Oral Dysplasia and Oral Cavity Cancer Risk in Dental and Medical Surveillance Settings Using a Chairside Chip-Based Cytopathology Tool

使用基于椅旁芯片的细胞病理学工具评估牙科和医疗监测环境中的口腔发育不良和口腔癌风险

• 批准号: 10344966
• 负责人: JOHN T MCDEVITT
• 金额: $ 76.7万
• 依托单位: NEW YORK UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-07 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10344966
• 关键词: Actins; Agreement; Algorithmic Software; Algorithms; Artificial Intelligence; Biological Assay; Biological Markers; Biopsy; CD34 gene; Carcinoma; Cell-Matrix Junction; Cells; Classification; Clinical; Clinical Pathways; Clinical Research; Consensus; Cytology; Cytopathology; Data; Data Sources; Databases; Dental; Diagnosis; Diagnostic; Disease; Disease Progression; Early Diagnosis; Early identification; Epidermal Growth Factor Receptor; Evolution; Excision; F-Actin; Goals; Gold; Health; Histopathology; Image; Image Cytometry; Incidence; Individual; Institution; Intraepithelial Neoplasia; Lead; Lesion; Lip structure; Literature; Localized Malignant Neoplasm; Longitudinal cohort study; Malignant - descriptor; Malignant neoplasm of pharynx; Measurement; Measures; Medical Surveillance; Microfluidics; Modeling; Monitor; National Institute of Dental and Craniofacial Research; Nuclear; Operative Surgical Procedures; Oral cavity; Pathway interactions; Patient Monitoring; Patient-Focused Outcomes; Patients; Performance; Persons; Phenotype; Ploidies; Population; Population Surveillance; Predictive Value; Prospective cohort study; Protocols documentation; Quality of life; Questionnaires; Recording of previous events; Recurrence; Risk; Sampling; Series; Severity of illness; Specimen; Speed; Surveys; System; Technology; Time; Treatment outcome; Trust; Validation; Visit; Visual; base; cancer diagnosis; cancer recurrence; cancer risk; cell type; cellular imaging; data acquisition; data streams; deep learning; deep learning algorithm; diagnostic technologies; diagnostic tool; experience; high risk; imaging agent; improved; indexing; individual patient; insight; instrument; malignant mouth neoplasm; mouth squamous cell carcinoma; multimodality; oral care; oral cavity epithelium; oral dysplasia; oral lesion; patient population; personalized diagnostics; point of care; portability; predictive modeling; preference; prognostic value; programmed cell death ligand 1; prospective; rate of change; risk prediction; scalpel; single cell analysis; targeted imaging; tertiary care; time use; tool

ABSTRACT In the US, approximately 50,000 oral and pharyngeal cancers (OPCs) are diagnosed annually (10/100,000 incidence). Further, oral epithelial dysplasia (OED) is about 15 times more common than OPC. Patients diagnosed with OED are known to be at risk for malignant transformation (MT), and those treated for oral squamous cell carcinoma (OSCC) are known to be at elevated risk for cancer recurrence (CR). There is little consensus about the optimal clinical surveillance pathways for these patients. Individuals with a history of OSCC and potentially malignant oral lesions (PMOLs) harboring OED/OSCC can have widely variable clinical presentation that overlaps with oral lesions of no malignant potential. Thus, clinicians may be reluctant to perform serial scalpel biopsies on these patients. Commercially available diagnostic adjuncts lack adequate clinical validation across the lesion disease spectrum. When OSCC or high-grade OED is diagnosed early, there is an opportunity to provide appropriate timely treatment, and patient outcomes can improve dramatically. Thus, there is a compelling need for new highly effective non-invasive precision oral lesion diagnostic technologies that can be tailored for the needs of individual patients. This multi-institution prospective cohort study seeks to utilize and optimize first Point-of-Care Oral Cytopathology Tool (POCOCT), a microfluidics ensemble and single cell image-based data acquisition system employing artificial intelligence with interpretation of >100 image features including nuclear F-actin for precision oral lesion diagnostics to be completed. Portable diagnostic tools and embedded algorithms will be optimized for secondary and tertiary care settings for the first time. In this R01 study, POCOCT-derived OSCC CR and OED MT models will be developed to elucidate population and patient-specific dynamic changes in numerical index that yield key information related to CR and risk of MT. While past efforts focused on a single time point, this same multimodal chip-based approach will be used to sample repeatedly during surveillance to identify the value of speed of change to MT and CR. The overarching goals of this R01 study are: (1) to determine whether cytological signatures, when examined serially over time, can lead to better risk prediction for CR, (2) to determine if the same signatures can lead to earlier detection of local recurrence than the traditional clinical pathway, and (3) to further optimize the POCOCT for precision lesion diagnostics of MT and CR using newly identified biomarkers, including nuclear F-actin, and rare cell phenotypes identified by deep learning. This R01 will leverage unique NIDCR-Grand Opportunity databases for a new paradigm of precision diagnostics. High risk patients will be longitudinally monitored in secondary and tertiary care settings at intervals, and their risk trajectory will be established over time using personalized multivariate cytological signatures as well as initial values. This prospective longitudinal cohort study has potential for more accurate lesion diagnosis, improving patient survival and overall quality of life.

