Infrared Spectroscopic Imaging and Machine Learning for Risk Stratification of Oral Epithelial Dysplasia

红外光谱成像和机器学习用于口腔上皮发育不良的风险分层

• 批准号: 10606086
• 负责人: YONG WANG
• 金额: $ 23.21万
• 依托单位: UNIVERSITY OF MISSOURI KANSAS CITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10606086
• 关键词: Address; Algorithms; Anatomy; Artificial Intelligence; Benign; Biochemical; Biological; Biological Markers; Biopsy; Carbohydrates; Characteristics; Classification; Clinical; Coupled; Data; Development; Diagnosis; Diagnostic; Discrimination; Disease Progression; Epithelium; Evaluation; Fingerprint; Fourier Transform; Functional Imaging; Future; Goals; Hematoxylin and Eosin Staining Method; Histologic; Histopathologic Grade; Image; Image Analysis; Immunohistochemistry; Individual; Intervention; Intraepithelial Neoplasia; Label; Lesion; Lipids; Machine Learning; Malignant - descriptor; Medical; Methods; Microscope; Microscopic; Molecular; Molecular Profiling; Monitor; Morphology; Nature; Nucleic Acids; Oral Diagnosis; Oral cavity; Oral mucous membrane structure; Outcome; Patients; Performance; Premalignant Cell; Prevention; Prognosis; Proteins; Research; Research Design; Risk; Risk Assessment; Sampling; Spectrum Analysis; Stains; Stratification; Subgroup; System; Techniques; Testing; Tissue Extracts; Tissues; Training; Validation; Work; World Health Organization; absorption; cancer diagnosis; digital pathology; efficacy evaluation; evidence base; feature extraction; high risk; imaging approach; imaging modality; imaging system; innovation; machine learning classifier; malignant mouth neoplasm; model development; mouth squamous cell carcinoma; multimodality; novel; novel strategies; oral carcinogenesis; oral cavity epithelium; oral lesion; oral tissue; premalignant; prevent; risk stratification; spectroscopic imaging; tool

PROJECT SUMMARY/ABSTRACT Successful treatment and management of oral mucosal lesions depend on a definitive, accurate, and timely diagnosis. Despite easy accessibility to the oral cavity, oral squamous cell carcinoma (OSCC), the most common oral cancer, is often not diagnosed until late stages, leading to a poor prognosis. Oral epithelial dysplasia (OED) is a microscopically diagnosed precancerous lesion associated with an increased risk of OSCC transformation. An OED can be histologically graded as mild, moderate, or severe based on the World Health Organization’s three-tier classification system. Unfortunately, the gold standard histopathological diagnosis relies on subjective morphological evaluation of the biopsy tissue and is unable to identify high-risk OEDs that are most likely to undergo malignant transformation. The lack of an objective and quantitative OED risk stratification approach has prevented effective management of precancerous oral lesions and delayed the diagnosis of OSCC. We propose a novel approach using Fourier transform infrared spectroscopic (FTIR) imaging and machine learning to address the medical gap of objective OED risk assessment. FTIR spectroscopy provides quantitative biochemical information of a sample in the form of characteristic absorption spectrum. With a microscope coupled to an FTIR spectrometer, FTIR imaging allows detailed and spatially resolved biochemical analysis of a sample, with each pixel containing a full FTIR spectrum. Machine learning is a powerful tool for hyperspectral FTIR image analysis and diagnostic model development. Using FTIR imaging aided by machine learning, we successfully trained three machine learning classifiers with 95–100% accuracy in discriminating OSCC from benign oral tissues in our preliminary study. More excitingly, our results demonstrated an innovative stratification of severe OEDs into Benign-like and OSCC-like subgroups based on their epithelial FTIR fingerprints. Inspired by the early finding, the central hypothesis of this proposal is that FTIR imaging aided by machine learning provides objective and quantitative OED risk stratification. To test the hypothesis, we propose the following two specific aims: 1) to develop OSCC-Benign classifiers based on epithelial and stromal FTIR fingerprints, and 2) to evaluate the feasibility of the FTIR image-based approach in OED risk stratification. The long-term goal of the research is to develop an artificial intelligence aided precision imaging system using FTIR imaging or in combination with other morphological and functional imaging modalities such as digital pathology and immunohistochemistry for early oral cancer diagnosis, treatment, and prevention.

项目摘要/摘要 口服粘膜病变的成功治疗和管理取决于确定，准确和及时的 诊断。尽管口腔易于使用，但口服鳞状细胞癌（OSCC），最常见 口腔癌通常直到晚期才被诊断出来，导致预后不良。口服上皮发育不良（OED） 是一种与OSCC转化风险增加有关的显微镜诊断的癌前病变。 根据世界卫生组织 三层分类系统。不幸的是，黄金标准组织病理学诊断依赖于主观 活检组织的形态学评估，无法识别最有可能的高风险OED 经历恶性转化。缺乏客观和定量的OED风险分层方法 防止有效治疗癌前口腔病变并延迟OSCC的诊断。我们建议 使用傅立叶变换红外光谱（FTIR）成像和机器学习的一种新颖方法 解决客观OED风险评估的医疗差距。 FTIR光谱提供了定量 样品以特征性滥用光谱的形式的生化信息。带有显微镜 FTIR成像耦合到FTIR光谱仪，允许对A的详细和空间分辨的生化分析 样品，每个像素包含完整的FTIR光谱。机器学习是高光谱的强大工具 FTIR图像分析和诊断模型开发。使用机器学习帮助的FTIR成像，我们 成功地培训了三个机器学习分类器，其精度为95–100％，以区分OSCC 我们的初步研究中的良性口服组织。更令人兴奋的是，我们的结果表明了创新的分层 根据其上皮FTIR指纹进入类似良性的OED和OSCC样子组。受到启发 通过早期发现，该提案的核心假设是FTIR成像是通过机器学习的帮助 提供客观和定量的OED风险分层。为了检验假设，我们提出以下两个 具体目的：1）开发基于上皮和基质FTIR指纹的OSCC-夹分类器，以及2） 评估基于FTIR图像的方法在OED风险分层中的可行性。长期目标 研究是使用FTIR成像或IN开发人工智能辅助精度成像系统 与其他形态学和功能成像的结合，例如数字病理和 早期口腔癌诊断，治疗和预防的免疫组织化学。