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：随着最近批准用于初步诊断的全载玻片扫描仪，其中常规玻璃 组织病理学切片被数字化并呈现给临床病理学家，以便在计算机监视器上进行诊断， 在常规临床实践中正在创建大量新的未开发数据，并将其放入不断增长的数据湖中。在 数字格式，这些全载玻片图像（WSI）可以进行数字病理组学，即，的过程 提取与组织学的形态、属性和关系相关的定量图像特征， WSIs中的对象。这些特征随后可用于许多领域中的发现， 组织基因组学，它将表型表现与生物学途径和基因相关联， 本体论此外，低成本的非组织破坏性的基于图像的伴随诊断测定（CDx） 可用于预测患者的预后和治疗反应。不幸的是，未经处理的大型 数据湖（例如，TCGA）不足以单独用于病理组学，并且通常需要难以处理的量的人免疫组化。 策展工作（i）对WSI进行细致的质量控制（即，避免“垃圾进，垃圾出”）和 随后（ii）精确地注释（例如，小区边界）和标记（例如，细胞类型）组织学对象。到 为了解决这些主要的限制因素，我们建议开发我们的小规模HistoTools 原型采用计算集群，从而使其功能在大型数字幻灯片的规模 数据库（DSR）：（i）HistoQC，通过识别伪影（模糊）对WSI进行稳健、可重现的质量控制 和离群值（染色不良的载玻片），以避免下游分析，（ii）用于识别的队列 和补偿批量影响，（iii）快速注释器，用于快速计算机辅助注释生成， 主动和机器学习的组合，（iv）PatchSorter用于改善组织学对象的子分型， 机器学习我们将评估HistoTools以改进质量控制和两者的效率 通过（a）现场管理和发布所使用的14k WSI来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个临床分支机构支持至少10万个WSI的外部管理 来自除南极洲以外的各大洲的人，在本提案期间，他们总共可以获得超过2000万份WSI。 我们的验证用例旨在加快CDx领域的现有现场项目，包括4个 收集的器官（乳腺、肺、心脏、肾脏）、3种疾病（癌症、肾脏疾病和器官排斥）和WSI >70个网站这些群组特征将有助于确保我们的工具在策展数据湖中的通用性 创建，采用开源和可用性研究方法，以获得合作者的反馈， 更大的研究社区。通过联合会（ITCR、NEPTUNE）和网站（Github、TCIA）传播 将提高知名度和采用率。我们发布的工具和精心策划的数据集有望引导 研究人员发起的CDx发现项目，沿着他们自己的现场修剪数据湖的创建。 总之，这项提案将引发基于数字病理学的精准医学研究。