Histotools: scaling digital pathology curation tools for quality control, annotation, labeling, and dataset identification

Histotools：用于质量控制、注释、标记和数据集识别的扩展数字病理学管理工具

• 批准号: 10708011
• 负责人: Andrew Robert Janowczyk
• 金额: $ 35.31万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-21 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10708011
• 关键词: Active Learning; Address; Adoption; Affect; Antarctica; Automobile Driving; Big Data; Biological; Biopsy; Breast; Cardiac; Categories; Cell Nucleus; Cells; Characteristics; Clinical; Communities; Compensation; Computer Assisted; Data; Data Set; Development; Diagnosis; Disease; Employment; Ensure; European Union; FDA approved; Feedback; Friends; Generations; Genes; Glass; Head and neck structure; Heart; Histologic; Histological Labelings; Histopathology; Human; Image; Infrastructure; Kidney; Kidney Diseases; Label; Letters; Lung; Lymphocyte; Lymphocytic Infiltrate; Machine Learning; Malignant Neoplasms; Malignant neoplasm of lung; Modeling; Morphologic artifacts; Morphology; Neck Cancer; Oncology; Ontology; Organ; Pathologist; Pathway interactions; Pattern; Performance; Privatization; Process; Prognosis; Quality Control; Recording of previous events; Reproducibility; Research; Research Personnel; Resources; Site; Slide; Stains; Testing; Texture; The Cancer Genome Atlas; The Cancer Imaging Archive; Tissues; Validation; Work; allograft rejection; cell type; clinical practice; cluster computing; cohort; companion diagnostics; computer monitor; cost; data curation; data lake; design; diagnostic assay; digital; digital pathology; experience; heart allograft; improved; innovation; interest; malignant breast neoplasm; novel; open source; organ transplant rejection; pathology imaging; patient response; precision medicine; predictive test; prognostic; prototype; quantitative imaging; repository; tool; tool development; treatment response; usability; web site; whole slide imaging

ABSTRACT: With recent approval of whole slide scanners for primary diagnosis, wherein routine glass histopathology slides are digitized and presented to clinical pathologists for diagnosis on computer monitors, a wealth of new untapped data is being created in routine clinical practice and placed in growing data lakes. In digital format, these whole slide images (WSIs) can be subjected to digital pathomics, i.e., the process of extracting quantitative image features associated with morphology, attributes, and relationships of histologic objects in WSIs. These features can subsequently be employed for discovery in many domains such as histogenomics, which sees associating phenotypical presentations with biological pathways and gene ontologies. Additionally, low-cost non-tissue destructive image-based companion diagnostic assays (CDx) can be developed for predicting prognosis and treatment response of patients. Unfortunately, unprocessed large data lakes (e.g., TCGA) are not alone sufficient for pathomics, and often require an intractable amount of human curation effort in (i) performing meticulous quality control of WSI (i.e., avoid “garbage-in, garbage-out”) and subsequently (ii) precisely annotating (e.g., cell boundary) and labeling (e.g., cell type) histologic objects. To address these major limiting factors in curating data lakes, we propose developing our small-scale HistoTools prototypes to employ computing clusters and thus enable their function at the scale of large digital slide repositories (DSR): (i) HistoQC for robust, reproducible quality control of WSI by identifying artifacts (blurriness) and outliers (poorly stained slides) for avoidance in downstream analyses, (ii) CohortFinder for identification and compensation of batch affects, (iii) Quick Annotator for rapid computer aided annotation generation via a combination of active and machine learning, (iv) PatchSorter for improving sub-typing of histologic objects with machine learning. We will evaluate HistoTools for improvement of quality control and the efficiency of both segmenting and labeling histologic objects of interest via (a) onsite curation and release of the 14k WSI used during our internal validation and (b) supported external curation of at least 100k WSI via 24-clinical affiliates from every continent, except Antarctica, whom together have access to over 20 million WSI during this proposal. Our validation use cases are designed to expedite existing onsite projects in the CDx space, consisting of 4 organs (breast, lung, heart, kidney), 3 diseases (cancer, kidney disease, and organ rejection) and WSIs collected from >70 sites. These cohort characteristics will help ensure the generalizability of our tools for curated data lake creation, with open-source and usability study approaches employed to obtain feedback from collaborators and the larger research community. Dissemination through consortia (ITCR, NEPTUNE) and websites (Github, TCIA) will improve visibility and adoption. The tools and well-curated data sets we release are anticipated to bootstrap researcher-initiated CDx discovery projects, along with the creation of their own onsite manicured data lakes. Together, this proposal will engender digital pathology based precision medicine research.

摘要：随着全面幻灯片扫描仪的最新批准，用于主要诊断的玻璃 组织病理学载玻片被数字化并呈现给临床病理学家，以进行计算机监测器的诊断，这是一个 在常规临床实践中正在创建大量新的未开发数据，并将其放置在不断发展的数据湖泊中。在 数字格式，这些整个幻灯片图像（WSIS）可以遵守数字悲剧，即 提取与形态学，属性和组织学关系相关的定量图像特征 WSI中的对象。这些功能随后可以在许多领域中进行发现，例如 组织基因组学认为与生物学途径和基因相关的表型表现 本体论。此外，低成本非组织破坏性的基于图像的伴侣诊断测定（CDX） 可以开发用于预测患者的预后和治疗反应。不幸的是，未经处理的大 数据湖（例如，TCGA）并不足以使病原学，并且经常需要大量的人类 在（i）执行WSI细致质量控制（即避免“垃圾，垃圾”）和 随后（ii）精确注释（例如细胞边界）和标记（例如细胞类型）组织学对象。到 解决了策划数据湖泊的这些主要限制因素，我们建议开发小规模的Histotools 原型采用计算簇，从而在大型数字幻灯片的规模上启用其功能 存储库（DSR）：（i）通过识别工件（模糊），用于强大的，可再现的WSI的HistoQC 在下游分析中，（ii）识别的异常值（染色较差）以避免避免 （iii）快速计算机辅助注释生成生成的批处理影响和批次的补偿。 主动学习和机器学习的组合，（iv）补丁程序，用于改善组织学对象的亚型 机器学习。我们将评估组织工具的提高质量控制和两者的效率 通过（a）现场策划和使用的14K WSI的释放，分割和标记感兴趣的组织学对象 在我们的内部验证期间，（b）通过24-临床分支机构支持至少100K WSI的外部策划 除南极洲以外，每个大陆都可以在此提案中使用超过2000万个WSI。 我们的验证用例旨在加快CDX空间中现有的现有项目，包括4个 器官（乳房，肺，心脏，肾脏），3种疾病（癌症，肾脏疾病和器官排斥）以及收集的WSI 来自> 70个站点。这些队列特征将有助于确保我们的策划数据湖工具的普遍性 创建，采用开源和可用性研究方法来获得合作者的反馈和 更大的研究界。通过Consortia（ITCR，Neptune）和网站（Github，TCIA）传播 将提高可见性和采用。我们释放的工具和精心策划的数据集将被引导 研究人员引发的CDX发现项目，以及他们自己的现场修剪数据湖泊的创建。 该建议将共同提供基于数字病理学的精确医学研究。