Effect of particularisation size on the accuracy and efficiency of a multiscale tumours' growth model.

In silico, medicine models are frequently used to represent a phenomenon across multiples space-time scales. Most of these multiscale models require impracticable execution times to be solved, even using high performance computing systems, because typically each representative volume element in the upper scale model is coupled to an instance of the lower scale model; this causes a combinatory explosion of the computational cost, which increases exponentially as the number of scales to be modelled increases. To attenuate this problem, it is a common practice to interpose between the two models a particularisation operator, which maps the upper-scale model results into a smaller number of lower-scale models, and an operator, which maps the fewer results of the lower-scale models on the whole space-time homogenisation domain of upper-scale model. The aim of this study is to explore what is the simplest particularisation / homogenisation scheme that can couple a model aimed to predict the growth of a whole solid tumour (neuroblastoma) to a tissue-scale model of the cell-tissue biology with an acceptable approximation error and a viable computational cost. Using an idealised initial dataset with spatial gradients representative of those of real neuroblastomas, but small enough to be solved without any particularisation, we determined the approximation error and the computational cost of a very simple particularisation strategy based on binning. We found that even such simple algorithm can significantly reduce the computational cost with negligible approximation errors.

PRIMAGE project: predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers.

PRIMAGE is one of the largest and more ambitious research projects dealing with medical imaging, artificial intelligence and cancer treatment in children. It is a 4-year European Commission-financed project that has 16 European partners in the consortium, including the European Society for Paediatric Oncology, two imaging biobanks, and three prominent European paediatric oncology units. The project is constructed as an observational in silico study involving high-quality anonymised datasets (imaging, clinical, molecular, and genetics) for the training and validation of machine learning and multiscale algorithms. The open cloud-based platform will offer precise clinical assistance for phenotyping (diagnosis), treatment allocation (prediction), and patient endpoints (prognosis), based on the use of imaging biomarkers, tumour growth simulation, advanced visualisation of confidence scores, and machine-learning approaches. The decision support prototype will be constructed and validated on two paediatric cancers: neuroblastoma and diffuse intrinsic pontine glioma. External validation will be performed on data recruited from independent collaborative centres. Final results will be available for the scientific community at the end of the project, and ready for translation to other malignant solid tumours.

Support for Taverna workflows in the VPH-Share cloud platform.

BACKGROUND AND OBJECTIVE: To address the increasing need for collaborative endeavours within the Virtual Physiological Human (VPH) community, the VPH-Share collaborative cloud platform allows researchers to expose and share sequences of complex biomedical processing tasks in the form of computational workflows. The Taverna Workflow System is a very popular tool for orchestrating complex biomedical & bioinformatics processing tasks in the VPH community. This paper describes the VPH-Share components that support the building and execution of Taverna workflows, and explains how they interact with other VPH-Share components to improve the capabilities of the VPH-Share platform. METHODS: Taverna workflow support is delivered by the Atmosphere cloud management platform and the VPH-Share Taverna plugin. These components are explained in detail, along with the two main procedures that were developed to enable this seamless integration: workflow composition and execution. RESULTS: 1) Seamless integration of VPH-Share with other components and systems. 2) Extended range of different tools for workflows. 3) Successful integration of scientific workflows from other VPH projects. 4) Execution speed improvement for medical applications. CONCLUSION: The presented workflow integration provides VPH-Share users with a wide range of different possibilities to compose and execute workflows, such as desktop or online composition, online batch execution, multithreading, remote execution, etc. The specific advantages of each supported tool are presented, as are the roles of Atmosphere and the VPH-Share plugin within the VPH-Share project. The combination of the VPH-Share plugin and Atmosphere engenders the VPH-Share infrastructure with far more flexible, powerful and usable capabilities for the VPH-Share community. As both components can continue to evolve and improve independently, we acknowledge that further improvements are still to be developed and will be described.