Deep learning for fine-grained molecular-based colorectal cancer classification.

Journal: Translational cancer research
Published Date: May 30, 2025

Abstract

BACKGROUND: Colorectal cancer (CRC) is one of the most common malignancies globally and a major cause of cancer-related deaths. In the molecular diagnosis of CRC, microsatellite instability (MSI) status and mutations in genes such as , , and are important molecular markers. Traditional molecular detection methods are costly and time-consuming. Therefore, this study proposes a fine-grained classification method for CRC based on hematoxylin and eosin (H&E) stained tissue section images combined with deep learning (DL) technology, aiming to provide new insights into the molecular diagnosis of CRC.

Authors

  • Junyu Bian
    The First School of Clinical Medicine, Lanzhou University, Lanzhou, China.
  • Yansong Li
    State Key Laboratory for Diagnosis and Treatment of Severe Zoonotic Infectious Diseases, Key Laboratory for Zoonosis Research of the Ministry of Education, Institute of Zoonosis, and College of Veterinary Medicine, Jilin University, Changchun 130062, China.
  • Yamei Dang
    Department of Pathology, Gansu Provincial Hospital, Lanzhou, China.
  • Yonglin Chen
    Department of Pathology, The First Hospital of Lanzhou University, Lanzhou, China.

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