期刊
COMPUTERS IN BIOLOGY AND MEDICINE
卷 123, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2020.103820
关键词
Drug delivery; Tumor spheroids; Artificial neural network; Cancer; Mathematical model; Physical parameter identification; Oncophysics
类别
资金
- National Cancer Institute of the National Institutes of Health [U54CA210181]
- Technical University of Munich - Institute for Advanced Study - German Excellence Initiative
- TUV SUD Foundation [DOR1718128]
We address the problem of determining from laboratory experiments the data necessary for a proper modeling of drug delivery and efficacy in anticancer therapy. There is an inherent difficulty in extracting the necessary parameters, because the experiments often yield an insufficient quantity of information. To overcome this difficulty, we propose to combine real experiments, numerical simulation, and Machine Learning (ML) based on Artificial Neural Networks (ANN), aiming at a reliable identification of the physical model factors, e.g. the killing action of the drug. To this purpose, we exploit the employed mathematical-numerical model for tumor growth and drug delivery, together with the ANN - ML procedure, to integrate the results of the experimental tests and feed back the model itself, thus obtaining a reliable predictive tool. The procedure represents a hybrid datadriven, physics-informed approach to machine learning. The physical and mathematical model employed for the numerical simulations is without extracellular matrix (ECM) and healthy cells because of the experimental conditions we reproduce.
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