Deep learning with whole slide images can improve the prognostic risk stratification with stage III colorectal cancer.

Journal: Computer methods and programs in biomedicine
Published Date: May 25, 2022

Abstract

BACKGROUND AND OBJECTIVE: Adjuvant chemotherapy is recommended as standard treatment for colorectal cancer (CRC) with stage III according to TNM stage. However, outcomes are varied even among patients receiving similar treatments. We aimed to develop a prognostic signature to stratify outcomes and benefit from different chemotherapy regimens by analyzing whole slide images (WSI) using deep learning.

Authors

  • Caixia Sun
    CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, China; Key Laboratory of Big Data-Based Precision Medicine, Ministry of Industry and Information Technology, Beihang University, Beijing, China.
  • Bingbing Li
    Department of Pathology, Nanfang Hospital and Basic Medical College, Southern Medical University, Guangzhou 510515, Guangdong Province, China; Guangdong Province Key Laboratory of Molecular Tumor Pathology, Guangzhou 510515, Guangdong Province, China.
  • Genxia Wei
    Department of Pathology, Nanfang Hospital and Basic Medical College, Southern Medical University, Guangzhou 510515, Guangdong Province, China; Guangdong Province Key Laboratory of Molecular Tumor Pathology, Guangzhou 510515, Guangdong Province, China.
  • Weihao Qiu
    Department of Pathology, Nanfang Hospital and Basic Medical College, Southern Medical University, Guangzhou 510515, Guangdong Province, China; Guangdong Province Key Laboratory of Molecular Tumor Pathology, Guangzhou 510515, Guangdong Province, China.
  • Danyi Li
    Department of Pathology, Nanfang Hospital and Basic Medical College, Southern Medical University, Guangzhou 510515, Guangdong Province, China; Guangdong Province Key Laboratory of Molecular Tumor Pathology, Guangzhou 510515, Guangdong Province, China.
  • Xiangzhao Li
    Department of Pathology, Nanfang Hospital and Basic Medical College, Southern Medical University, Guangzhou 510515, Guangdong Province, China; Guangdong Province Key Laboratory of Molecular Tumor Pathology, Guangzhou 510515, Guangdong Province, China.
  • Xiangyu Liu
    School of Pharmacy, Shenyang Medical College, Shenyang 110034, People's Republic of China.
  • Wei Wei
    Dept. Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
  • Shuo Wang
    College of Tea & Food Science, Anhui Agricultural University, Hefei, China.
  • Zhenyu Liu
    School of Electronic Information, Hangzhou Dianzi University, Hangzhou 310018, China.
  • Jie Tian
    CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
  • Li Liang
    Duke Clinical Research Institute, Duke University, Durham, North Carolina.

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