Continuous and Discrete Models of Melanoma Progression Simulated in Multi-GPU Environment

Dzwinel, Witold; Kłusek, Adrian; Wcisło, Rafał; Panuszewska, Marta; Topa, Paweł

doi:10.1007/978-3-319-78024-5_44

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…In Figure 8, we can follow the development of the cone shaped nodular melanoma. However, as shown in Dzwinel et al (2016b), Dzwinel et al (2018), and Kłusek et al (2016), the changes of the parameter representing the mechanical interactions between tumor cells in various levels of the skin tissue can produce the melanoma shapes similar to that observed in the micrographs. Particularly, the hardness of the basement membrane , which is located between stratum spinosum and dermisis , is responsible for the vertical growth of the tumor.…”

Section: Timings and Results Of Cancer Dynamics Simulationmentioning

confidence: 59%

“…Moreover, it is the model component, which drastically decreases the overall gain in efficiency expected in case of its implementation in GPU/CUDA environment. Similarly, we demonstrated in Dzwinel et al (2018) that coupling the continuous tumor model with blood vessels can represent the serious issue, especially, for ready to run numerical engines intended for modeling classical fluid dynamics or CAD/CAM systems. We consider this problem in a more explicit way in the following section.…”

Section: Tumor Modelmentioning

confidence: 92%

“…The most difficult problem in heterogeneous continuous/discrete and discrete/continuous models (Dzwinel et al, 2016a, 2018) is the problem of coupling their continuous and discrete components. In case of tumor dynamics, we show in Dzwinel et al (2016a) and Worecki and Wcisło (2012) that the vessels remodeling is the most computationally demanding part of the particle-based discrete/continuous PAM model.…”

Section: Tumor Modelmentioning

confidence: 99%

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Efficient model of tumor dynamics simulated in multi-GPU environment

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The International Journal of High Performance Computing Applica

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The application of computer simulation as a tool in predicting cancer dynamics (e.g. during anticancer therapy) requires tumor models, which are nontrivial and, simultaneously, not computationally demanding. To this end, both the level of details and computational efficiency of the model should be well balanced. The restrictions on computational time are forced by very demanding data assimilation process in the phase of parameters learning and their correction on the basis of incoming medical data. Herein we present a very efficient multi-GPU/CUDA implementation of three-dimensional (3-D) cancer model which allows for simulating both the growth and treatment phases of tumor dynamics. We demonstrate that the interaction between the tissue and the discrete network of blood vessels is a crucial component, which influences considerably the simulation time. Here we present a new solution which eliminates this flaw. We show also that the efficiency of our model does not depend on the complexity of tumor setup. As an example, we confront the growth of tumor in a simple and homogeneous environment with melanoma evolution, which proliferates in a complex environment of human skin. Consequently, the 3-D simulation of a tumor growth up to 1 cm in diameter requires an hour of computations on a midrange multi-GPU server.

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Section: Timings and Results Of Cancer Dynamics Simulationmentioning

confidence: 59%

Section: Tumor Modelmentioning

confidence: 92%