Modeling the efficacy of different anti-angiogenic drugs on treatment of solid tumors using 3D computational modeling and machine learning
文献类型：期刊论文
| 作者 | Mousavi, Milad11; Manshadi, Mahsa Dehghan10,11; Soltani, Madjid10,12,13; Kashkooli, Farshad M.10,14; Rahmim, Arman15,16; Mosavi, Amir3,5,9,17; Kvasnica, Michal5; Atkinson, Peter M.6,7,8,18; Kovacs, Levente1,2; Koltay, Andras3 |
| 刊名 | COMPUTERS IN BIOLOGY AND MEDICINE
 |
| 出版日期 | 2022-07-01 |
| 卷号 | 146页码:16 |
| ISSN号 | 0010-4825 |
| 关键词 | Solid tumor Tumor growth Anti-angiogenic drugs Bevacizumab Ranibizumab Brolucizumab Artificial intelligence Cancer |
| DOI | 10.1016/j.compbiomed.2022.105511 |
| 通讯作者 | Soltani, Madjid() ; Mosavi, Amir(amir.mosavi@uni-obuda.hu) |
| 英文摘要 | 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- 3 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%. |
| WOS关键词 | INTERSTITIAL FLUID PRESSURE ; COMPUTER-AIDED DIAGNOSIS ; AUTOMATED DIAGNOSIS ; CELL-PROLIFERATION ; CLASSIFICATION ; CONNECTIVITY ; FEATURES ; AUTISM ; GRAPH |
| 资助项目 | National Research, Development and Innovation Fund of Hungary[2019-1.3.1-KK-2019-00007] ; National Research, Development and Innovation Fund of Hungary[2019-1.2.1-KK] ; Eotvos Lorand Research Network Secretariat[ELKH KO-40-2020] ; European Union[945478] |
| WOS研究方向 | Life Sciences & Biomedicine - Other Topics ; Computer Science ; Engineering ; Mathematical & Computational Biology |
| 语种 | 英语 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| WOS记录号 | WOS:000799841700001 |
| 资助机构 | National Research, Development and Innovation Fund of Hungary ; Eotvos Lorand Research Network Secretariat ; European Union |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/178326]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Soltani, Madjid; Mosavi, Amir |
| 作者单位 | 1.Obuda Univ, Biomatics Inst, John Neumann Fac Informat, H-1034 Budapest, Hungary 2.Univ Res, Obuda Univ, Physiol Controls Res Ctr, Innovat Ctr, H-1034 Budapest, Hungary 3.Natl Univ Publ Serv, Budapest, Hungary 4.Ohio State Univ, Dept Biomed Informat & Neurosci, Columbus, OH 43220 USA 5.Slovak Univ Technol Bratislava, Inst Informat Engn,Automation & Math, Bratislava, Slovakia 6.Univ Southampton, Geog & Environm Sci, Highfield, Southampton SO17 1BJ, England 7.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, 11A Datun Rd, Beijing 100101, Peoples R China 8.Univ Lancaster, Environm Ctr, Bailrigg, Lancaster LA1 4YR, England 9.Obuda Univ, Budapest, Hungary 10.K N Toosi Univ Technol, Dept Mech Engn, Tehran 1999143344, Iran |
| 推荐引用方式 GB/T 7714 | Mousavi, Milad,Manshadi, Mahsa Dehghan,Soltani, Madjid,et al. Modeling the efficacy of different anti-angiogenic drugs on treatment of solid tumors using 3D computational modeling and machine learning[J]. COMPUTERS IN BIOLOGY AND MEDICINE,2022,146:16. |
| APA | Mousavi, Milad.,Manshadi, Mahsa Dehghan.,Soltani, Madjid.,Kashkooli, Farshad M..,Rahmim, Arman.,...&Adeli, Hojjat.(2022).Modeling the efficacy of different anti-angiogenic drugs on treatment of solid tumors using 3D computational modeling and machine learning.COMPUTERS IN BIOLOGY AND MEDICINE,146,16. |
| MLA | Mousavi, Milad,et al."Modeling the efficacy of different anti-angiogenic drugs on treatment of solid tumors using 3D computational modeling and machine learning".COMPUTERS IN BIOLOGY AND MEDICINE 146(2022):16. |
入库方式： OAI收割
来源：地理科学与资源研究所
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