HistoTools: A suite of digital pathology tools for quality control, annotation and dataset identification

HistoTools：一套用于质量控制、注释和数据集识别的数字病理学工具

• 批准号: 10392854
• 负责人: Andrew Robert Janowczyk
• 金额: $ 28.1万
• 依托单位: CASE WESTERN RESERVE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-20 至 2023-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10392854
• 关键词: Active Learning; Address; Adoption; Algorithms; American; American Society of Clinical Oncology; Automobile Driving; Cancer Detection; Cancer Patient; Cell Nucleus; Characteristics; Classification; Clinical; Clinical Pathology; Clinical Research; Clinical Trials; Collection; Communities; Computer Assisted; Data; Data Set; Detection; Development; Diagnosis; Eastern Cooperative Oncology Group; Employment; Ensure; Environment; Estrogen receptor positive; Evaluation; Feedback; Generations; Histologic; Histology; Image; Immunotherapy; International; Label; Learning; Lymphocyte; Machine Learning; Malignant Neoplasms; Malignant neoplasm of lung; Manuals; Masks; Mitosis; Modeling; Morphologic artifacts; Morphology; Nature; Nivolumab; Noise; Non-Small-Cell Lung Carcinoma; Nuclear; Oncology; Optics; Outcome; Paper; Pathologist; Patients; Performance; Population; Preparation; Process; Prognosis; Quality Control; Recurrence; Reproducibility; Research; Role; Scanning; Slide; Societies; Stains; Technology; Testing; The Cancer Imaging Archive; Time; Tissues; Training; Tumor-Infiltrating Lymphocytes; Validation; Visualization; Weight; Work; anticancer research; base; cancer diagnosis; cancer recurrence; cohort; combat; companion diagnostics; deep learning; design; diagnostic assay; digital; digital pathology; experimental study; heterogenous data; high resolution imaging; imaging informatics; improved; indexing; industry partner; innovation; interactive tool; interest; large datasets; learning network; malignant breast neoplasm; open source; open source tool; outcome prediction; pathology imaging; photonics; precision medicine; predicting response; prognostic; prototype; quantitative imaging; repository; response; tool; treatment response; tumor heterogeneity

ABSTRACT: Roughly 40% of the US population will be diagnosed with some form of cancer in their lifetime. In a majority of these cases, a definitive cancer diagnosis is only possible via histopathologic confirmation using a tissue slide. Increasingly, these slides are being digitally scanned as high-resolution images for usage in both clinical and research digital pathology (DP) workflows. Our group has been pioneering the use of deep learning (DL), a form of machine learning, for segmentation, detection, and classification of various cancers using digital pathology images. DL learns features and their associated weighting from large datasets to maximally discriminate between user labeled data (e.g., cancer vs non-cancer, nuclei vs non-nuclei); a paradigm known as “learn from data”. Unfortunately, this paradigm makes DL especially sensitive to low quality slides, noise induced by small errors in the manual user labeling process, and general dataset heterogeneity. As many groups do not intentionally account for these problems, they learn that successful employment of DL technologies relies heavily on explicitly addressing challenges associated with (a) carefully curating high quality slides without preparation or scanning artifacts, (b) obtaining a large precise collection of annotations delineating objects of interest, and (c) selecting diverse datasets to ensure robust classifier performance when clinically deploying the model. To address these challenges we propose HistoTools, a suite of three modules or “Apps”: (1) HistoQC examines slides for artifacts and computes metrics associated with slide presentation characteristics (e.g., stain intensity, compression levels) helping to quantify ranges of acceptable characteristics for downstream algorithmic evaluation. (2) HistoAnno drastically improves the efficiency of annotation efforts using a combined active learning and deep learning approach to ensure experts focus only on regions which are important for classifier improvement. (3) HistoFinder aids in selecting suitable training and test cohorts to guarantee that various tissue level characteristics are well balanced, leading to increased reproducibility. Our team already has working prototypes of HistoQC (100% concordance with a pathologist, evaluated on n>1200 slides) and HistoAnno (30% efficiency improvement during annotation tasks). In this U01, we seek to further develop and evaluate HistoTools in the context of enhancing two companion diagnostic (CDx) assays being developed in our group. First, we will use HistoTools to quality control and annotate nuclei, tubules, and mitosis for improving our CDx classifier for predicting recurrence in breast cancers using a cohort of n>900 patients from completed trial ECOG 2197. Secondly, HistoTools will be employed for quality control and identification of tumor infiltrating lymphocytes and cancer nuclei towards improving our CDx classifier for predicting response to immunotherapy in lung cancer using the n>700 patients from completed clinical trials Checkmate 017 and 057. These tools will build on our existing open source tool repository to aid in real-time feedback and dissemination throughout the ITCR and cancer research community.

