Multimodal Intraoral Imaging System for Oral Cancer Detection and Diagnosis in Low Resource Setting

用于资源匮乏环境下口腔癌检测和诊断的多模态口腔内成像系统

• 批准号: 10663873
• 负责人: Rongguang Liang
• 金额: $ 61.82万
• 依托单位: UNIVERSITY OF ARIZONA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-10 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10663873
• 关键词: Accounting; Address; Adopted; Algorithms; Anatomy; Area; Arizona; Benign; Biopsy; California; Cancer Center; Cancer Detection; Classification; Clinical; Dental; Detection; Diagnosis; Diagnostic; Disease Progression; Dysplasia; Early Diagnosis; Ensure; Faucial pillar; Goals; Heterogeneity; Histopathology; Image; Imaging Techniques; India; Individual; Infrastructure; Institution; Lesion; Light; Location; Malignant - descriptor; Malignant Neoplasms; Maps; Medical; Methods; Modality; Morbidity - disease rate; Multimodal Imaging; Nodal; Oncology; Optical Coherence Tomography; Oral; Oral Diagnosis; Oral cavity; Oropharyngeal; Oropharyngeal Squamous Cell Carcinoma; Output; Participant; Performance; Resolution; Resource-limited setting; Risk; Scanning; Schedule; Screening for Oral Cancer; Sensitivity and Specificity; Site; Stage at Diagnosis; Survival Rate; System; Systems Integration; Techniques; Technology; Training; Translating; Translations; Triage; Universities; Work; cancer diagnosis; cancer imaging; cancer prevention; classification algorithm; clinical decision-making; clinical diagnostics; commercialization; cost; cost effective; deep learning; deep learning algorithm; design; diagnostic algorithm; diagnostic tool; experience; flexibility; follow-up; high risk; image processing; imaging probe; imaging system; impression; improved; industry partner; innovation; intraoral probe; low and middle-income countries; malignant mouth neoplasm; malignant oropharynx neoplasm; miniaturize; mortality; multimodality; neural network; optical imaging; oral care; oral dysplasia; oral lesion; portability; prototype; recruit; response; rural area; screening; standard of care; tongue root; treatment planning; user-friendly

Oral and oropharyngeal squamous cell carcinoma (OSCC) together rank as the sixth most common cancer worldwide, accounting for 400,000 new cancer cases each year. Two-thirds of these cancers occur in low- and middle-income countries (LMICs). While the 5-year survival rate in the U.S. is 62%, the survival rate is only 10- 40% and cure rate around 30% in the developing world. The poor survival rate in LMICs is mainly due to late diagnosis and the resultant progression of disease to an advanced stage at diagnosis. Therefore, it is imperative to diagnose precursor and malignant lesions in LRS early and expeditiously. To meet the need for technologies that enable comprehensive oral cancer screening and diagnosis in low resource settings (LRS) to identify the suspicious lesions, triage the high-risk subjects and thereby enable appropriate treatment management and follow up, this project brings together an interdisciplinary team with complementary expertise in optical imaging, oncology, deep learning, technology translation, and commercialization. The team will develop, validate, and clinically translate a multimodal intraoral imaging system for oral cancer detection and diagnosis with better sensitivity and specificity. This work will address key barriers to adopting optical imaging techniques for oral cancer in LRS by building on the team’s experience in 1) developing and evaluating dual-mode (polarized white light imaging [pWLI] and autofluorescence imaging [AFI]) mobile imaging probes; 2) evaluating a low-cost, portable optical coherence tomography (OCT) system for oral cancer detection and diagnosis in a nodal center setting in India; and 3) developing and evaluating deep learning-based image classification algorithms for clinical decision-making guidance. As each of these key techniques has been demonstrated separately for oral cancer imaging in LRS, the potential of successfully developing a multimodal intraoral imaging system for accurate, objective and location-resolved diagnosis of oral cancer and transitioning to a new capability to medical professionals in LRS is very high. To achieve the project objective, the team proposes three Aims: 1) develop a portable, semi-flexible, and compact multimodal intraoral imaging system; 2) evaluate the clinical feasibility of the prototyped intraoral imaging system and develop deep learning-based image processing algorithms for early detection, diagnosis, and mapping of oral dysplastic and malignant lesions; and 3) validate the capability of the prototyped intraoral imaging system for diagnosing oral dysplasia and malignant lesions. Successful completion of this project will lead to the transition of a multimodal intraoral imaging system and deep learning image classification that leverage the individual strengths of multiple technologies and deliver new and urgently-needed capabilities to the end users in LRS. This integrated system will 1) detect suspicious regions with high sensitivity and specificity; 2) triage the high-risk subjects; and 3) guide the selection of biopsy sites and map lesion heterogeneity to improve treatment planning and intra-operative guidance.

口腔和口咽鳞状细胞癌（OSCC）共同排名第6位最常见的癌症

项目成果

Inter-observer agreement among specialists in the diagnosis of Oral Potentially Malignant Disorders and Oral Cancer using Store-and-Forward technology.

使用存储转发技术诊断口腔潜在恶性疾病和口腔癌的专家之间的观察者间协议。

• DOI: 10.21203/rs.3.rs-2754683/v1
• 发表时间: 2023
• 期刊: Research square
• 作者: Gurushanth,Keerthi;Mukhia,Nirza;Sunny,SumsumP;Song,Bofan;Raghavan,ShubhasiniA;Gurudath,Shubha;Mendonca,Pramila;Li,Shaobai;Patrick,Sanjana;Imchen,Tsusennaro;Leivon,ShirleyT;Shruti,Tulika;Kolur,Trupti;Shetty,Vivek;BhushanR,V
• 通讯作者: BhushanR,V

Interpretable and Reliable Oral Cancer Classifier with Attention Mechanism and Expert Knowledge Embedding via Attention Map.

• DOI: 10.3390/cancers15051421
• 发表时间: 2023-02-23
• 期刊: Cancers
• 影响因子: 5.2