Computational modeling of cancer growth using microvascular input from in vivo microct images

Mohammad Hossein Zangooei, Ryan Margolis, Kenneth Hoyt

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Despite the number of clinical and experimental surveys, a computational model of cancer growth in which the abundance of data can be structured and understood is lacking. The goal of this project was to provide a comprehensive and expandable simulation method to predict and visualize cancer and microvascular network growth. This novel method offers the advantage of a multiscale model that incorporates data from in vivo microscale computed tomography (microCT) images of the microvasculature in breast cancer-bearing animals. We use a lattice-based model that is designed so that different evolutionary scenarios can be established to predict the impact of nutrient stress on tumor morphology and growth patterns. Overall, simulation results show tumor progression similar to that known to occur in clinical practice.

Original languageEnglish (US)
Title of host publication2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021
PublisherIEEE Computer Society
Pages299-302
Number of pages4
ISBN (Electronic)9781665412469
DOIs
StatePublished - Apr 13 2021
Externally publishedYes
Event18th IEEE International Symposium on Biomedical Imaging, ISBI 2021 - Nice, France
Duration: Apr 13 2021Apr 16 2021

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2021-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference18th IEEE International Symposium on Biomedical Imaging, ISBI 2021
Country/TerritoryFrance
CityNice
Period4/13/214/16/21

Keywords

  • Artificial intelligence
  • Cancer growth
  • Computational modeling
  • Deep Q-network
  • Medical imaging

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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