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: 10116983
• 负责人: Andrew Robert Janowczyk
• 金额: --
• 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/10116983
• 关键词: Active Learning; Address; Adoption; Algorithms; American; American Society of Clinical Oncology; Automobile Driving; 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; predicting response; prognostic; prototype; quantitative imaging; repository; response; tool; 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%的美国人将在其一生中被诊断出患有某种形式的癌症。在