摘要：大约40%的美国人在有生之年会被诊断出患有某种癌症。在……里面 在这些病例中，只有通过组织病理学证实才有可能做出明确的癌症诊断 组织滑块。这些幻灯片越来越多地被数字化扫描为高分辨率图像，以便在两种情况下使用 临床和研究数字病理学(DP)工作流。我们的团队一直在率先使用深度学习 (DL)，一种机器学习形式，用于使用数字分割、检测和分类各种癌症 病理图像。动态链接库从大数据集中学习要素及其关联权重，以最大限度地 区分用户标记的数据(例如，癌症与非癌症、核与非核)；一种已知的范例 “从数据中学习”。不幸的是，这种模式使数字图书馆对低质量的幻灯片、噪音特别敏感 由于手动用户标记过程中的小错误和一般数据集的异构性而引起的。同样多的人 这些问题并不是团体故意造成的，他们了解到成功就业的数字图书馆 技术在很大程度上依赖于明确地解决与(A)仔细策划高 无需准备或扫描伪影即可获得高质量的幻灯片，(B)获得大量精确的注释集合 描述感兴趣的对象，以及(C)选择不同的数据集以确保在以下情况下稳健的分类器性能 在临床上部署该模型。为了应对这些挑战，我们提出了一套由三个模块组成的历史工具 或“应用程序”：(1)HistoQC检查幻灯片中的人工制品，并计算与幻灯片演示相关的指标 特征(例如，污渍强度、压缩程度)有助于量化可接受的范围 用于下游算法评估的特征。(2)OrganoAnno极大地提高了工作效率 使用主动学习和深度学习相结合的方法进行注释工作，以确保专家只关注 关于对分类器改进很重要的区域。(3)HistoFinder帮助选择合适的培训 和测试队列，以确保各种组织水平特征得到很好的平衡，从而导致 再现性。我们的团队已经有了组织质量控制的工作原型(与病理学家100%一致， 在n&gt；1200张幻灯片上进行了评估)和HistoAnno(在批注任务中效率提高了30%)。在这个U01中， 我们寻求在增强两个配套诊断的背景下进一步开发和评估组织工具 (CDX)检测在我们组正在进行中。首先，我们将使用组织工具对细胞核进行质量控制和注释， 小管和有丝分裂用于改进我们的CDX分类器，使用队列预测乳腺癌的复发 在完成试验ECOG 2197的900名患者中。其次，将使用OrganoTools进行质量控制 和肿瘤浸润性淋巴细胞和癌核的识别，以改进我们的CDX分类器 利用已完成的临床试验中的700名患者预测肺癌免疫治疗的反应 将死017和057。这些工具将构建在我们现有的开源工具库之上，以实时提供帮助 在ITCR和癌症研究社区的反馈和传播。

项目成果

Update on the current opinion, status and future development of digital pathology in Switzerland in light of COVID-19.

• DOI: 10.1136/jclinpath-2021-207768
• 发表时间: 2021-09-13
• 期刊: Journal of clinical pathology
• 影响因子: 3.4
• 作者: Koelzer VH;Grobholz R;Zlobec I;Janowczyk A;Swiss Digital Pathology Consortium (SDiPath)
• 通讯作者: Swiss Digital Pathology Consortium (SDiPath)

Current opinion, status and future development of digital pathology in Switzerland.

• DOI: 10.1136/jclinpath-2019-206155
• 发表时间: 2020-06
• 期刊: Journal of clinical pathology
• 影响因子: 3.4