A large histological images dataset of gastric cancer with tumour microenvironment annotation for AI.

Journal: Scientific data
PMID: 39843474

Abstract

Gastric cancer (GC) is the third leading cause of cancer death worldwide. Its clinical course varies considerably due to the highly heterogeneous tumour microenvironment (TME). Decomposing the complex TME from histological images into its constituent parts is crucial for evaluating its patterns and enhancing GC therapies. Although various deep learning methods were developed in medical field, their applications on this task are hindered by the lack of well-annotated histological images of GC. Through this work, we seek to provide a large database of histological images of GC completely annotated for 8 tissue classes in TME. The dataset consists of nearly 31 K histological images from 300 whole slide images. Additionally, we explained two deep learning models used as validation examples using this dataset.

Authors

  • Shenghan Lou
    Department of Oncology Surgery, Harbin Medical University Cancer Hospital, No.150 Haping Road, Harbin, Heilongjiang, 150081, China.
  • Jianxin Ji
    Department of Biostatistics, School of Public Health, Harbin Medical University, Harbin 150081, China.
  • Huiying Li
    Jilin University, College of Computer Science and Technology, Changchun, Jilin Province, China; Key Laboratory of Symbol Computation and Knowledge Engineering, Jilin University, Changchun 130012, China. Electronic address: lihuiying@jlu.edu.cn.
  • Xuan Zhang
  • Yang Jiang
    Department of Ophthalmology Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences Beijing People's Republic of China.
  • Menglei Hua
    Department of Biostatistics, School of Public Health, Harbin Medical University, Harbin, 150081, China.
  • Kexin Chen
    Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.
  • Kaiyuan Ge
    Department of Biostatistics, School of Public Health, Harbin Medical University, Harbin, 150081, China.
  • Qi Zhang
    Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Liuying Wang
  • Peng Han
    Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao, Qingdao, China.
  • Lei Cao
    State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning, People's Republic of China. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, Liaoning, People's Republic of China. University of Chinese Academy of Sciences, Beijing, People's Republic of China.

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