Automated curation of large‐scale cancer histopathology image datasets using deep learning

Authors:
raw_author_name: Narmin Ghaffari Laleh
raw_affiliation_string: Department of Medicine III University Hospital RWTH Aachen Aachen Germany; Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus Technical University Dresden Dresden Germany
raw_author_name: Nicholas P West
raw_affiliation_string: Pathology &amp; Data Analytics Leeds Institute of Medical Research at St James's, University of Leeds Leeds UK
raw_author_name: Katherine J Hewitt
raw_affiliation_string: Department of Medicine III University Hospital RWTH Aachen Aachen Germany; Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus Technical University Dresden Dresden Germany
raw_author_name: Philip Quirke
raw_affiliation_string: Pathology &amp; Data Analytics Leeds Institute of Medical Research at St James's, University of Leeds Leeds UK
raw_author_name: Heike I Grabsch
raw_affiliation_string: Department of Pathology, GROW - Research Institute for Oncology and Reproduction Maastricht University Medical Center+ Maastricht The Netherlands; Pathology &amp; Data Analytics Leeds Institute of Medical Research at St James's, University of Leeds Leeds UK
raw_author_name: Chiara M L Loeffler
raw_affiliation_string: Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus Technical University Dresden Dresden Germany
raw_author_name: Titus J Brinker
raw_affiliation_string: Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ) Heidelberg Germany
raw_author_name: Tanwei Yuan
raw_affiliation_string: Division of Clinical Epidemiology and Aging Research German Cancer Research Center (DKFZ) Heidelberg Germany
raw_author_name: Hermann Brenner
raw_affiliation_string: Division of Clinical Epidemiology and Aging Research German Cancer Research Center (DKFZ) Heidelberg Germany; Division of Preventive Oncology German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT) Heidelberg Germany
raw_author_name: Alexander Brobeil
raw_affiliation_string: Institute of Pathology, University Hospital Heidelberg Heidelberg Germany; Tissue Bank National Center for Tumor Diseases (NCT), University Hospital Heidelberg Heidelberg Germany
raw_author_name: Michael Hoffmeister
raw_affiliation_string: Division of Clinical Epidemiology and Aging Research German Cancer Research Center (DKFZ) Heidelberg Germany
raw_author_name: Jakob Nikolas Kather
raw_affiliation_string: Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus Technical University Dresden Dresden Germany; Medical Oncology, National Center for Tumor Diseases (NCT) University Hospital Heidelberg Heidelberg Germany; Pathology &amp; Data Analytics Leeds Institute of Medical Research at St James's, University of Leeds Leeds UK

Abstract: Background Artificial intelligence (AI) has numerous applications in pathology, supporting diagnosis and prognostication in cancer. However, most AI models are trained on highly selected data, typically one tissue slide per patient. In reality, especially for large surgical resection specimens, dozens of slides can be available for each patient. Manually sorting and labelling whole‐slide images (WSIs) is a very time‐consuming process, hindering the direct application of AI on the collected tissue samples from large cohorts. In this study we addressed this issue by developing a deep‐learning (DL)‐based method for automatic curation of large pathology datasets with several slides per patient. Methods We collected multiple large multicentric datasets of colorectal cancer histopathological slides from the United Kingdom (FOXTROT, N = 21,384 slides; CR07, N = 7985 slides) and Germany (DACHS, N = 3606 slides). These datasets contained multiple types of tissue slides, including bowel resection specimens, endoscopic biopsies, lymph node resections, immunohistochemistry‐stained slides, and tissue microarrays. We developed, trained, and tested a deep convolutional neural network model to predict the type of slide from the slide overview (thumbnail) image. The primary statistical endpoint was the macro‐averaged area under the receiver operating curve (AUROCs) for detection of the type of slide. Results In the primary dataset (FOXTROT), with an AUROC of 0.995 [95% confidence interval [CI]: 0.994–0.996] the algorithm achieved a high classification performance and was able to accurately predict the type of slide from the thumbnail image alone. In the two external test cohorts (CR07, DACHS) AUROCs of 0.982 [95% CI: 0.979–0.985] and 0.875 [95% CI: 0.864–0.887] were observed, which indicates the generalizability of the trained model on unseen datasets. With a confidence threshold of 0.95, the model reached an accuracy of 94.6% (7331 classified cases) in CR07 and 85.1% (2752 classified cases) for the DACHS cohort. Conclusion Our findings show that using the low‐resolution thumbnail image is sufficient to accurately classify the type of slide in digital pathology. This can support researchers to make the vast resource of existing pathology archives accessible to modern AI models with only minimal manual annotations.