Assuring AI/ML-readiness of digital pathology in diverse existing and emerging multi-omic datasets through quality control workflows

通过质量控制工作流程，确保现有和新兴的多组学数据集中数字病理学的 AI/ML 就绪性

• 批准号: 10841333
• 负责人: Julie Ann Bletz
• 金额: $ 27万
• 依托单位: SAGE BIONETWORKS
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-14 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10841333
• 关键词: Adoption; Area; Artificial Intelligence; Basic Science; Biomedical Research; Biopsy; Classification; Clinical; Clinical Research; Collaborations; Data; Data Commons; Data Set; Deposition; Development; Diagnosis; Division of Cancer Biology; Drops; Ensure; Evaluation; Exclusion; FAIR principles; Failure; Funding; Future; Genotype; Genotype-Tissue Expression Project; Grant; Health Resources; Histology; Image; Inferior; Infrastructure; Learning; Link; Machine Learning; Manuals; Masks; Modeling; Morphologic artifacts; Morphology; Multiomic Data; National Cancer Institute; Nephrotic Syndrome; Output; Performance; Play; Process; Prognosis; Quality Control; Reader; Readiness; Reporting; Reproducibility; Research; Research Personnel; Role; Sampling; Science; Slide; Synapses; Techniques; The Cancer Genome Atlas; Time; Tissues; Training; Translational Research; Trust; United States National Institutes of Health; Universities; Validation; Work; anticancer research; biomarker discovery; cohort; comparative; cost; data management; data mining; data quality; data sharing; deep learning; digital pathology; generalist repository; histological image; imaging biomarker; imaging detection; improved; interest; machine learning method; machine learning model; multiple omics; open data; open source; parent grant; programs; prospective; prototype; public repository; repository; success; tool; treatment response; whole slide imaging

Abstract In an era of multi-omics, histology remains an essential approach for basic, translational, and clinical research providing valuable, low-cost, and non-destructive information about tissue morphology. The adoption of whole slide imaging (WSI) and digital pathology (DP) has led to large clinical and research repositories being instantiated for computational data mining of image-based biomarkers associated with genotype, diagnosis, prognosis, and therapy response. Importantly, data quality plays a critical role in the usage of these WSI, especially when employing artificial intelligence (AI) and machine learning (ML) methods. Artifacts and batch effects may arise at many points in the process from biopsy to digitization, and while several tools to detect them have been developed, consistent application and reporting are lacking, with none being routinely applied in public repositories. This leaves a unique opportunity to immediately provide added value to existing and future NIH- supported datasets. This proposal sees a collaboration between Sage Bionetworks, experts in FAIR data sharing and Team Science, and Dr. Andrew Janowczyk, a leader in automated quality control (QC) of WSI who has spearheaded the development of an open-source DP QC tool, HistoQC. We propose to enhance the AI/ML readiness of existing and future DP data by providing transparent, reproducible, reporting of detected imaging artifacts and batch effects within NIH-sponsored datasets in an automated fashion via the extension of our existing QC workflows. Implementing transparent reporting of DP data quality will enable researchers to exclude artifacts from their training sets in a consistent cross-investigator manner. Our work will provide greater trust in dataset reuse and experimental reproducibility while also easing AI/ML model creation and enhancing their performance. We will build on strong preliminary data and prototypes, demonstrating both significantly improved cross-reader QC reproducibility and technical feasibility, with three specific aims. Aim 1 sees this enrichment process will be applied to WSI from NIH-supported public datasets, including TCGA and GTEx, and for NIH/NCI Division of Cancer Biology research programs supported by the Multi-Consortia Coordinating (MC2) Center parent grant. Aim 2 employs the lessons learned from the enhancement of raw DP data to be AI/ML ready in Aim 1 to deploy a scalable workflow for QC of all incoming DP data from MC2-supported programs, providing continual prospective data enrichment to assure AI/ML readiness. Lastly, Aim 3 demonstrates enhanced AI/ML readiness of DP data subjected to our automated QC processes using a prototypical self-supervised tissue classification task. Our deliverables include (a) 5000 WSI annotated by our QC workflow and enhanced into AI/ML ready datasets; (b) workflows to enable processing of incoming datasets for AI-readiness, (c) a failure rate of identifying poor quality slides is <1%; and (d) our QC comparative AI/ML demonstration yields an improvement of >10% performance in terms of tissue classification performance as a result of our data enhancements.

抽象的 在一个多词的时代，组织学仍然是基础，转化和临床研究的重要方法 提供有关组织形态的有价值，低成本和非破坏性信息。整体的采用 幻灯片成像（WSI）和数字病理学（DP）导致大量的临床和研究存储库是 实例化了与基因型，诊断，基于图像的生物标志物的计算数据挖掘 预后和治疗反应。重要的是，数据质量在这些WSI的使用中起着至关重要的作用， 特别是在采用人工智能（AI）和机器学习（ML）方法时。文物和批处理 从活检到数字化的过程中，效果可能在许多方面产生，而几种用于检测它们的工具 已经开发了，缺乏一致的应用和报告，没有例行应用于公众 存储库。这留下了一个独特的机会，可以立即为现有和未来的NIH提供附加价值 支持的数据集。该建议看到Sage Bionetworks（公平数据共享专家）之间的合作 和团队科学，以及WSI自动化质量控制（QC）的领导者Andrew Janowczyk博士 带领开源DP QC工具HistoQC的开发。我们建议增强AI/ML 通过提供透明，可重复的报告，对现有和未来的DP数据的准备就绪 通过扩展我们 现有的QC工作流程。实施DP数据质量的透明报告将使研究人员排除 他们的训练集以一致的交叉评估方式进行。我们的工作将提供更大的信任 数据集的重复使用和实验性可重复性，同时还可以轻松创建AI/ML模型并增强其 表现。我们将建立在强大的初步数据和原型的基础上，证明两者都得到了显着改善 跨阅读器QC的可重复性和技术可行性，具有三个具体目标。 AIM 1看到这个丰富 流程将从NIH支持的公共数据集（包括TCGA和GTEX）以及NIH/NCI中应用于WSI 癌症生物学研究计划的部门由多康斯比特协调（MC2）中心支持 父母赠款。 AIM 2采用从原始DP数据的增强中学到的经验教训，以准备AI/ML 目的1以从MC2支持的程序中为所有传入的DP数据部署可扩展的工作流程，提供 持续的前瞻性数据丰富，以确保AI/ML准备就绪。最后，AIM 3展示了增强的AI/ML 使用典型的自我监视组织进行自动化QC过程的DP数据准备就绪 分类任务。我们的可交付成果包括（a）5000 WSI，由我们的QC工作流注释，并增强到 AI/ML就绪数据集； （b）工作流程以启用传入数据集以进行AI RERVINESS，（c）失败率 识别质量差的幻灯片<1％； （d）我们的QC比较AI/ML演示得出的改善 由于我们的数据增强，在组织分类性能方面的性能> 10％。