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.
School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu, 210096, China.
Biomaterials. 2021 May;272:120770. doi: 10.1016/j.biomaterials.2021.120770. Epub 2021 Mar 22.
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.
三维体外肿瘤模型比 2D 模型更能提供生理相关的药物反应，但由于缺乏适当的评估指标和 3D 中肿瘤行为的繁琐定量，限制了 3D 肿瘤模型在大规模临床前药物筛选中的应用。在这里，我们提出了两个 3D 肿瘤侵袭性的指标-过剩周长指数（EPI）和多尺度熵指数（MSEI）-并将这些指标与一种新的基于卷积神经网络的肿瘤球体边界检测算法相结合。这种新的 3D 肿瘤边界检测和侵袭性分析算法比任何其他现有的算法都更准确。我们将这种球体监测和基于人工智能的识别技术（"SMART"）应用于评估从肿瘤细胞系和 3D 培养的原发性肿瘤细胞生长的肿瘤球体的侵袭性。
