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.

项目总结/摘要 口腔粘膜病变的成功治疗和管理依赖于明确、准确和及时的 诊断.尽管容易进入口腔，但口腔鳞状细胞癌（OSCC），最常见的 口腔癌通常直到晚期才被诊断出来，导致预后不良。口腔上皮发育不良（OED） 是一种显微镜下诊断的癌前病变，与口腔鳞状细胞癌转化的风险增加有关。 根据世界卫生组织的组织学分级，OED可分为轻度、中度或重度。 三级分类系统。不幸的是，黄金标准的组织病理学诊断依赖于主观 活检组织的形态学评价，无法识别最有可能 经历恶性转化。由于缺乏客观和定量的OED风险分层方法， 阻止了对癌前口腔病变的有效治疗，并延迟了OSCC的诊断。我们提出 一种使用傅里叶变换红外光谱（FTIR）成像和机器学习的新方法， 解决客观OED风险评估的医学差距。FTIR光谱提供定量 以特征吸收光谱的形式提供样品的生化信息。用显微镜 与FTIR光谱仪耦合，FTIR成像允许对一种生物样品进行详细的和空间分辨的生化分析。 样品，每个像素包含完整的FTIR光谱。机器学习是高光谱分析的有力工具 FTIR图像分析和诊断模型开发。使用机器学习辅助的FTIR成像，我们 成功训练了三个机器学习分类器，在区分OSCC和 良性口腔组织的初步研究。更令人兴奋的是，我们的研究结果显示了一种创新的分层方法， 根据其上皮FTIR指纹，将重度OED分为良性样和OSCC样亚组。启发 根据早期的发现，该提案的中心假设是，机器学习辅助的FTIR成像 提供客观和定量的OED风险分层。为了验证这个假设，我们提出了以下两个假设： 具体目标：1）开发基于上皮和基质FTIR指纹的OSCC良性分类器，以及2） 评估FTIR图像为基础的方法在OED危险分层的可行性。的长期目标 研究是开发一种人工智能辅助精确成像系统，使用FTIR成像或 与其他形态学和功能成像模式的组合，例如数字病理学， 免疫组化用于早期口腔癌诊断、治疗和预防。