capr-24-0253}
}
MLA
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MLA Copy
Mitbander, Ruchika, et al. “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.” Cancer Prevention Research, vol. 18, no. 4, Jan. 2025, pp. OF1-OF11. https://aacrjournals.org/cancerpreventionresearch/article/doi/10.1158/1940-6207.CAPR-24-0253/751120/Development-and-Evaluation-of-an-Automated.