Automated evaluation of tumor spheroid behavior in 3D culture using deep learning-based recognition.

Journal: Biomaterials
PMID: 33798957

Abstract

Three-dimensional in vitro tumor models provide more physiologically relevant responses to drugs than 2D models, but the lack of proper evaluation indices and the laborious quantitation of tumor behavior in 3D have limited the use of 3D tumor models in large-scale preclinical drug screening. Here we propose two indices of 3D tumor invasiveness-the excess perimeter index (EPI) and the multiscale entropy index (MSEI)-and combine these indices with a new convolutional neural network-based algorithm for tumor spheroid boundary detection. This new algorithm for 3D tumor boundary detection and invasiveness analysis is more accurate than any other existing algorithms. We apply this spheroid monitoring and AI-based recognition technique ("SMART") to evaluating the invasiveness of tumor spheroids grown from tumor cell lines and from primary tumor cells in 3D culture.

Authors

  • Zaozao Chen
    State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu, 210096, China; Institute of Biomaterials and Medical Devices, Southeast University, Suzhou, Jiangsu, 215163, China.
  • Ning Ma
    Key Laboratory of Preparation and Applications of Environmental Friendly Materials (Jilin Normal University), Ministry of Education, Changchun 130103, PR China.
  • Xiaowei Sun
    Heilongjiang University of Chinese Medicine, 24 Heping Road, Xiangfang District, Harbin, China 8615-0040.
  • Qiwei Li
    Department of General Surgery, South Campus, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China.
  • Yi Zeng
    Department of Geriatrics, Second Xiangya Hospital, Central South University, Changsha, 410011, Hunan, China.
  • Fei Chen
    Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China.
  • Shiqi Sun
    Institute of Biomaterials and Medical Devices, Southeast University, Suzhou, Jiangsu, 215163, China.
  • Jun Xu
    Department of Nephrology, The Affiliated Baiyun Hospital of Guizhou Medical University, Guizhou, China.
  • Jing Zhang
    MOEMIL Laboratory, School of Optoelectronic Information, University of Electronic Science and Technology of China, Chengdu, China.
  • Huan Ye
    Institute of Biomaterials and Medical Devices, Southeast University, Suzhou, Jiangsu, 215163, China.
  • Jianjun Ge
    Institute of Biomaterials and Medical Devices, Southeast University, Suzhou, Jiangsu, 215163, China.
  • Zheng Zhang
    Key Laboratory of Sustainable and Development of Marine Fisheries, Ministry of Agriculture and Rural Affairs, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao, PR China.
  • Xingran Cui
    State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu, 210096, China.
  • Kam Leong
    Department of Biomedical Engineering, Columbia University, New York, NY, 10032, USA.
  • Yang Chen
    Orthopedics Department of the First Affiliated Hospital of Tsinghua University, Beijing, China.
  • Zhongze Gu
    State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu, 210096, China; Institute of Biomaterials and Medical Devices, Southeast University, Suzhou, Jiangsu, 215163, China. Electronic address: gu@seu.edu.cn.

Keywords

External Resources

Popular Topics
Recent Journals