Quantifying the individual contributions of comorbid tau neuropathologies using deep learning

使用深度学习量化共病 tau 神经病理学的个体贡献

• 批准号: 10058010
• 负责人: Satwik Rajaram
• 金额: $ 45.03万
• 依托单位: UT SOUTHWESTERN MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10058010
• 关键词: Adoption; Affect; Age; Aging; Alzheimer' s Disease; Antibodies; Astrocytes; Atlases; Autopsy; Biological; Brain; Cells; Cerebral cortex; Characteristics; Classification; Clinical; Complex; Data; Dementia; Deposition; Development; Diagnosis; Diagnostic; Disease; Elements; Exhibits; Future; Genetic; Gold; Heterogeneity; Histopathology; Human; Image; Individual; Investigation; Knowledge; Lesion; Libraries; Light; Machine Learning; Malignant Neoplasms; Methods; Microscopic; Monoclonal Antibodies; Morphology; Nerve Degeneration; Neurodegenerative Disorders; Neurons; Pathologic; Pathologist; Pathology; Pattern; Phenotype; Progressive Supranuclear Palsy; Protein Isoforms; Research; Resources; Sampling; Senile Plaques; Spatial Distribution; Stains; Tauopathies; Technology; Testing; Tissue Stains; Tissues; Visual; Visualization; base; case-based; cell type; clinical biomarkers; comorbidity; deep learning; digital imaging; gray matter; hands-on learning; improved; learning classifier; learning strategy; method development; neocortical; neuropathology; protein aggregation; specific biomarkers; tau Proteins; tau-1; tool; tumor; virtual; virtual imaging; white matter; whole slide imaging

PROJECT SUMMARY/ABSTRACT Co-occurrence of different neurodegenerative diseases is increasingly common with age and acts as a confounding factor in the development of disease-specific biomarkers. Yet, even by the gold standard of evaluating immunostaining for aggregated proteins in autopsy brains, pathologic complexity makes it impossible to reliably quantify the mixture of diseases by visual inspection, especially when coexistent disorders both feature the same aggregated protein, albeit in different disease-specific patterns. Here, we hypothesize that recent advances in deep learning can identify the distinctive patterns of Alzheimer disease (AD) and progressive supranuclear palsy (PSP) neuropathology, thereby allowing us to de-convolve their individual contributions from phospho-tau immunostaining of mixed pathologies. We will tackle this problem in three steps. First, in order to incorporate biological knowledge and enable interpretability of our disease predictions, we will develop a set of deep learning classifiers to identify disease relevant “features” in virtual whole slide images. These features will include different types of cells (e.g. neurons, astrocytes), aggregates (e.g. tufted astrocytes and senile plaques that are enriched in PSP and AD, respectively) and tissue regions (gray vs. white matter, which differ in pattern of involvement in these diseases). Second, based on the assumption that comorbid pathologies exhibit a mixture of pure disease features, we will build disease classifiers from pure AD and pure PSP cases. Given a local patch of tau-stained tissue, these classifiers will return their confidence that tissue exhibited either of these diseases. We will evaluate two approaches, one building on the “features” identified above and the other a more traditional black- box deep learning approach working purely off of image patches. Finally, we will evaluate our pure disease classifiers on cases with mixed pathologies based on pathologist review and concordance with antibodies to tau isoforms whose individual histomorphologies help to distinguish between AD and PSP. As they will identify established neuropathology features demonstrated by the widely-used AT8 phospho-tau and 3R and 4R tau isoform immunostaining, our classifiers will be a valuable resource for future digital imaging based studies in neuropathology. Our framework for de-convolving comorbidities from autopsy samples can be extended to other diseases, thus enabling better integration with clinical and biomarker data, and ultimately, improved antemortem diagnosis and therapy.

项目摘要/摘要 不同神经退行性疾病的同时出现越来越普遍，随着年龄而充当 疾病特异性生物标志物发展的混杂因素。但是，即使是按照黄金标准 评估尸检大脑中综合蛋白质的免疫染色，病理复杂性使它成为 不可能可靠地通过视觉检查量化疾病的混合物，尤其是在共存时 疾病均具有相同的综合蛋白质，尽管以不同的疾病特异性模式。在这里，我们 假设深度学习的最新进展可以识别阿尔茨海默氏病的独特模式 （AD）和进行性跨核麻痹（PSP）神经病理学，从而使我们能够解除其 混合病理学的磷酸-TAU免疫染色的个人贡献。 我们将通过三个步骤解决这个问题。首先，为了纳入生物学知识并启用 我们疾病预测的可解释性，我们将开发一组深度学习分类器来识别疾病 虚拟整个幻灯片图像中的相关“功能”。这些特征将包括不同类型的细胞（例如 神经元，星形胶质细胞），聚集体（例如，簇状的星形胶质细胞和老年斑块，富含PSP和AD， 分别）和组织区域（灰色与白质，在这些区域的参与模式上差异 疾病）。其次，基于合并症表现出纯病的混合物的假设 功能，我们将通过纯AD和纯PSP病例构建疾病分类器。鉴于当地的tau染色 组织，这些分类器将回报他们对组织暴露在这些疾病中的任何一种的信心。我们将 评估两种方法，一种在上面确定的“特征”上建造的，另一个是更传统的黑人 盒子深度学习方法纯粹是从图像贴片上工作的。最后，我们将评估我们的纯粹疾病 基于病理学家审查和与抗体一致的病理病例的分类器 TAU同工型的各个组织形态有助于区分AD和PSP。 因为他们将确定广泛使用的AT8 Phosho-Tau所证明的已建立的神经病理学特征 以及3R和4R TAU同工型免疫染色，我们的分类器将成为未来数字成像的宝贵资源 基于神经病理学的研究。我们从尸检样本中解散合并症的框架可以是 扩展到其他疾病，从而可以更好地与临床和生物标志物数据进行更好的整合 改进的抗虫诊断和治疗。