@article{LiscaProdaniucGrauschopfetal.2021, author = {Lisca, Gheorghe and Prodaniuc, Cristian and Grauschopf, Thomas and Axenie, Cristian}, title = {Less Is More: Learning Insights From a Single Motion Sensor for Accurate and Explainable Soccer Goalkeeper Kinematics}, volume = {21}, journal = {IEEE Sensors Journal}, number = {18}, publisher = {IEEE}, address = {Piscataway}, issn = {1530-437X}, doi = {https://doi.org/10.1109/JSEN.2021.3094929}, pages = {20375 -- 20387}, year = {2021}, language = {en} } @article{KondylakisAxenieBastolaetal.2020, author = {Kondylakis, Haridimos and Axenie, Cristian and Bastola, Dhundy and Katehakis, Dimitrios G. and Kouroubali, Angelina and Kurz, Daria and Larburu, Nekane and Mac{\´i}a, Iv{\´a}n and Maguire, Roma and Maramis, Christos and Marias, Kostas and Morrow, Philip and Muro, Naiara and N{\´u}{\~n}ez-Benjumea, Francisco Jos{\´e} and Rampun, Andrik and Rivera-Romero, Octavio and Scotney, Bryan and Signorelli, Gabriel and Wang, Hui and Tsiknakis, Manolis and Zwiggelaar, Reyer}, title = {Status and Recommendations of Technological and Data-Driven Innovations in Cancer Care: Focus Group Study}, volume = {22}, pages = {e22034}, journal = {Journal of Medical Internet Research}, number = {12}, publisher = {JMIR Publications}, address = {Toronto}, issn = {1438-8871}, doi = {https://doi.org/10.2196/22034}, year = {2020}, abstract = {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.}, language = {en} } @article{KurzSanchezAxenie2021, author = {Kurz, Daria and S{\´a}nchez, Carlos Salort and Axenie, Cristian}, title = {Data-Driven Discovery of Mathematical and Physical Relations in Oncology Data Using Human-Understandable Machine Learning}, volume = {4}, pages = {713690}, journal = {Frontiers in Artificial Intelligence}, publisher = {Frontiers}, address = {Lausanne}, issn = {2624-8212}, doi = {https://doi.org/10.3389/frai.2021.713690}, year = {2021}, abstract = {For decades, researchers have used the concepts of rate of change and differential equations to model and forecast neoplastic processes. This expressive mathematical apparatus brought significant insights in oncology by describing the unregulated proliferation and host interactions of cancer cells, as well as their response to treatments. Now, these theories have been given a new life and found new applications. With the advent of routine cancer genome sequencing and the resulting abundance of data, oncology now builds an "arsenal" of new modeling and analysis tools. Models describing the governing physical laws of tumor-host-drug interactions can be now challenged with biological data to make predictions about cancer progression. Our study joins the efforts of the mathematical and computational oncology community by introducing a novel machine learning system for data-driven discovery of mathematical and physical relations in oncology. The system utilizes computational mechanisms such as competition, cooperation, and adaptation in neural networks to simultaneously learn the statistics and the governing relations between multiple clinical data covariates. Targeting an easy adoption in clinical oncology, the solutions of our system reveal human-understandable properties and features hidden in the data. As our experiments demonstrate, our system can describe nonlinear conservation laws in cancer kinetics and growth curves, symmetries in tumor's phenotypic staging transitions, the preoperative spatial tumor distribution, and up to the nonlinear intracellular and extracellular pharmacokinetics of neoadjuvant therapies. The primary goal of our work is to enhance or improve the mechanistic understanding of cancer dynamics by exploiting heterogeneous clinical data. We demonstrate through multiple instantiations that our system is extracting an accurate human-understandable representation of the underlying dynamics of physical interactions central to typical oncology problems. Our results and evaluation demonstrate that, using simple—yet powerful—computational mechanisms, such a machine learning system can support clinical decision-making. To this end, our system is a representative tool of the field of mathematical and computational oncology and offers a bridge between the data, the modeler, the data scientist, and the practicing clinician.}, language = {en} } @article{AxenieKurzSaveriano2022, author = {Axenie, Cristian and Kurz, Daria and Saveriano, Matteo}, title = {Antifragile Control Systems: The Case of an Anti-Symmetric Network Model of the Tumor-Immune-Drug Interactions}, volume = {14}, pages = {2034}, journal = {Symmetry}, number = {10}, publisher = {MDPI}, address = {Basel}, issn = {2073-8994}, doi = {https://doi.org/10.3390/sym14102034}, year = {2022}, abstract = {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.