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: 10671642
• 负责人: Stella Kang
• 金额: $ 50.43万
• 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/10671642
• 关键词: 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 Equipment; 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 Center for Advancing Translational Sciences; National Institute of Dental and Craniofacial Research; Optics; Oral; Oral Diagnosis; Oral Examination; Oral Medicine; Oral Stage; Organ; Outcome; Pathway interactions; Patients; Performance; Persons; 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; aspirate; cancer prevention; clinical application; clinical care; clinical decision-making; clinical risk; clinical translation; cost; cost effective; cost effectiveness; curative treatments; diagnostic screening; diagnostic strategy; disorder risk; economic evaluation; economic outcome; 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; translational diagnostics; usability

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.

项目摘要 尽管在过去几十年中的治疗进展，但口服癌症的癌症特异性生存期 仍然黯淡，主要是由于大多数病例在后期被诊断出。早期检测 癌症（大多数经常口服鳞状细胞癌（OSCC））将使毁容较低，昂贵的治疗较低 具有治疗意图。然而，传统视觉考试对口服癌性和前癌症的局限性 癌性病变阻碍了癌症的检测和支持筛查。分离的视觉检查 癌性病变或癌变的良性不准确，因此标准做法需要 大多数可能恶性口腔病变的转诊和手术刀活检。此外，约有20％ 潜在的恶性口腔病变含有一定程度的上皮发育不良或癌，因此早期 鉴定可以允许治疗性治疗，因为大多数OSCC通常开始为发育不良，并且 发育不良程度与恶性转化率有关。口服筛查的批评者引用 良性口腔病变和轻度发育不良的高患病率会使患者处于危害的危险之中 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进行转换 在护理点上提供高度准确的细胞病理信息的测试，例如NIDCR支持 护理点口腔细胞病理学工具。最近显示了计算机视觉辅助精确成像测试 口腔病变表征的强大诊断性能，但它们的潜在陷阱和承诺必须是 在临床应用之前进行了彻底研究。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 - 制作，防止不必要的手术刀活检，以进行低风险病变，并实现风险分层的监视或 治疗。我们的计算机疾病模拟建模，机器学习，口服医学专家团队以及 经济评估将改变疾病模拟模型，以在护理点提供分析，并 评估精确成像诊断的不同潜在用途将转化为临床护理。我们将 扩展我们现有的潜在恶性口腔病变的疾病模型，以表示病变特征和 临床风险类别（例如，基于烟草和饮酒），通过合并 数据集（AIM 1），以评估人工智能辅助的细胞学测试是否可以改善 对低，中或高风险类别的筛查的有效性和成本效益（AIM 2）。最后，我们 将评估病变可视化的辅助功能是否具有良好的有效性和成本效益 跨风险类别筛选，无论是否有人工智能支持，并开发一个用户界面 模型（目标3）。这项工作将产生一种分析引擎，以指导人工智能的临床翻译 - 辅助口服病变检测和表征的诊断，以克服筛查可靠性不足。