Computational models are not just appealing because they can simulate and predict the development of biological phenomena across multiple spatial and temporal scales, but also because they can integrate information from well-established in vitro and in vivo models and test new hypotheses in cancer biomedicine. Agent-based models and simulations are especially interesting candidates among computational modeling procedures in cancer research due to the capability to, for instance, recapitulate the dynamics of neoplasia and tumor - host interactions. Yet, the absence of methods to validate the consistency of the results across scales can hinder adoption by turning fine-tuned models into black boxes. This review compiles relevant literature that explores strategies to leverage high-fidelity simulations of multi-scale, or multi-level, cancer models with a focus on verification approached as simulation calibration. We consolidate our review with an outline of modern approaches for agent-based models' validation and provide an ambitious outlook toward rigorous and reliable calibration.

Background: The status of the data-driven management of cancer care as well as the challenges, opportunities, and recommendations aimed at accelerating the rate of progress in this field are topics of great interest. Two international workshops, one conducted in June 2019 in Cordoba, Spain, and one in October 2019 in Athens, Greece, were organized by four Horizon 2020 (H2020) European Union (EU)–funded projects: BOUNCE, CATCH ITN, DESIREE, and MyPal. The issues covered included patient engagement, knowledge and data-driven decision support systems, patient journey, rehabilitation, personalized diagnosis, trust, assessment of guidelines, and interoperability of information and communication technology (ICT) platforms. A series of recommendations was provided as the complex landscape of data-driven technical innovation in cancer care was portrayed.Objective: This study aims to provide information on the current state of the art of technology and data-driven innovations for the management of cancer care through the work of four EU H2020–funded projects.Methods: Two international workshops on ICT in the management of cancer care were held, and several topics were identified through discussion among the participants. A focus group was formulated after the second workshop, in which the status of technological and data-driven cancer management as well as the challenges, opportunities, and recommendations in this area were collected and analyzed.Results: Technical and data-driven innovations provide promising tools for the management of cancer care. However, several challenges must be successfully addressed, such as patient engagement, interoperability of ICT-based systems, knowledge management, and trust. This paper analyzes these challenges, which can be opportunities for further research and practical implementation and can provide practical recommendations for future work.Conclusions: Technology and data-driven innovations are becoming an integral part of cancer care management. In this process, specific challenges need to be addressed, such as increasing trust and engaging the whole stakeholder ecosystem, to fully benefit from these innovations.

A therapy’s outcome is determined by a tumor’s response to treatment which, in turn, depends on multiple factors such as the severity of the disease and the strength of the patient’s immune response. Gold standard cancer therapies are in most cases fragile when sought to break the ties to either tumor kill ratio or patient toxicity. Lately, research has shown that cancer therapy can be at its most robust when handling adaptive drug resistance and immune escape patterns developed by evolving tumors. This is due to the stochastic and volatile nature of the interactions, at the tumor environment level, tissue vasculature, and immune landscape, induced by drugs. Herein, we explore the path toward antifragile therapy control, that generates treatment schemes that are not fragile but go beyond robustness. More precisely, we describe the first instantiation of a control-theoretic method to make therapy schemes cope with the systemic variability in the tumor-immune-drug interactions and gain more tumor kills with less patient toxicity. Considering the anti-symmetric interactions within a model of the tumor-immune-drug network, we introduce the antifragile control framework that demonstrates promising results in simulation. We evaluate our control strategy against state-of-the-art therapy schemes in various experiments and discuss the insights we gained on the potential that antifragile control could have in treatment design in clinical settings.

On-field sensor-based soccer player tracking solutions are emerging and provide new insights into the dynamics of the player during training or a match. Yet, not all player positions are equally privileged. Goalkeepers' training and performance assessment were for a long time ignored. Understanding what is “the side of the post where most high dives were performed” provides valuable input for both the trainer and the athlete to improve performance or avoid injuries. In the current study, we focus on a practical methodology to extract insights from goalkeeper kinematics to inform such analytics. We demonstrate that information from a single motion sensor can be successfully used for learning patterns in goalkeeper's motion and provide an explainable goalkeeper kinematics assessment. We employed raw and quaternions data and we evaluated a series of machine learning algorithms that discriminate dive types (i.e. binary classification) and dives from other types of specific motions (i.e. multi-class classification) directly from the data. Our results demonstrate that XGBoost outperforms other approaches in terms of performance when considering both raw and quaternions, essentially benefiting from both types of data. Additionally, each prediction of the model is accompanied by an explanation of how each sensed motion component contributes to describing a specific goalkeeper's action captured by the model. The explainable predictions along with the efficient deployment of XGboost were decisive in our applied study. We evaluated our methodology on a first batch of experiments using online available data from 7 goalkeepers during 30 minutes-long training sessions.

Assistive care robots operate in cluttered, complex environments, akin to human residences, and need to face sensor and actuator faults during their operation without compromising safety. In these situations, the system must foresee and provide reactions that are beyond robust in addition to compensating for uncertainty and volatility in its functioning. We propose a new control design framework based on the principles of antifragility. In this work, we provide a formal concept of antifragile control and outline the design procedures for creating a mobile robot trajectory-tracking antifragile controller. An extended comparative evaluation against other controllers and a methodical investigation of the performance under parametrizable uncertainty and errors is also provided. Our findings demonstrate the effectiveness of antifragile closed-loop control in volatile and unanticipated circumstances.