Automated evaluation of tumor spheroid behavior in 3D culture using deep learning-based recognition. (May 2021)

• Record Type: Journal Article
• Authors: Chen, Zaozao; Ma, Ning; Sun, Xiaowei; Li, Qiwei; Zeng, Yi; Chen, Fei; Sun, Shiqi; Xu, Jun; Zhang, Jing; Ye, Huan; Ge, Jianjun; Zhang, Zheng; Cui, Xingran; Leong, Kam; Chen, Yang; Gu, Zhongze
• Is Part Of: Biomaterials. Volume 272(2021)
• Publication Date: 2021-05
• Subjects: Cancer invasiveness -- 3D culture -- Microphysiological System -- Deep learning; Biomedical materials -- Periodicals; Biocompatible Materials -- Periodicals; Biomatériaux -- Périodiques; 610.28
• DOI: 10.1016/j.biomaterials.2021.120770
• Languages: English
• ISSNs: 0142-9612

Abstract

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. Graphical abstract: Image 1