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.

口腔和口咽鳞状细胞癌(OSCC)在最常见的癌症中排名第六 全球每年新增癌症病例40万例。这些癌症中有三分之二发生在低位和 中等收入国家(LMIC)。虽然美国的5年存活率为62%，但存活率只有10%- 发展中国家为40%，治愈率约为30%。LMICs存活率低的主要原因是晚期 诊断和由此导致的疾病进展到诊断的晚期阶段。因此，它势在必行。 目的：早期、快速诊断腰椎前病变和恶性病变。 满足对能够以低成本进行全面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，对高危对象进行分类，从而实现 适当的治疗管理和跟进，这个项目汇集了一个跨学科的团队， 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、验证和临床翻译一种多模式口腔内成像 用于口腔癌检测和诊断的系统具有更高的灵敏度和特异性。这项工作将 通过在团队的基础上解决口腔癌采用光学成像技术的关键障碍 1)开发和评估双模(偏振白光成像[pWLI]和 自体荧光成像[AFI])移动成像探针；2)评估低成本、便携式光学相干性 印度结节中心口腔癌检测和诊断的断层扫描(OCT)系统；以及3) 临床决策中基于深度学习的图像分类算法的开发与评价 指导。由于这些关键技术中的每一种都已分别用于LRS中的口腔癌成像， 成功开发多模式口腔内成像系统的潜力 口腔癌的位置分辨诊断和向LRS医务人员的新能力过渡 非常高。为了实现项目目标，团队提出了三个目标：1)开发可移植的、半灵活的、 和紧凑型多模式口腔内成像系统；2)评价口腔内样机的临床可行性 成像系统，并开发基于深度学习的图像处理算法，用于早期检测、诊断、 以及口腔发育不良和恶性病变的标绘；以及3)验证口腔内原型的能力 诊断口腔异型增生和恶性病变的影像系统。 该项目的成功完成将导致多模式口腔内成像系统的过渡 和深度学习图像分类，利用多种技术的个人优势并提供 在LRS中向终端用户提供新的和迫切需要的功能。这个集成的系统将1)检测可疑的 具有高敏感性和高特异性的区域；2)对高危对象进行分类；以及3)指导活检的选择 定位和绘制病变异质性地图，以改进治疗计划和术中指导。