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，每年被诊断出约50,000口腔和咽癌（OPC） （发病率10/100,000）。此外，口腔上皮发育不良（OED）的常见约15倍。 已知诊断为OED的患者有恶性转化（MT）的风险，而接受治疗的患者 已知口服鳞状细胞癌（OSCC）的癌症复发风险升高（CR）。有 关于这些患者的最佳临床监测途径的共识很少。有历史的人 OSCC和携带OED/OSCC的潜在恶性口服病变（PMOLS）可能具有广泛的临床变化 表现与没有恶性潜力的口腔病变重叠。因此，临床医生可能不愿进行 这些患者的串行手术刀活检。市售的诊断辅助手段缺乏足够的临床 在病变疾病范围内的验证。当OSCC或高级OED早期诊断出时，就会有一个 提供适当及时治疗的机会，患者的结果可以大大改善。因此，那里 是对新的高效非侵入精度口服病变诊断技术的迫切需求，可以 根据个别患者的需求进行量身定制。 这项多机构的前瞻性队列研究旨在利用和优化口头的第一次口头 细胞病理学工具（POCOCT），微流体集合和基于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，以精确 口服病变诊断要完成。便携式诊断工具和嵌入式算法将被优化 第一次中学和三级护理设置。在这项R01研究中，Pococt衍生的OSCC CR和OED MT模型将开发以阐明数值指数的人口和特定于患者的动态变化 产生与CR和MT风险有关的关键信息。虽然过去的努力集中在一个时间点上，但 相同的多模式基于基于芯片的方法将在监视期间反复进行采样，以识别该值 变革速度到MT和Cr。这项R01研究的总体目标是：（1）确定是否是否 随着时间的流逝，在串行检查时，细胞学特征可能会导致CR的更好风险预测，（2） 确定相同的签名是否可以导致比传统临床更早发现局部复发 途径和（3）进一步优化了使用新的MT和CR的精确病变诊断的PocoCT 通过深度学习鉴定出的生物标志物，包括核F-肌动蛋白和稀有细胞表型。 此R01将利用独特的NIDCR栅格机会数据库来获得新的精确范式 诊断。高风险患者将在次级和三级护理环境中纵向监测 随着时间的流逝，他们的风险轨迹将使用个性化的多元细胞学特征作为 以及初始值。这项前瞻性纵向队列研究具有更准确的病变诊断， 改善患者的生存和整体生活质量。