Optimizing Oral Cancer Screening and Precision Management of Potentially Malignant Oral Lesions

优化口腔癌筛查和潜在恶性口腔病变的精准管理

• 批准号: 10298437
• 负责人: Stella Kang
• 金额: $ 64.2万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10298437
• 关键词: Address; Adult; Affect; Alcohol consumption; American; Appearance; Artificial Intelligence; Benign; Biopsy; Cancer Control; Cancer Detection; Cancerous; Carcinoma; Caring; Categories; Cells; Characteristics; Clinical; Complex; Computer Vision Systems; Computers; Consultations; Cytology; Cytopathology; Data; Decision Aid; Decision Making; Decision Modeling; Detection; Devices; Diagnosis; Diagnostic; Diagnostic Imaging; Diagnostic tests; Disease; Disease model; Dysplasia; Early Diagnosis; Early identification; Effectiveness; Goals; Health; Health Benefit; High Prevalence; Histologic; Image; Incidence; Intraepithelial Neoplasia; Lesion; Longitudinal cohort study; Machine Learning; Malignant - descriptor; Malignant Neoplasms; Malignant neoplasm of pharynx; Methods; Mild Dysplasia; Modeling; National Institute of Dental and Craniofacial Research; Optics; Oral; Oral Diagnosis; Oral Examination; Oral Medicine; Oral Stage; Organ; Outcome; Pathway interactions; Patients; Performance; Population; Precision therapeutics; Procedures; Quality of life; Risk; Risk Assessment; Risk Factors; Role; Science; Screening for Oral Cancer; Specialist; Structure; Tactile; Testing; Tissues; Tobacco use; Translating; Translations; Triage; United States National Institutes of Health; Visual; Visualization; Work; base; cancer prevention; clinical application; clinical care; clinical decision-making; clinical risk; clinical translation; cost; cost effective; cost effectiveness; curative treatments; diagnostic screening; disorder risk; economic evaluation; economic outcome; health economics; high risk; improved; improved outcome; longitudinal dataset; malignant mouth neoplasm; models and simulation; mortality risk; mouth squamous cell carcinoma; new technology; oral care; oral diagnostics; oral lesion; oral premalignancy; overtreatment; personalized diagnostics; personalized management; point of care; premalignant; premature; prevent; risk stratification; scalpel; screening; screening guidelines; tool

Project Summary Despite treatment advances over the past several decades, cancer-specific survival for oral cancers remains bleak, mostly due to the majority of cases being diagnosed at late stages. Early-stage detection of cancers (most often oral squamous cell carcinoma (OSCC)) would enable less disfiguring, less costly therapy with curative intent. However, limitations of traditional visual-tactile examination for oral cancerous and pre- cancerous lesions have hindered cancer detection and support for screening. Visual inspection for separation of benign from precancerous or cancerous lesions is inaccurate, and therefore standard practice entails referral and scalpel biopsy of most potentially malignant oral lesions. Furthermore, approximately 20% of potentially malignant oral lesions contain some degree of epithelial dysplasia or carcinoma, and therefore early identification could allow curative treatment as the majority of OSCC typically starts as dysplasia, and the degree of dysplasia is correlated with the rate of malignant transformation. Detractors of oral screening cite the high prevalence of benign oral lesions and mild dysplasia as circumstances placing patients at risk of harms from over-testing and over-treatment. Thus, screening efforts could be transformed by adjunctive diagnostic tests that offer highly accurate cytopathologic information at the point of care, such as the NIDCR-supported Point-of-Care Oral Cytopathology Tool. Computer vision-assisted precision imaging tests have recently shown strong diagnostic performance for oral lesion characterization, but their potential pitfalls and promises must be thoroughly investigated before clinical application. Similarly, machine learning could bolster optical tests for visualizing potentially malignant lesions. If successful, these artificial intelligence devices could aid decision- making, preventing unnecessary scalpel biopsies for low-risk lesions and enabling risk-stratified surveillance or treatment. Our team of experts in computer disease simulation modeling, machine learning, oral medicine, and economic evaluation will transform a disease simulation model to provide analysis at the point of care, and evaluate the different potential uses of precision imaging diagnostics for translation to clinical care. We will expand our existing disease model of potentially malignant oral lesions to represent lesion characteristics and clinical risk categories (e.g. based on tobacco and alcohol use) through incorporation of large longitudinal datasets (Aim 1), in order to evaluate whether artificial intelligence-assisted cytologic testing can improve the effectiveness and cost-effectiveness of screening for low, moderate, or high risk categories (Aim 2). Finally, we will evaluate whether adjuncts for lesion visualization render favorable effectiveness and cost effectiveness of screening across risk categories, with or without artificial intelligence support, and develop a user interface for the model (Aim 3). This work will produce an analytic engine to guide clinical translation of artificial intelligence- aided diagnostics for oral lesion detection and characterization, to overcome insufficient screening reliability.

项目概要 尽管过去几十年来治疗取得了进步，但口腔癌的癌症特异性生存率 情况依然黯淡，主要是因为大多数病例已处于晚期诊断。早期检测 癌症（最常见的是口腔鳞状细胞癌（OSCC））可以减少毁容，降低治疗成本 具有治疗目的。然而，传统的视觉触觉检查对于口腔癌和口腔癌前病变的局限性 癌性病变阻碍了癌症检测和筛查支持。目视检查分离情况 将良性病变与癌前病变或癌性病变区分开来是不准确的，因此标准做法需要 大多数潜在恶性口腔病变的转诊和手术刀活检。此外，大约 20% 潜在的恶性口腔病变包含一定程度的上皮发育不良或癌，因此早期 鉴定可以实现治愈性治疗，因为大多数 OSCC 通常始于不典型增生，并且 不典型增生的程度与恶变率相关。口腔筛查的批评者引用了 良性口腔病变和轻度发育不良的患病率很高，使患者面临伤害风险 来自过度测试和过度治疗。因此，筛查工作可以通过辅助诊断来改变 在护理点提供高度准确的细胞病理学信息的测试，例如 NIDCR 支持的 护理点口腔细胞病理学工具。计算机视觉辅助精密成像测试最近表明 对于口腔病变特征具有很强的诊断性能，但它们的潜在陷阱和前景必须是 临床应用前要进行彻底的研究。同样，机器学习可以支持光学测试 可视化潜在的恶性病变。如果成功，这些人工智能设备可以帮助决策 进行、防止对低风险病变进行不必要的手术刀活检，并实现风险分层监测或 治疗。我们的专家团队由计算机疾病模拟建模、机器学习、口腔医学和 经济评估将转变疾病模拟模型以提供护理点分析，以及 评估精密成像诊断转化为临床护理的不同潜在用途。我们将 扩展我们现有的潜在恶性口腔病变的疾病模型以代表病变特征和 通过纳入大型纵向数据来划分临床风险类别（例如，基于烟草和酒精的使用） 数据集（目标 1），以评估人工智能辅助细胞学检测是否可以改善 低、中或高风险类别筛查的有效性和成本效益（目标 2）。最后，我们 将评估病变可视化的辅助手段是否具有良好的有效性和成本效益 在有或没有人工智能支持的情况下，跨风险类别进行筛查，并开发一个用户界面 模型（目标 3）。这项工作将产生一个分析引擎来指导人工智能的临床转化—— 口腔病变检测和表征的辅助诊断，以克服筛查可靠性不足的问题。