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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：
10430122
负责人：
Curtis Pickering
金额：
$ 67.04万
依托单位：
UNIVERSITY OF TX MD ANDERSON CAN CTR
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-06-15 至 2023-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10430122
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 Future General Population Genomics Goals Hematoxylin and Eosin Staining Method Heterogeneity Histologic Histology Histopathologic Grade Image Image Analysis Immune Immunologic Markers Individual Intraepithelial Neoplasia Lead 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 base 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 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.

摘要