抽象的 在美国，每年诊断出约 50,000 例口腔癌和咽癌 (OPC) （10/100,000 发生率）。此外，口腔上皮发育不良 (OED) 的发生率比 OPC 大约高 15 倍。 已知诊断为 OED 的患者有恶变 (MT) 的风险，而接受治疗的患者 已知口腔鳞状细胞癌 (OSCC) 的癌症复发 (CR) 风险较高。有 关于这些患者的最佳临床监测途径几乎没有达成共识。有以下病史的个人 OSCC 和含有 OED/OSCC 的潜在恶性口腔病变 (PMOL) 的临床表现可能存在很大差异 与无恶性潜力的口腔病变重叠的表现。因此，临床医生可能不愿意执行 对这些患者进行连续手术刀活检。市售诊断辅助手段缺乏足够的临床依据 跨病变疾病谱的验证。当早期诊断出 OSCC 或高级 OED 时，会出现以下情况： 有机会提供适当的及时治疗，患者的治疗效果可以显着改善。因此，有 迫切需要新型高效非侵入性精准口腔病变诊断技术 针对个别患者的需求进行定制。 这项多机构前瞻性队列研究旨在利用和优化第一个护理点口腔 细胞病理学工具 (POCOCT)，一种微流体集成和基于单细胞图像的数据采集系统 采用人工智能解释超过 100 个图像特征，包括核 F-肌动蛋白，以实现精确性 待完成口腔病变诊断。便携式诊断工具和嵌入式算法将针对以下方面进行优化 首次建立二级和三级护理机构。在这项 R01 研究中，POCOCT 衍生的 OSCC CR 和 OED 将开发 MT 模型来阐明人群和患者特定的数值指数动态变化 产生与 CR 和 MT 风险相关的关键信息。虽然过去的努力集中在单个时间点，但这次 在监测过程中将使用相同的基于多模式芯片的方法重复采样以识别值 MT 和 CR 的变化速度。这项 R01 研究的总体目标是：(1) 确定是否 随着时间的推移连续检查细胞学特征，可以更好地预测 CR 风险，(2) 确定相同的特征是否可以比传统临床更早地检测局部复发 (3) 使用新的方法进一步优化 POCOCT，用于 MT 和 CR 的精确病变诊断 识别了生物标志物，包括核 F-肌动蛋白，以及通过深度学习识别的稀有细胞表型。 该 R01 将利用独特的 NIDCR-Grand Opportunity 数据库来实现新的精确范式 诊断。高风险患者将在二级和三级护理机构中定期进行纵向监测， 随着时间的推移，他们的风险轨迹将使用个性化的多变量细胞学特征来确定 以及初始值。这项前瞻性纵向队列研究有可能实现更准确的病变诊断， 提高患者的生存率和整体生活质量。