Final published version
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Modeling the efficacy of different anti-angiogenic drugs on treatment of solid tumors using 3D computational modeling and machine learning
AU - Mousavi, Milad
AU - Manshadi, Mahsa Dehghan
AU - Soltani, Madjid
AU - Kashkooli, Farshad M
AU - Rahmim, Arman
AU - Mosavi, Amir
AU - Kvasnica, Michal
AU - Atkinson, Peter M
AU - Kovács, Levente
AU - Koltay, Andras
AU - Kiss, Norbert
AU - Adeli, Hojjat
PY - 2022/7/31
Y1 - 2022/7/31
N2 - Accurate simulation of tumor growth during chemotherapy has significant potential to alleviate the risk of unknown side effects and optimize clinical trials. In this study, a 3D simulation model encompassing angiogenesis and tumor growth was developed to identify the vascular endothelial growth factor (VEGF) concentration and visualize the formation of a microvascular network. Accordingly, three anti-angiogenic drugs (Bevacizumab, Ranibizumab, and Brolucizumab) at different concentrations were evaluated in terms of their efficacy. Moreover, comprehensive mechanisms of tumor cell proliferation and endothelial cell angiogenesis are proposed to provide accurate predictions for optimizing drug treatments. The evaluation of simulation output data can extract additional features such as tumor volume, tumor cell number, and the length of new vessels using machine learning (ML) techniques. These were investigated to examine the different stages of tumor growth and the efficacy of different drugs. The results indicate that brolucizuman has the best efficacy by decreasing the length of sprouting new vessels by up to 16%. The optimal concentration was obtained at 10 mol m with an effectiveness percentage of 42% at 20 days post-treatment. Furthermore, by performing comparative analysis, the best ML method (matching the performance of the reference simulations) was identified as reinforcement learning with a 3.3% mean absolute error (MAE) and an average accuracy of 94.3%. [Abstract copyright: Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.]
AB - Accurate simulation of tumor growth during chemotherapy has significant potential to alleviate the risk of unknown side effects and optimize clinical trials. In this study, a 3D simulation model encompassing angiogenesis and tumor growth was developed to identify the vascular endothelial growth factor (VEGF) concentration and visualize the formation of a microvascular network. Accordingly, three anti-angiogenic drugs (Bevacizumab, Ranibizumab, and Brolucizumab) at different concentrations were evaluated in terms of their efficacy. Moreover, comprehensive mechanisms of tumor cell proliferation and endothelial cell angiogenesis are proposed to provide accurate predictions for optimizing drug treatments. The evaluation of simulation output data can extract additional features such as tumor volume, tumor cell number, and the length of new vessels using machine learning (ML) techniques. These were investigated to examine the different stages of tumor growth and the efficacy of different drugs. The results indicate that brolucizuman has the best efficacy by decreasing the length of sprouting new vessels by up to 16%. The optimal concentration was obtained at 10 mol m with an effectiveness percentage of 42% at 20 days post-treatment. Furthermore, by performing comparative analysis, the best ML method (matching the performance of the reference simulations) was identified as reinforcement learning with a 3.3% mean absolute error (MAE) and an average accuracy of 94.3%. [Abstract copyright: Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.]
KW - Solid tumor
KW - Anti-angiogenic drugs
KW - Tumor growth
KW - Brolucizumab
KW - Bevacizumab
KW - Artificial intelligence
KW - Ranibizumab
KW - Cancer
U2 - 10.1016/j.compbiomed.2022.105511
DO - 10.1016/j.compbiomed.2022.105511
M3 - Journal article
C2 - 35490641
VL - 146
JO - Computers in biology and medicine
JF - Computers in biology and medicine
SN - 1879-0534
M1 - 105511
ER -