Deep learning-based methods for classification of microsatellite instability in endometrial cancer from HE-stained pathological images.

Journal: Journal of cancer research and clinical oncology
PMID: 37150803

Abstract

BACKGROUND: Microsatellite instability (MSI) is one of the essential tumor biomarkers for cancer treatment and prognosis. The presence of more significant PD-L1 expression on the surface of tumor cells in endometrial cancer with MSI suggests that MSI may be a promising biomarker for anti-PD-1/PD-L1 immunotherapy. However, the conventional testing methods are labor-intensive and expensive for patients.

Authors

  • Ying Zhang
    Department of Nephrology, Nanchong Central Hospital Affiliated to North Sichuan Medical College, Nanchong, China.
  • Shijie Chen
    Department of Spine Surgery, The Third Xiangya Hospital of Central South University, 138 Tongzipo Rd, Changsha, 410013, Hunan, China. shijiechencsu@csu.edu.cn.
  • Yuling Wang
    a Marine College Shandong University (weihai) , Shandong , China .
  • Jingjing Li
    Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China.
  • Kai Xu
    Department of Anesthesiology, Huai'an Hospital Affiliated to Yangzhou University (The Fifth People's Hospital of Huai'an), Huaian, China.
  • Jyhcheng Chen
    Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
  • Jie Zhao
    Department of Liver & Gallbladder Surgery, The First People's Hospital, Shangqiu, Henan, China.

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