111997 20240219135522.0 doi 10.1016/j.compbiomed.2021.104547 sideral 126219 ART-2021-126219 eng Pérez-Aliacar, M. Universidad de Zaragoza (orcid)0000-0002-7909-4446 Predicting cell behaviour parameters from glioblastoma on a chip images. A deep learning approach 2021 Access copy available to the general public Unrestricted The broad possibilities offered by microfluidic devices in relation to massive data monitoring and acquisition open the door to the use of deep learning technologies in a very promising field: cell culture monitoring. In this work, we develop a methodology for parameter identification in cell culture from fluorescence images using Convolutional Neural Networks (CNN). We apply this methodology to the in vitro study of glioblastoma (GBM), the most common, aggressive and lethal primary brain tumour. In particular, the aim is to predict the three parameters defining the go or grow GBM behaviour, which is determinant for the tumour prognosis and response to treatment. The data used to train the network are obtained from a mathematical model, previously validated with in vitro experimental results. The resulting CNN provides remarkably accurate predictions (Pearson''s ¿ > 0.99 for all the parameters). Besides, it proves to be sound, to filter noise and to generalise. After training and validation with synthetic data, we predict the parameters corresponding to a real image of a microfluidic experiment. The obtained results show good performance of the CNN. The proposed technique may set the first steps towards patient-specific tools, able to predict in real-time the tumour evolution for each particular patient, thanks to a combined in vitro-in silico approach. © 2021 The Author(s) info:eu-repo/grantAgreement/ES/MICINN/PGC2018-097257-B-C31 info:eu-repo/grantAgreement/ES/MICINN/PID2019-106099RB-C44 info:eu-repo/semantics/openAccess by-nc-nd http://creativecommons.org/licenses/by-nc-nd/3.0/es/ 6.698 2021 BIOLOGY 13 / 94 = 0.138 2021 Q1 T1 MATHEMATICAL & COMPUTATIONAL BIOLOGY 6 / 57 = 0.105 2021 Q1 T1 ENGINEERING, BIOMEDICAL 22 / 98 = 0.224 2021 Q1 T1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 24 / 112 = 0.214 2021 Q1 T1 1.309 2021 Health Informatics 2021 Q1 Computer Science Applications 2021 Q1 8.2 2021 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Doweidar, M.H. Universidad de Zaragoza (orcid)0000-0003-0088-7222 Doblaré, M. Universidad de Zaragoza (orcid)0000-0001-8741-6452 Ayensa-Jiménez, J. Universidad de Zaragoza (orcid)0000-0003-2564-6038 5004 605 Universidad de Zaragoza Dpto. Ingeniería Mecánica Área Mec.Med.Cont. y Teor.Est. 135 (2021), 104547 [11 pp.] Comput. biol. med. Computers in biology and medicine 0010-4825 3030685 http://zaguan.unizar.es/record/111997/files/texto_completo.pdf Versión publicada 2578116 http://zaguan.unizar.es/record/111997/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:111997 articulos driver 2024-02-19-13:52:25 ARTICLE