}, language = {en} } @inproceedings{BecherAxenieGrauschopf2019, author = {Becher, Armin and Axenie, Cristian and Grauschopf, Thomas}, title = {VIRTOOAIR: Virtual Reality TOOlbox for Avatar Intelligent Reconstruction}, booktitle = {2018 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)}, subtitle = {System for VR motion reconstruction based on a VR tracking system and a single RGB camera}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-5386-7592-2}, doi = {https://doi.org/10.1109/ISMAR-Adjunct.2018.00085}, pages = {275 -- 279}, year = {2019}, language = {en} } @inproceedings{AxenieBecherKurzetal.2019, author = {Axenie, Cristian and Becher, Armin and Kurz, Daria and Grauschopf, Thomas}, title = {Meta-Learning for Avatar Kinematics Reconstruction in Virtual Reality Rehabilitation}, booktitle = {Proceedings 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)}, publisher = {IEEE}, address = {Los Alamitos}, isbn = {978-1-7281-4617-1}, issn = {2471-7819}, doi = {https://doi.org/10.1109/BIBE.2019.00117}, pages = {617 -- 624}, year = {2019}, language = {en} } @article{AxenieKurz2020, author = {Axenie, Cristian and Kurz, Daria}, title = {Role of Kinematics Assessment and Multimodal Sensorimotor Training for Motion Deficits in Breast Cancer Chemotherapy-Induced Polyneuropathy}, volume = {10}, pages = {1419}, journal = {Frontiers in Oncology}, subtitle = {A Perspective on Virtual Reality Avatars}, publisher = {Frontiers Media}, address = {Lausanne}, issn = {2234-943X}, doi = {https://doi.org/10.3389/fonc.2020.01419}, year = {2020}, abstract = {Chemotherapy-induced polyneuropathy (CIPN), one of the most severe and incapacitating side effects of chemotherapeutic drugs, is a serious concern in breast cancer therapy leading to dose diminution, delay, or cessation. The reversibility of CIPN is of increasing importance since active chemotherapies prolong survival. Clinical assessment tools show that patients experiencing sensorimotor CIPN symptoms not only do they have to cope with loss in autonomy and life quality, but CIPN has become a key restricting factor in treatment. CIPN incidence poses a clinical challenge and has lacked established and efficient therapeutic options up to now. Complementary, non-opioid therapies are sought for both prevention and management of CIPN. In this perspective, we explore the potential that digital interventions have for sensorimotor CIPN rehabilitation in breast cancer patients. Our primary goal is to emphasize the benefits and impact that Virtual Reality (VR) avatars and Machine Learning have in combination in a digital intervention aiming at (1) assessing the complete kinematics of deficits through learning underlying patient sensorimotor parameters, and (2) parameterize a multimodal VR simulation to drive personalized deficit compensation. We support our perspective by evaluating sensorimotor effects of chemotherapy, the metrics to assess sensorimotor deficits, and relevant clinical studies. We subsequently analyse the neurological substrate of VR sensorimotor rehabilitation, with multisensory integration acting as a key element. Finally, we propose a closed-loop patient-centered design recommendation for CIPN sensorimotor rehabilitation. Our aim is to provoke the scientific community toward the development and use of such digital interventions for more efficient and targeted rehabilitation.}, language = {en} } @article{AxenieBauerRodriguezMartinez2021, author = {Axenie, Cristian and Bauer, Roman and Rodr{\´i}guez Mart{\´i}nez, Mar{\´i}a}, title = {The Multiple Dimensions of Networks in Cancer: A Perspective}, volume = {13}, pages = {1559}, journal = {Symmetry}, number = {9}, publisher = {MDPI}, address = {Basel}, issn = {2073-8994}, doi = {https://doi.org/10.3390/sym13091559}, year = {2021}, abstract = {This perspective article gathers the latest developments in mathematical and computational oncology tools that exploit network approaches for the mathematical modelling, analysis, and simulation of cancer development and therapy design. It instigates the community to explore new paths and synergies under the umbrella of the Special Issue "Networks in Cancer: From Symmetry Breaking to Targeted Therapy". The focus of the perspective is to demonstrate how networks can model the physics, analyse the interactions, and predict the evolution of the multiple processes behind tumour-host encounters across multiple scales. From agent-based modelling and mechano-biology to machine learning and predictive modelling, the perspective motivates a methodology well suited to mathematical and computational oncology and suggests approaches that mark a viable path towards adoption in the clinic.}, language = {en} }