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

被引:0
|
作者
Mitbander, Ruchika [1 ]
Brenes, David [1 ]
Coole, Jackson B. [1 ]
Kortum, Alex [1 ]
Vohra, Imran S. [1 ]
Carns, Jennifer [1 ]
Schwarz, Richard A. [1 ]
Varghese, Ida [2 ]
Durab, Safia [2 ]
Anderson, Sean [2 ]
Bass, Nancy E. [2 ]
Clayton, Ashlee D. [1 ]
Badaoui, Hawraa [3 ]
Anandasivam, Loganayaki [4 ]
Giese, Rachel A. [5 ]
Gillenwater, Ann M. [3 ]
Vigneswaran, Nadarajah [2 ]
Richards-Kortum, Rebecca [1 ]
机构
[1] Rice Univ, Dept Bioengn, Houston, TX USA
[2] Univ Texas, Hlth Sci Ctr Houston, Sch Dent, Dept Diagnost & Biomed Sci, Houston, TX USA
[3] Univ Texas MD Anderson Canc Ctr, Dept Head & Neck Surg, Houston, TX USA
[4] Brownsville Community Hlth Ctr, Brownsville, TX USA
[5] Univ Texas Hlth Sci Ctr San Antonio, Dept Otolaryngol Head & Neck Surg, San Antonio, TX USA
关键词
POTENTIALLY MALIGNANT DISORDERS; CAVITY;
D O I
10.1158/1940-6207.CAPR-24-0253
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
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.
引用
收藏
页码:197 / 207
页数:11
相关论文
共 24 条
  • [21] The detection of oral pre- malignant lesions with an autofluorescence based imaging system (VELscope™) - a single blinded clinical evaluation (vol 9, pg 21, 2013)
    Hanken, Henning
    Kraatz, Juliane
    Smeets, Ralf
    Heiland, Max
    Assaf, Alexandre Thomas
    Blessmann, Marco
    Eichhorn, Wolfgang
    Clauditz, Till Sebastian
    Groebe, Alexander
    Kolk, Andreas
    Rana, Madiha
    HEAD & FACE MEDICINE, 2013, 9
  • [22] An Automated System Targeting Outpatients at High Risk for Oral Cancer Improves Recruitment to Screening Programs for Premalignant or Early-stage Cancerous Lesions
    Macey, Richard
    JOURNAL OF EVIDENCE-BASED DENTAL PRACTICE, 2015, 15 (02) : 68 - 69
  • [23] Bimodal multispectral imaging system with cloud-based machine learning algorithm for real-time screening and detection of oral potentially malignant lesions and biopsy guidance
    Narayanan, Subhash
    Anand, Suresh
    Prasanna, Ranimol
    Managoli, Sandeep
    Suvarnadas, Rinoy
    Shyamsundar, Vidyarani
    Nagarajan, Karthika
    Mishra, Sourav K.
    Johnson, Migi
    Ramanand, Mahesh Dathurao
    Jogigowda, Sanjay C.
    Rao, Vishal
    Gopinath, Kodaganur S.
    JOURNAL OF BIOMEDICAL OPTICS, 2021, 26 (08)
  • [24] Non-Invasive Diagnostic System Based on Light for Detecting Early-Stage Oral Cancer and High-Risk Precancerous Lesions-Potential for Dentistry
    Tatehara, Seiko
    Satomura, Kazuhito
    CANCERS, 2020, 12 (11) : 1 - 15