000111997 001__ 111997
000111997 005__ 20240219135522.0
000111997 0247_ $$2doi$$a10.1016/j.compbiomed.2021.104547
000111997 0248_ $$2sideral$$a126219
000111997 037__ $$aART-2021-126219
000111997 041__ $$aeng
000111997 100__ $$0(orcid)0000-0002-7909-4446$$aPérez-Aliacar, M.$$uUniversidad de Zaragoza
000111997 245__ $$aPredicting cell behaviour parameters from glioblastoma on a chip images. A deep learning approach
000111997 260__ $$c2021
000111997 5060_ $$aAccess copy available to the general public$$fUnrestricted
000111997 5203_ $$aThe 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)
000111997 536__ $$9info:eu-repo/grantAgreement/ES/MICINN/PID2019-106099RB-C44$$9info:eu-repo/grantAgreement/ES/MICINN/PGC2018-097257-B-C31
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000111997 591__ $$aBIOLOGY$$b13 / 94 = 0.138$$c2021$$dQ1$$eT1
000111997 593__ $$aHealth Informatics$$c2021$$dQ1
000111997 591__ $$aMATHEMATICAL & COMPUTATIONAL BIOLOGY$$b6 / 57 = 0.105$$c2021$$dQ1$$eT1
000111997 593__ $$aComputer Science Applications$$c2021$$dQ1
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000111997 591__ $$aCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS$$b24 / 112 = 0.214$$c2021$$dQ1$$eT1
000111997 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000111997 700__ $$0(orcid)0000-0003-0088-7222$$aDoweidar, M.H.$$uUniversidad de Zaragoza
000111997 700__ $$0(orcid)0000-0001-8741-6452$$aDoblaré, M.$$uUniversidad de Zaragoza
000111997 700__ $$0(orcid)0000-0003-2564-6038$$aAyensa-Jiménez, J.$$uUniversidad de Zaragoza
000111997 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000111997 773__ $$g135 (2021), 104547 [11 pp.]$$pComput. biol. med.$$tComputers in biology and medicine$$x0010-4825
000111997 8564_ $$s3030685$$uhttps://zaguan.unizar.es/record/111997/files/texto_completo.pdf$$yVersión publicada
000111997 8564_ $$s2578116$$uhttps://zaguan.unizar.es/record/111997/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
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