‘In Silico’ Oncology for Clinical Decision Making in the Context of Nephroblastoma

Graf, Norbert; Hoppe, Alexander; Georgiadi, Eleni; Belleman, R.G.; Desmedt, Christine; Dionysiou, Dimitra D.; Erdt, Marius; Jacques, Judy; Kolokotroni, Eleni; Lunzer, Aran; Tsiknakis, Manolis; Stamatakos, Georgios S.

doi:10.1055/s-0029-1216368

Cited by 18 publications

(24 citation statements)

References 37 publications

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“…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. When there is a need for an upwards movement in the biocomplexity scales, a summary of the available information pertaining to the previous lower level is used.…”

Section: Methodsmentioning

confidence: 99%

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: Methodsmentioning

confidence: 99%

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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“…For instance, in clinico-genomic research gene expression patterns are generated before, during and after treatment in order to study the patient's gene activity in response to therapy. The final goal of clinico-genomic research is to identify significant biological markers associated with the development of disease and its response to treatment 20 21. However, a direct benefit for the patient is possible but not generally expected 22–24…”

Section: Introductionmentioning

confidence: 99%

Disclosure of individual research results in clinico-genomic trials: challenges, classification and criteria for decision-making: Table 1

Kollek

Petersen

2011

J Med Ethics

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While an ethical obligation to report findings of clinical research to trial participants is increasingly recognised, the academic debate is often vague about what kinds of data should be fed back and how such a process should be organised. In this article, we present a classification of different actors, processes and data involved in the feedback of research results pertaining to an individual. In a second step, we reflect on circumstances requiring further ethical consideration. In regard to a concrete research setting--the one of clinico-genomic research--we discuss what kinds of difficulties have to be faced when returning individual research results to trial participants. In a last step, we elaborate on a stepwise model to trigger the individual feedback process. Hence, this paper gives guidance on how to feedback individual research results in a specific research setting and responds at the same time to new challenges in the debate on the duty to return individual research findings.

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“…By the end of the ACGT project some 7000 simulations had been run using the ORS; early results of this work were published in [21] and [22], [23]. In general, the simulator was shown to be robust and predictable across a broad range of parameter settings, but the ability to make comparisons did help to discover various unexpected behaviors that the simulator developers were then able to resolve.…”

Section: Subjunctive Interface System For the Exploration Of The mentioning

confidence: 99%

The Technologically Integrated Oncosimulator: Combining Multiscale Cancer Modeling With Information Technology in the In Silico Oncology Context

Stamatakos

Dionysiou

Lunzer

et al. 2014

IEEE J. Biomed. Health Inform.

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This paper outlines the major components and function of the technologically integrated oncosimulator developed primarily within the Advancing Clinico Genomic Trials on Cancer (ACGT) project. The Oncosimulator is defined as an information technology system simulating in vivo tumor response to therapeutic modalities within the clinical trial context. Chemotherapy in the neoadjuvant setting, according to two real clinical trials concerning nephroblastoma and breast cancer, has been considered. The spatiotemporal simulation module embedded in the Oncosimulator is based on the multiscale, predominantly top-down, discrete entity-discrete event cancer simulation technique developed by the In Silico Oncology Group, National Technical University of Athens. The technology modules include multiscale data handling, image processing, invocation of code execution via a spreadsheet-inspired environment portal, execution of the code on the grid, and the visualization of the predictions. A refining scenario for the eventual coupling of the oncosimulator with immunological models is also presented. Parameter values have been adapted to multiscale clinical trial data in a consistent way, thus supporting the predictive potential of the oncosimulator. Indicative results demonstrating various aspects of the clinical adaptation and validation process are presented. Completion of these processes is expected to pave the way for the clinical translation of the system.

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‘In Silico’ Oncology for Clinical Decision Making in the Context of Nephroblastoma

Cited by 18 publications

References 37 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

Disclosure of individual research results in clinico-genomic trials: challenges, classification and criteria for decision-making: Table 1

The Technologically Integrated Oncosimulator: Combining Multiscale Cancer Modeling With Information Technology in the In Silico Oncology Context

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