Deep learning identifies inflamed fat as a risk factor for lymph node metastasis in early colorectal cancer.

Journal: The Journal of pathology
Published Date: Mar 1, 2022

Abstract

The spread of early-stage (T1 and T2) adenocarcinomas to locoregional lymph nodes is a key event in disease progression of colorectal cancer (CRC). The cellular mechanisms behind this event are not completely understood and existing predictive biomarkers are imperfect. Here, we used an end-to-end deep learning algorithm to identify risk factors for lymph node metastasis (LNM) status in digitized histopathology slides of the primary CRC and its surrounding tissue. In two large population-based cohorts, we show that this system can predict the presence of more than one LNM in pT2 CRC patients with an area under the receiver operating curve (AUROC) of 0.733 (0.67-0.758) and patients with any LNM with an AUROC of 0.711 (0.597-0.797). Similarly, in pT1 CRC patients, the presence of more than one LNM or any LNM was predictable with an AUROC of 0.733 (0.644-0.778) and 0.567 (0.542-0.597), respectively. Based on these findings, we used the deep learning system to guide human pathology experts towards highly predictive regions for LNM in the whole slide images. This hybrid human observer and deep learning approach identified inflamed adipose tissue as the highest predictive feature for LNM presence. Our study is a first proof of concept that artificial intelligence (AI) systems may be able to discover potentially new biological mechanisms in cancer progression. Our deep learning algorithm is publicly available and can be used for biomarker discovery in any disease setting. © 2021 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Authors

  • Scarlet Brockmoeller
    Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK.
  • Amelie Echle
    Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany.
  • Narmin Ghaffari Laleh
    Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany.
  • Susanne Eiholm
    Department of Pathology, Zealand University Hospital, University of Copenhagen, Roskilde, Denmark.
  • Marie Louise Malmstrøm
    Department of Surgery, Nordsjaellands Hospital, Hillerod, Denmark.
  • Tine Plato Kuhlmann
    Department of Pathology, Herlev University Hospital, Copenhagen, Denmark.
  • Katarina Levic
    Department of Surgery, Herlev University Hospital, Copenhagen, Denmark.
  • Heike Irmgard Grabsch
    Department of Pathology, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, The Netherlands; Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom.
  • Nicholas P West
    Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom.
  • Oliver Lester Saldanha
    Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany.
  • Katerina Kouvidi
    Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK.
  • Aurora Bono
    Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK.
  • Lara R Heij
    Institute of Pathology, University Hospital RWTH Aachen, Aachen, Germany.
  • Titus J Brinker
    National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany.
  • Ismayil Gögenür
    Department of Surgery, Zealand University Hospital, University of Copenhagen, Køge, Denmark.
  • Philip Quirke
    Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom.
  • Jakob Nikolas Kather
    Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany.

Keywords

External Resources

Popular Topics
Recent Journals