Deep learning image analysis quantifies tumor heterogeneity and identifies microsatellite instability in colon cancer.

Journal: Journal of surgical oncology
PMID: 36251352

Abstract

BACKGROUND AND OBJECTIVES: Deep learning utilizing convolutional neural networks (CNNs) applied to hematoxylin & eosin (H&E)-stained slides numerically encodes histomorphological tumor features. Tumor heterogeneity is an emerging biomarker in colon cancer that is, captured by these features, whereas microsatellite instability (MSI) is an established biomarker traditionally assessed by immunohistochemistry or polymerase chain reaction.

Authors

  • Jill C Rubinstein
    The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, USA.
  • Ali Foroughi Pour
    The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.
  • Jie Zhou
    Departments of Ultrasound, Jiading District Central Hospital Affiliated Shanghai University of Medicine &Health Sciences, Shanghai, China.
  • Todd B Sheridan
    The Jackson Laboratory for Genomic Medicine, 10 Discovery Dr., Farmington, CT, 06032, USA.
  • Brian S White
    The Jackson Laboratory for Genomic Medicine, 10 Discovery Dr., Farmington, CT, 06032, USA.
  • Jeffrey H Chuang
    The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA. Jeff.Chuang@jax.org.

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