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Modeling the efficacy of different anti-angiogenic drugs on treatment of solid tumors using 3D computational modeling and machine learning

Research output: Contribution to Journal/MagazineJournal articlepeer-review

E-pub ahead of print
  • Milad Mousavi
  • Mahsa Dehghan Manshadi
  • Madjid Soltani
  • Farshad M Kashkooli
  • Arman Rahmim
  • Amir Mosavi
  • Michal Kvasnica
  • Peter M Atkinson
  • Levente Kovács
  • Andras Koltay
  • Norbert Kiss
  • Hojjat Adeli
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Article number105511
<mark>Journal publication date</mark>31/07/2022
<mark>Journal</mark>Computers in biology and medicine
Volume146
Number of pages16
Publication StatusE-pub ahead of print
Early online date18/04/22
<mark>Original language</mark>English

Abstract

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.]