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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要/摘要口腔粘膜病变的成功治疗和管理取决于明确、准确和及时的尽管口腔鳞状细胞癌（OSCC）很容易进入口腔，但口腔鳞状细胞癌（OSCC）是最常见的。口腔癌通常直到晚期才被诊断出来，导致口腔上皮发育不良（OED）预后不良。是一种通过显微镜诊断的癌前病变，与 OSCC 转化风险增加相关。根据世界卫生组织的标准，OED 在组织学上可分为轻度、中度或重度不幸的是，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 缺乏客观、定量的 OED 风险分层方法。阻碍了口腔癌前病变的有效治疗并延迟了 OSCC 的诊断。一种利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，提供定量分析。使用显微镜以特征吸收光谱的形式显示样品的生化信息。与 FTIR 光谱仪相结合，FTIR 成像可以对生物化学进行详细的空间分辨分析。样本，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。我们使用机器学习辅助的 FTIR 图像分析和诊断模型开发。成功训练了三个机器学习分类器，其区分 OSCC 的准确度为 95-100% 更令人兴奋的是，我们的结果证明了一种创新的分层。根据上皮 FTIR 指纹将严重的 OED 分为良性样和 OSCC 样亚组。根据早期发现，该提案的中心假设是机器学习辅助的 FTIR 成像提供客观且定量的 OED 风险分层为了检验该假设，我们提出以下两个建议。具体目标：1) 基于上皮和基质 FTIR 指纹开发 OSCC 良性分类器，2) 评估基于 FTIR 图像的方法在 OED 风险分层中的可行性。研究的目的是开发一种使用 FTIR 成像或红外成像的人工智能辅助精密成像系统与其他形态学和功能成像模式相结合，例如数字病理学和免疫组织化学用于早期口腔癌的诊断、治疗和预防。