ConvPath: A software tool for lung adenocarcinoma digital pathological image analysis aided by a convolutional neural network.

Journal: EBioMedicine
Published Date:

Abstract

BACKGROUND: The spatial distributions of different types of cells could reveal a cancer cell's growth pattern, its relationships with the tumor microenvironment and the immune response of the body, all of which represent key "hallmarks of cancer". However, the process by which pathologists manually recognize and localize all the cells in pathology slides is extremely labor intensive and error prone.

Authors

  • Shidan Wang
    Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, 5325 Harry Hines Blvd, Dallas, TX, 75390, USA.
  • Tao Wang
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Lin Yang
    National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, China.
  • Donghan M Yang
    Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas.
  • Junya Fujimoto
    Department of Translational Molecular Pathology, University of Texas MD Anderson Cancer Center, Houston, TX.
  • Faliu Yi
    Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, 5325 Harry Hines Blvd, Dallas, TX, 75390, USA.
  • Xin Luo
    Department of Pharmacology, The Basic Medical Sciences College of Xinjiang Medical University Urumqi 830054, China.
  • Yikun Yang
    Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui 230027, People's Republic of China.
  • Bo Yao
    Institute of Microanalytical Systems, Department of Chemistry, Zhejiang University, Hangzhou, 310058, China.
  • ShinYi Lin
    Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX.
  • Cesar Moran
    Department of Pathology, Division of Pathology/Lab Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX.
  • Neda Kalhor
    Department of Pathology, Division of Pathology/Lab Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX.
  • Annikka Weissferdt
    Department of Pathology, Division of Pathology/Lab Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX.
  • John Minna
    Hamon Center for Therapeutic Oncology Research, Department of Internal Medicine and Department of Pharmacology, University of Texas Southwestern Medical Center, Dallas, TX; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX.
  • Yang Xie
    Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, 5325 Harry Hines Blvd, Dallas, TX, 75390, USA.
  • Ignacio I Wistuba
    Department of Translational Molecular Pathology, University of Texas MD Anderson Cancer Center, Houston, TX.
  • Yousheng Mao
    Department of Thoracic Surgery, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences (CHCAMS), China.
  • Guanghua Xiao