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: 10465103
• 负责人: Rongguang Liang
• 金额: $ 63.98万
• 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/10465103
• 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; Network-based; 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; Techniques; Technology; Training; Translating; Translations; Triage; Universities; Work; base; 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 guided; 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.

口服和口咽鳞状细胞癌（OSCC）列为第六个最常见的癌症 全球，每年占40万例新癌症病例。这些癌症中有三分之二发生在低 - 和 中等收入国家（LMIC）。虽然美国的5年生存率为62％，但生存率仅为10- 发展中国家的40％和治愈率约为30％。 LMIC的存活率差主要是由于迟到 诊断和疾病诊断后的晚期阶段进展。因此，这是必须的 早日诊断LRS中的前体和恶性病变。 满足对能够在低低的口腔癌筛查和诊断的技术的需求 资源设置（LRS）以识别可疑病变，分类高风险主体，从而使 适当的治疗管理和跟进，该项目汇集了一个跨学科团队 在光学成像，肿瘤学，深度学习，技术翻译和 商业化。该团队将开发，验证和临床翻译多式联运内成像 口腔癌检测和诊断系统具有更好的敏感性和特异性。这项工作将 通过在团队的建立中，解决LRS采用光学成像技术的关键障碍 经验1）开发和评估双模式（偏光白光成像[PWLI]和 自动荧光成像[AFI]）移动成像问题； 2）评估低成本的便携式光学连贯性 在印度的淋巴结中心环境中进行口腔癌检测和诊断的层析成像（OCT）系统； 3） 为临床决策开发和评估基于学习的深度图像分类算法 指导。由于这些关键技术都针对LRS中的口腔癌成像分别证明了 成功开发多模式内成像系统以进行准确，客观和 口腔癌的位置分辨诊断，并过渡到LRS医疗专业人员的新能力 很高。为了实现项目目标，团队提出三个目标：1）开发一种便携式，半富裕的， 和紧凑的多模式内成像系统； 2）评估原型口内的临床可行性 成像系统和开发基于深度学习的图像处理算法，用于早期检测，诊断， 以及口服异型和恶性病变的映射； 3）验证原型内部的能力 诊断性口腔发育不良和恶性病变的成像系统。 该项目的成功完成将导致多模式内成像系统的过渡 以及深入学习图像分类，以利用多种技术的个人优势并提供 最终用户在LRS中为最终用户提供了新的和急需的功能。该集成系统将1）检测到可疑 具有高灵敏度和特异性的区域； 2）分类高风险受试者； 3）指导活检的选择 位点和地图病变异质性，以改善治疗计划和术中指导。