AI-empowered 3D Computer Vision and Image-Omics Integration for Digital Kidney Histopathology

AI 赋能的 3D 计算机视觉和图像组学集成用于数字肾脏组织病理学

基本信息

批准号：
10635439
负责人：
Yuankai Huo
金额：
$ 61.49万
依托单位：
VANDERBILT UNIVERSITY
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：
2023-03-15 至 2027-12-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10635439
关键词：
3-Dimensional Adult Algorithms Allografting Anatomy Appearance Biological Markers Biopsy Caring Cell Communication Characteristics Chronic Kidney Failure Classification Clinical Computer Assisted Computer Vision Systems Computer software Decision Making Diagnosis Elements Focal and Segmental Glomerulosclerosis Future Gene Expression Profile Goals Grain Healthcare Heterogeneity Histopathology Human BioMolecular Atlas Program Image Individual International Kidney Kidney Diseases Kidney Transplantation Label Lesion Methods Modeling Molecular Morphologic artifacts Morphology Needle biopsy procedure Nephrotic Syndrome Pathogenesis Pathologist Pathology Patient Care Patients Phenotype Prognosis Renal Tissue Reproducibility Research Scientist Stains Standardization Structure Techniques Technology Three-Dimensional Imaging Tissues Transplant Recipients United States National Institutes of Health Visualization allograft rejection analytical tool cell type clinical phenotype cost deep learning design digital digital imaging digital pathology empowerment glomerulosclerosis imaging biomarker imaging modality kidney allograft molecular marker novel personalized diagnostics routine practice success supervised learning three-dimensional visualization tool transcriptomics two-dimensional whole slide imaging

项目摘要

PROJECT SUMMARY Our overarching goal is to enable the AI-empowered 3D histopathological interpretation on routine digitized renal tissue biopsies, so as to (1) allow renal pathologists to perform a reproducible 3D phenotyping on serial 2D whole slide images (WSI), (2) advance the characterization of kidney-allograft rejection phenotypes on kidney transplant patients with cutting-edge 3D computer vision, and (3) equip clinical scientists with an advanced 3D spatial transcriptomics analytics tool to investigate the anatomical-molecular associated causes of chronic kidney disease (CKD). Our novel 3D histopathological interpretation, with 3D computer vision (Map3D toolkit) and 3D spatial transcriptomics, will open a new door for performing reproducible clinical phenotyping (Pheno3D toolkit), identifying and validating new 3D imaging and molecular biomarkers (GPS3D toolkit), and ultimately advancing the patient care with personalized diagnosis and prognosis options for a wide range of CKD. Despite more than 25 years of exploitation of digital pathology, the presence, significance and characteristics of 3D contextual information in renal histopathological assessment have been largely overlooked. The current 2D interpretation on renal histopathology is error-prone and less reproducible due to the heterogeneity of tissue morphologies (e.g., glomeruli, tubules, vessels) across 3D serial sections. For example, our previous study on segmental glomerulosclerosis (GS) in patients with nephrotic syndrome and idiopathic FSGS, the percent of GS increased from 31.5 +/- 6.8% to 48.0 +/- 6.6% (P < 0.025) in adults by replacing a 2D single section analysis with 3D serial section analysis. Moreover, 2D based phenotyping can also hinder the discovery of new biomarkers via state-of-the-art spatial transcriptomic techniques. As an example, a glomerulus with focal segmental glomerulosclerosis (FSGS) can have a normal appearance on a specific 2D section, which might lead to an opposite molecular finding using 2D spatial transcriptomics The core tenant of this proposal is NOT developing a new 3D imaging modality, but rather, to develop technologies that enable reproducible 3D characterization on routine 2D renal histopathological biopsies (with trivial added cost), so as to advance the care of future patients with renal diseases. To this end, we will: Aim 1. Develop novel 3D computer vision tools (Map3D) to facilitate renal pathologists in modeling, quantifying, and visualizing 3D renal histopathological tissues from routine 2D digital histopathology. Impact: Allow renal pathologists to perform a reproducible 3D phenotyping on serial 2D whole slide images (WSI). Aim 2. Develop 3D phenotyping tools (Pheno3D) to advance the characterization of kidney-allograft rejection for kidney transplant patients via 3D computer vision and self-supervised deep learning. Impact: Advance the characterization of kidney-allograft rejection phenotypes for kidney transplant patients. Aim 3. Develop 3D computer vision algorithms for 2D and 3D spatial transcriptomics (GPS3D toolkit). Impact: Equip clinical scientists an 3D spatial transcriptomics analytics tool to investigate image-omics interaction.

项目概要我们的首要目标是对常规数字化肾病进行人工智能支持的 3D 组织病理学解释组织活检，以便 (1) 允许肾脏病理学家对连续 2D 整体进行可重复的 3D 表型分析幻灯片图像 (WSI)，(2) 推进肾脏同种异体移植排斥表型的表征为患者移植先进的 3D 计算机视觉，以及 (3) 为临床科学家配备先进的 3D 空间转录组学分析工具，用于研究慢性肾病的解剖分子相关原因疾病（CKD）。我们新颖的 3D 组织病理学解释，具有 3D 计算机视觉（Map3D 工具包）和 3D 空间转录组学将为执行可重复的临床表型分析打开一扇新的大门（Pheno3D 工具包），识别和验证新的 3D 成像和分子生物标志物（GPS3D 工具包），并最终通过针对各种 CKD 的个性化诊断和预后选项来推进患者护理。尽管数字病理学的开发已超过 25 年，但其存在、意义和肾脏组织病理学评估中 3D 背景信息的特征已在很大程度上得到了证实。被忽视了。目前对肾脏组织病理学的二维解释容易出错且可重复性较差，因为 3D 连续切片中组织形态（例如肾小球、肾小管、血管）的异质性。为了例如，我们之前对肾病综合征患者节段性肾小球硬化症（GS）的研究特发性 FSGS，成人中 GS 的百分比从 31.5 +/- 6.8% 增加到 48.0 +/- 6.6% (P < 0.025) 用 3D 串行截面分析替换 2D 单截面分析。此外，基于 2D 的表型分析还可以阻碍通过最先进的空间转录组技术发现新的生物标志物。举个例子，一个患有局灶节段性肾小球硬化症 (FSGS) 的肾小球在特定 2D 上可以具有正常外观部分，这可能会导致使用 2D 空间转录组学得出相反的分子发现该提案的核心租户不是开发新的 3D 成像模式，而是开发能够对常规 2D 肾脏组织病理学活检进行可重复 3D 表征的技术（微不足道的增加成本），从而促进未来肾病患者的护理。为此，我们将：目标 1. 开发新型 3D 计算机视觉工具 (Map3D)，以方便肾脏病理学家进行建模、从常规 2D 数字组织病理学中量化和可视化 3D 肾脏组织病理学组织。影响：允许肾脏病理学家对连续 2D 全切片图像 (WSI) 进行可重复的 3D 表型分析。目标 2. 开发 3D 表型分析工具 (Pheno3D) 以推进同种异体肾移植物的表征通过 3D 计算机视觉和自我监督深度学习来治疗肾移植患者的排斥反应。影响：推进肾移植患者肾同种异体移植排斥表型的表征。目标 3. 开发用于 2D 和 3D 空间转录组学的 3D 计算机视觉算法（GPS3D 工具包）。影响：为临床科学家配备 3D 空间转录组学分析工具来研究图像组学相互作用。