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.

摘要 在美国，每年诊断出约50，000例口腔和咽癌（OPC） (10/100，000发病率）。此外，口腔上皮发育不良（OED）比OPC常见约15倍。 已知诊断为OED的患者有恶性转化（MT）的风险，而那些接受治疗的患者 已知口腔鳞状细胞癌（OSCC）具有较高的癌症复发（CR）风险。有 关于这些患者的最佳临床监测途径的共识很少。有以下病史的人 口腔鳞状细胞癌和潜在恶性口腔病变（PMOLs）窝藏OED/口腔鳞状细胞癌可以有广泛的临床变化， 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进行 对这些病人进行了一系列的手术刀活检市售诊断试剂缺乏足够的临床应用 跨病变疾病谱的验证。当口腔鳞状细胞癌或高度OED被早期诊断时， 提供适当的及时治疗的机会，患者的结果可以显着改善。因此 迫切需要新的高效非侵入性精确口腔病变诊断技术， 根据个别患者的需要量身定制。 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），一种基于微流体集成和单细胞图像的数据采集系统 采用人工智能解释>100个图像特征，包括核F-肌动蛋白，以提高精度 完成口腔病变诊断。便携式诊断工具和嵌入式算法将进行优化， 第一次在二级和三级医疗机构进行。在这项R 01研究中，POCOCT衍生的OSCC CR和OED 将开发MT模型，以阐明数值指数的人群和患者特异性动态变化 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一个单一的时间点， 在监测期间，将使用相同的多模式芯片方法重复采样，以确定值 MT和CR的变化速度。本R 01研究的总体目标是：（1）确定是否 细胞学特征，当随着时间的推移进行连续检查时，可以导致更好的CR风险预测，（2） 确定相同的特征是否可以导致比传统临床更早地检测局部复发 路径，和（3）进一步优化POCOCT，以精确诊断MT和CR病变，使用新的 确定的生物标志物，包括核F-肌动蛋白，以及通过深度学习确定的罕见细胞表型。 该R 01将利用独特的NIDCR-Grand Opportunity数据库，实现新的精度范例 诊断高风险患者将在二级和三级医疗机构中定期进行纵向监测， 并且他们的风险轨迹将随着时间的推移使用个性化的多变量细胞学特征来建立， 以及初始值。这项前瞻性纵向队列研究有可能获得更准确的病变诊断， 提高患者生存率和整体生活质量。