Related Experiment Video
Updated: Mar 15, 2026
Modeling Primary Bone Tumors and Bone Metastasis with Solid Tumor Graft Implantation into Bone
Published on: September 9, 2020
Image based modeling of tumor growth
N Meghdadi1,2, M Soltani3,4,5,6, H Niroomand-Oscuii7
1Division of Biomechanics, Department of Mechanical Engineering, Sahand University of Technology, East Azerbaijan, Tabriz, Iran.
Abstract:
Tumors are a main cause of morbidity and mortality worldwide. Despite the efforts of the clinical and research communities, little has been achieved in the past decades in terms of improving the treatment of aggressive tumors. Understanding the underlying mechanism of tumor growth and evaluating the effects of different therapies are valuable steps in predicting the survival time and improving the patients' quality of life. Several studies have been devoted to tumor growth modeling at different levels to improve the clinical outcome by predicting the results of specific treatments. Recent studies have proposed patient-specific models using clinical data usually obtained from clinical images and evaluating the effects of various therapies. The aim of this review is to highlight the imaging role in tumor growth modeling and provide a worthwhile reference for biomedical and mathematical researchers with respect to tumor modeling using the clinical data to develop personalized models of tumor growth and evaluating the effect of different therapies.
Insights
This review highlights how medical imaging aids tumor growth modeling. Personalized models using imaging data can predict patient survival and improve aggressive tumor treatment outcomes.
Area of Science:
- Oncology
- Biomedical Engineering
- Mathematical Modeling
Background:
- Tumors are a significant global cause of illness and death, with limited progress in treating aggressive types.
- Understanding tumor growth mechanisms and therapy effects is crucial for predicting survival and enhancing patient quality of life.
Purpose of the Study:
- To review the role of medical imaging in tumor growth modeling.
- To provide a reference for developing personalized tumor growth models using clinical data.
- To guide research on evaluating therapeutic effects through modeling.
Main Methods:
- Literature review focusing on studies integrating clinical data and imaging for tumor modeling.
- Analysis of methodologies for patient-specific tumor growth models.
- Examination of approaches for evaluating therapy effects within these models.
Main Results:
- Medical imaging provides essential data for creating accurate, patient-specific tumor growth models.
- These models can predict treatment responses and patient outcomes.
- Integrating imaging data enhances the predictive power of tumor growth simulations.
Conclusions:
- Medical imaging is pivotal for advancing personalized tumor growth modeling.
- Personalized models offer a pathway to improved treatment strategies for aggressive tumors.
- This review underscores the interdisciplinary approach needed for effective tumor modeling and treatment evaluation.
