Multilevel cancer modeling in the clinical environment: Simulating the behavior of Wilms tumor in the context of the SIOP 2001/GPOH clinical trial and the ACGT project

Georgiadi, Eleni; Stamatakos, Georgios S.; Graf, Norbert; Kolokotroni, Eleni; Dionysiou, Dimitra D.; Hoppe, Alexander; Uzunoglu, N.K.

doi:10.1109/bibe.2008.4696759

Cited by 5 publications

(9 citation statements)

References 12 publications

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“…The areas that appear in the graphs of Figure 4 show only combinations of biologically relevant parameter values leading to tumors that exhibit monotonic behavior for the case of free growth [23], [24] and tumors displaying volume reduction after therapy for the case of treatment.…”

Section: Resultsmentioning

confidence: 99%

“…For tumor regrowth after therapy studies, an exponential free growth pattern has been considered, which in fact approximates a segment of the Gompertzian curve, as explained in [23] . The areas that appear in the graphs of Figure 4 show only combinations of biologically relevant parameter values leading to tumors that exhibit monotonic behavior for the case of free growth [23] , [24] and tumors displaying volume reduction after therapy for the case of treatment.…”

Section: Resultsmentioning

confidence: 99%

“…The model is a predominantly discrete, clinically-oriented multiscale cancer model of solid tumor response to chemotherapy [23] , [24] , stemming from previous work of the In Silico Oncology Group (ISOG), National Technical University of Athens (NTUA). A “top-down” simulation approach is formulated [25] , [26] ; the method starts from the macroscopic imaging data (a high biocomplexity level) and proceeds towards lower biocomplexity levels.…”

Section: Methodsmentioning

confidence: 99%

“…The cells initially residing within each GC of the mesh are distributed into the five classes and subclasses mentioned above. The technique used for the tumor's constitution initialization is critical, in order to avoid latent artificial tumor growth behaviors, as previously described in [23] , [24] .…”

Section: Methodsmentioning

confidence: 99%

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Exploiting Clinical Trial Data Drastically Narrows the Window of Possible Solutions to the Problem of Clinical Adaptation of a Multiscale Cancer Model

et al. 2011

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The development of computational models for simulating tumor growth and response to treatment has gained significant momentum during the last few decades. At the dawn of the era of personalized medicine, providing insight into complex mechanisms involved in cancer and contributing to patient-specific therapy optimization constitute particularly inspiring pursuits. The in silico oncology community is facing the great challenge of effectively translating simulation models into clinical practice, which presupposes a thorough sensitivity analysis, adaptation and validation process based on real clinical data. In this paper, the behavior of a clinically-oriented, multiscale model of solid tumor response to chemotherapy is investigated, using the paradigm of nephroblastoma response to preoperative chemotherapy in the context of the SIOP/GPOH clinical trial. A sorting of the model's parameters according to the magnitude of their effect on the output has unveiled the relative importance of the corresponding biological mechanisms; major impact on the result of therapy is credited to the oxygenation and nutrient availability status of the tumor and the balance between the symmetric and asymmetric modes of stem cell division. The effect of a number of parameter combinations on the extent of chemotherapy-induced tumor shrinkage and on the tumor's growth rate are discussed. A real clinical case of nephroblastoma has served as a proof of principle study case, demonstrating the basics of an ongoing clinical adaptation and validation process. By using clinical data in conjunction with plausible values of model parameters, an excellent fit of the model to the available medical data of the selected nephroblastoma case has been achieved, in terms of both volume reduction and histological constitution of the tumor. In this context, the exploitation of multiscale clinical data drastically narrows the window of possible solutions to the clinical adaptation problem.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Exploiting Clinical Trial Data Drastically Narrows the Window of Possible Solutions to the Problem of Clinical Adaptation of a Multiscale Cancer Model

et al. 2011

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“…An effort to overcome some of the above mentioned limitations has been previously made by our research group through the development of four‐dimensional patient-specific in vivo simulation models of imageable tumour response to radiotherapeutic and chemotherapeutic schemes 17–29. All parameters used in the models have already been defined and can be determined (in principle) experimentally or clinically.…”

Section: Introductionmentioning

confidence: 99%

Introduction of Hypermatrix and Operator Notation into a Discrete Mathematics Simulation Model of Malignant Tumour Response to Therapeutic SchemesIn Vivo. Some Operator Properties

Stamatakos

Dionysiou

2009

Cancer Inform

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The tremendous rate of accumulation of experimental and clinical knowledge pertaining to cancer dictates the development of a theoretical framework for the meaningful integration of such knowledge at all levels of biocomplexity. In this context our research group has developed and partly validated a number of spatiotemporal simulation models of in vivo tumour growth and in particular tumour response to several therapeutic schemes. Most of the modeling modules have been based on discrete mathematics and therefore have been formulated in terms of rather complex algorithms (e.g. in pseudocode and actual computer code). However, such lengthy algorithmic descriptions, although sufficient from the mathematical point of view, may render it difficult for an interested reader to readily identify the sequence of the very basic simulation operations that lie at the heart of the entire model. In order to both alleviate this problem and at the same time provide a bridge to symbolic mathematics, we propose the introduction of the notion of hypermatrix in conjunction with that of a discrete operator into the already developed models. Using a radiotherapy response simulation example we demonstrate how the entire model can be considered as the sequential application of a number of discrete operators to a hypermatrix corresponding to the dynamics of the anatomic area of interest. Subsequently, we investigate the operators’ commutativity and outline the “summarize and jump” strategy aiming at efficiently and realistically address multilevel biological problems such as cancer. In order to clarify the actual effect of the composite discrete operator we present further simulation results which are in agreement with the outcome of the clinical study RTOG 83–02, thus strengthening the reliability of the model developed.

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A spatially adaptive active contour method for improving semi-automatic medical image annotation

et al. 2009

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Multilevel cancer modeling in the clinical environment: Simulating the behavior of Wilms tumor in the context of the SIOP 2001/GPOH clinical trial and the ACGT project

Cited by 5 publications

References 12 publications

Exploiting Clinical Trial Data Drastically Narrows the Window of Possible Solutions to the Problem of Clinical Adaptation of a Multiscale Cancer Model

Exploiting Clinical Trial Data Drastically Narrows the Window of Possible Solutions to the Problem of Clinical Adaptation of a Multiscale Cancer Model

Introduction of Hypermatrix and Operator Notation into a Discrete Mathematics Simulation Model of Malignant Tumour Response to Therapeutic SchemesIn Vivo. Some Operator Properties

A spatially adaptive active contour method for improving semi-automatic medical image annotation

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