Deep Learning Image Analysis Algorithms to Improve Oral Cancer Risk Assessment for Oral Potentially Malignant Disorders

深度学习图像分析算法可改善口腔潜在恶性疾病的口腔癌风险评估

基本信息

批准号：
10805177
负责人：
Curtis Pickering
金额：
$ 68.4万
依托单位：
YALE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-05-01 至 2026-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10805177
关键词：
Affect Algorithmic Analysis Algorithms Architecture Biological Biological Markers Biology Biopsy Case/Control Studies Cell Nucleus Cells Characteristics Classification Clinical Clinical Data Clinical Trials Collection Computational algorithm Computer Analysis Computing Methodologies Data Development Diagnosis Disease Dysplasia Epithelial Cells Evaluation Exclusion Future General Population Genomics Goals Hematoxylin and Eosin Staining Method Heterogeneity Histologic Histology Histopathologic Grade Image Image Analysis Immune Immunologic Markers Individual Intraepithelial Neoplasia Lesion Leukoplakia Machine Learning Malignant - descriptor Malignant Neoplasms Measurement Measures Methods Modeling Morphology Mucous Membrane Oral Oral Leukoplakia Oral cavity Oral mucous membrane structure Pathologic Pathology Patient Care Patient risk Patients Performance Persons Prevention Procedures Process Process Assessment Prognostic Marker Protocols documentation Recording of previous events Research Risk Risk Assessment Risk Marker Sampling Slide Stains Standardization Statistical Models System TP53 gene Testing Therapeutic Intervention Tissue Sample Tissue imaging Tissues Uncertainty Validation cancer diagnosis cancer risk cell type clinical care clinical risk cohort data integration deep learning disorder risk genomic biomarker genomic data high risk imaging biomarker improved innovation insight learning algorithm learning strategy malignant mouth neoplasm mouth squamous cell carcinoma mutational status novel oral plaque phenotypic data predict clinical outcome predictive modeling prognostic value progression risk prospective risk stratification treatment planning whole slide imaging

项目摘要

Abstract Oral potentially malignant disorders (OPMD) are a group of mucosal diseases in the oral cavity with a risk of progressing to oral squamous cell carcinoma. Risk assessment is traditionally done through a combination of clinical and histologic evaluation. Leukoplakia is a common type of OPMD that is given a histologic grading score that is supposed to be related to its risk of progression. However, there is tremendous intra- and inter- observer heterogeneity in dysplasia grading, leading to variability and uncertainty in risk assessment and treatment planning. This also hinders the ability to study the biology of these lesions. We propose to use whole slide imaging on routine hematoxylin and eosin (H&E) stained sections in combination with deep learning methods to build a consistent risk scoring system for OPMD. Our methods will identify cell, nucleus, and tissue architectural features relevant to risk of progression in OPMD. These features will be tested in a large retrospective case-control study and then validated prospectively. We will also explore combining them with genomic and immune biomarkers in order to improve the prognostic power and explore the biolo gy of progression in OPMD. We hope that these efforts will improve and standardize risk assessment for OPMD. This could lead to improved treatment and prevention options by enabling risk stratification and allowing future clinical trials be conducted in a more uniform patient cohort. Similarly, it could improve our understanding for the biology of OPMD and the process of progression to cancer.

抽象的口腔潜在恶性疾病（OPMD）是一组口腔粘膜疾病，具有以下风险：进展为口腔鳞状细胞癌。传统上，风险评估是通过结合以下因素来完成的：临床和组织学评估。白斑是一种常见的 OPMD 类型，具有组织学分级应该与其进展风险相关的分数。然而，存在巨大的内部和外部不典型增生分级的观察者异质性，导致风险评估和评估的可变性和不确定性治疗计划。这也阻碍了研究这些病变的生物学能力。我们建议使用整体结合深度学习对常规苏木精和伊红 (H&E) 染色切片进行幻灯片成像为 OPMD 建立一致的风险评分系统的方法。我们的方法将识别细胞、细胞核和组织与 OPMD 进展风险相关的结构特征。这些功能将在大范围内进行测试回顾性病例对照研究，然后进行前瞻性验证。我们还将探索将它们与基因组和免疫生物标志物，以提高预后能力并探索 OPMD 的进展。我们希望这些努力能够改进并规范 OPMD 的风险评估。这可以通过实现风险分层并允许未来改善治疗和预防方案临床试验应在更统一的患者队列中进行。同样，它可以提高我们对 OPMD 的生物学和进展为癌症的过程。

项目成果

期刊论文数量（2）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

ScopeViewer: A Browser-Based Solution for Visualizing Spatial Transcriptomics Data.

ScopeViewer：基于浏览器的空间转录组数据可视化解决方案。

DOI：
发表时间：
2023-07-25
期刊：
影响因子：
0
作者：
Luo, Danni;Robertson, Sophie;Zhan, Yuanchun;Rong, Ruichen;Wang, Shidan;Jiang, Xi;Yang, Sen;Palmer, Suzette;Jia, Liwei;Li, Qiwei;Xiao, Guanghua;Zhan, Xiaowei
通讯作者：
Zhan, Xiaowei

Artificial intelligence in mental healthcare: an overview and future perspectives.

精神卫生保健中的人工智能：概述和未来展望。