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Updated: May 17, 2026
A Syngeneic Mouse Model of Metastatic Renal Cell Carcinoma for Quantitative and Longitudinal Assessment of Preclinical Therapies
Published on: April 12, 2017
Towards in silico oncology: adapting a four dimensional nephroblastoma treatment model to a clinical trial case based
Eleni Ch Georgiadi1, Dimitra D Dionysiou, Norbert Graf
1In Silico Oncology Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, Greece.
Abstract:
In the past decades a great progress in cancer research has been made although medical treatment is still widely based on empirically established protocols which have many limitations. Computational models address such limitations by providing insight into the complex biological mechanisms of tumor progression. A set of clinically-oriented, multiscale models of solid tumor dynamics has been developed by the In Silico Oncology Group (ISOG), Institute of Communication and Computer Systems (ICCS)-National Technical University of Athens (NTUA) to study cancer growth and response to treatment. Within this context using certain representative parameter values, tumor growth and response have been modeled under a cancer preoperative chemotherapy protocol in the framework of the SIOP 2001/GPOH clinical trial. A thorough cross-method sensitivity analysis of the model has been performed. Based on the sensitivity analysis results, a reasonable adaptation of the values of the model parameters to a real clinical case of bilateral nephroblastomatosis has been achieved. The analysis presented supports the potential of the model for the study and eventually the future design of personalized treatment schemes and/or schedules using the data obtained from in vitro experiments and clinical studies.
Insights
Computational models offer insights into tumor progression, addressing limitations in current cancer treatment protocols. These models aid in designing personalized cancer therapies by analyzing tumor growth and response to chemotherapy.
Area of Science:
- Computational oncology
- Mathematical modeling of tumors
- Translational bioinformatics
Background:
- Cancer treatment relies on empirical protocols with significant limitations.
- Computational models provide crucial insights into complex tumor biology and progression.
- Multiscale models of solid tumor dynamics are essential for advancing cancer research.
Purpose of the Study:
- To develop and validate clinically-oriented, multiscale computational models for solid tumor dynamics.
- To study cancer growth and treatment response within the SIOP 2001/GPOH clinical trial framework.
- To adapt model parameters for personalized treatment design using real clinical data.
Main Methods:
- Development of multiscale computational models for solid tumor dynamics.
- Modeling tumor growth and response to preoperative chemotherapy.
- Performing cross-method sensitivity analysis on model parameters.
- Adapting model parameters to a clinical case of bilateral nephroblastomatosis.
Main Results:
- A thorough cross-method sensitivity analysis was successfully performed on the computational model.
- Model parameters were reasonably adapted to a real clinical case of bilateral nephroblastomatosis.
- The study demonstrated the model's capability to simulate tumor growth and chemotherapy response.
Conclusions:
- Computational models offer a powerful tool to overcome limitations in current cancer treatment protocols.
- Sensitivity analysis is crucial for refining computational models for clinical applications.
- These models hold significant potential for the future design of personalized cancer treatment schemes and schedules.
