A voxel‐based multiscale model to simulate the radiation response of hypoxic tumors

Espinoza, Ignacio; Peschke, Peter; Karger, Christian P.

doi:10.1118/1.4903298

Cited by 20 publications

(29 citation statements)

References 72 publications

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“…May et al . coupled the biomechanics of tumor–host tissues interaction with a cellular model of cancer growth, an important determinant especially in tumors growing in regions confined by bone tissue, such as in the case of brain tumors; multiscale models are also used to investigate the role of angiogenesis in tumor growth, or to better understand the effect of radiotherapy …”

Section: Multiscale Models Of Other Biomechanics Problemsmentioning

confidence: 99%

Multiscale modeling methods in biomechanics

Bhattacharya

Viceconti

2017

WIREs Mechanisms of Disease

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More and more frequently, computational biomechanics deals with problems where the portion of physical reality to be modeled spans over such a large range of spatial and temporal dimensions, that it is impossible to represent it as a single space–time continuum. We are forced to consider multiple space–time continua, each representing the phenomenon of interest at a characteristic space–time scale. Multiscale models describe a complex process across multiple scales, and account for how quantities transform as we move from one scale to another. This review offers a set of definitions for this emerging field, and provides a brief summary of the most recent developments on multiscale modeling in biomechanics. Of all possible perspectives, we chose that of the modeling intent, which vastly affect the nature and the structure of each research activity. To the purpose we organized all papers reviewed in three categories: ‘causal confirmation,’ where multiscale models are used as materializations of the causation theories; ‘predictive accuracy,’ where multiscale modeling is aimed to improve the predictive accuracy; and ‘determination of effect,’ where multiscale modeling is used to model how a change at one scale manifests in an effect at another radically different space–time scale. Consistent with how the volume of computational biomechanics research is distributed across application targets, we extensively reviewed papers targeting the musculoskeletal and the cardiovascular systems, and covered only a few exemplary papers targeting other organ systems. The review shows a research subdomain still in its infancy, where causal confirmation papers remain the most common. WIREs Syst Biol Med 2017, 9:e1375. doi: 10.1002/wsbm.1375For further resources related to this article, please visit the WIREs website.

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Section: Multiscale Models Of Other Biomechanics Problemsmentioning

confidence: 99%

Multiscale modeling methods in biomechanics

Bhattacharya

Viceconti

2017

WIREs Mechanisms of Disease

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“…48,49 The use of computational models allows investigating the interplay among many different mechanisms hindering the tumor response, even at multiscale level. 50,51 The major limitation of this method is the availability of data for model training and validation purposes. In this work, the data gathering issue was addressed by means of a noninvasive imaging technique (3D-Power Doppler Ultrasound), which was sufficient to constrain a macroscopic model.…”

Section: D Final Remarksmentioning

confidence: 99%

Comparison between model‐predicted tumor oxygenation dynamics and vascular‐/flow‐related Doppler indices

et al. 2017

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The results suggest that the modeled relation between tumor evolution and oxygen dynamics was reasonable enough to provide realistic oxygenation curves in five cases (correlation greater than 50%) out of seven. In case of nonresponsive tumors, the model failed in predicting the oxygenation trend while succeeded in reproducing the average oxygenation value according to the mean vascularization-flow index. Despite the need for deeper investigations, the outcomes of the present work support the hypothesis that a simple macroscale model of tumor response to radiotherapy is able to predict the tumor oxygenation. The possibility of an objective and quantitative validation on imaging data discloses the possibility to translate them as decision support tools in clinical practice and to move a step forward in the treatment personalization.

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“…The modeling of tumor proliferation has been often addressed in the literature. 18,19,21 Among the most common mathematical formulations for the spontaneous tumor growth, the Gompertzian, Logistic, and exponential equations need to be mentioned. 29,30 The Gompertzian and Logistic curves feature an initial exponential-like growth that saturates toward an asymptotic value, similar to what is often reported by in vitro and in vivo studies.…”

Section: B Mathematical Model Of Tumor Evolutionmentioning

confidence: 99%

Tumor radio‐sensitivity assessment by means of volume data and magnetic resonance indices measured on prostate tumor bearing rats

et al. 2016

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Purpose: Radiation therapy is one of the most common treatments in the fight against prostate cancer, since it is used to control the tumor (early stages), to slow its progression, and even to control pain (metastasis). Although many factors (e.g., tumor oxygenation) are known to influence treatment efficacy, radiotherapy doses and fractionation schedules are often prescribed according to the principle “one-fits-all,” with little personalization. Therefore, the authors aim at predicting the outcome of radiation therapy a priori starting from morphologic and functional information to move a step forward in the treatment customization. Methods: The authors propose a two-step protocol to predict the effects of radiation therapy on individual basis. First, one macroscopic mathematical model of tumor evolution was trained on tumor volume progression, measured by caliper, of eighteen Dunning R3327-AT1 bearing rats. Nine rats inhaled 100% O2 during irradiation (oxy), while the others were allowed to breathe air. Second, a supervised learning of the weight and biases of two feedforward neural networks was performed to predict the radio-sensitivity (target) from the initial volume and oxygenation-related information (inputs) for each rat group (air and oxygen breathing). To this purpose, four MRI-based indices related to blood and tissue oxygenation were computed, namely, the variation of signal intensity )(normalΔnormalSI in interleaved blood oxygen level dependent and tissue oxygen level dependent (IBT) sequences as well as changes in longitudinal )(normalΔR1 and transverse )(normalΔR2* relaxation rates. Results: An inverse correlation of the radio-sensitivity parameter, assessed by the model, was found with respect the normalΔR2* (−0.65) for the oxy group. A further subdivision according to positive and negative values of normalΔR2* showed a larger average radio-sensitivity for the oxy rats with normalΔR2*<0 and a significant difference in the two distributions (p < 0.05). Finally, a leave-one-out procedure yielded a radio-sensitivity error lower than 20% in both neural networks. Conclusions: While preliminary, these specific results suggest that subjects affected by the same pathology can benefit differently from the same irradiation modalities and support the usefulness of IBT in discriminating between different responses.

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A voxel‐based multiscale model to simulate the radiation response of hypoxic tumors

Cited by 20 publications

References 72 publications

Multiscale modeling methods in biomechanics

Multiscale modeling methods in biomechanics

Comparison between model‐predicted tumor oxygenation dynamics and vascular‐/flow‐related Doppler indices

Tumor radio‐sensitivity assessment by means of volume data and magnetic resonance indices measured on prostate tumor bearing rats

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