TY - JOUR A1 - Lisca, Gheorghe A1 - Prodaniuc, Cristian A1 - Grauschopf, Thomas A1 - Axenie, Cristian T1 - Less Is More: Learning Insights From a Single Motion Sensor for Accurate and Explainable Soccer Goalkeeper Kinematics JF - IEEE Sensors Journal UR - https://doi.org/10.1109/JSEN.2021.3094929 Y1 - 2021 UR - https://doi.org/10.1109/JSEN.2021.3094929 SN - 1530-437X VL - 21 IS - 18 SP - 20375 EP - 20387 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Kondylakis, Haridimos A1 - Axenie, Cristian A1 - Bastola, Dhundy A1 - Katehakis, Dimitrios G. A1 - Kouroubali, Angelina A1 - Kurz, Daria A1 - Larburu, Nekane A1 - Macía, Iván A1 - Maguire, Roma A1 - Maramis, Christos A1 - Marias, Kostas A1 - Morrow, Philip A1 - Muro, Naiara A1 - Núñez-Benjumea, Francisco José A1 - Rampun, Andrik A1 - Rivera-Romero, Octavio A1 - Scotney, Bryan A1 - Signorelli, Gabriel A1 - Wang, Hui A1 - Tsiknakis, Manolis A1 - Zwiggelaar, Reyer T1 - Status and Recommendations of Technological and Data-Driven Innovations in Cancer Care: Focus Group Study JF - Journal of Medical Internet Research N2 - 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. UR - https://doi.org/10.2196/22034 KW - neoplasms KW - inventions KW - data-driven science Y1 - 2020 UR - https://doi.org/10.2196/22034 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-9473 SN - 1438-8871 VL - 22 IS - 12 PB - JMIR Publications CY - Toronto ER - TY - JOUR A1 - Kurz, Daria A1 - Sánchez, Carlos Salort A1 - Axenie, Cristian T1 - Data-Driven Discovery of Mathematical and Physical Relations in Oncology Data Using Human-Understandable Machine Learning JF - Frontiers in Artificial Intelligence N2 - 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. UR - https://doi.org/10.3389/frai.2021.713690 KW - mathematical oncology KW - machine learning KW - mechanistic modeling KW - data-driven predictions KW - clinical data KW - decision support system Y1 - 2021 UR - https://doi.org/10.3389/frai.2021.713690 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-34808 SN - 2624-8212 VL - 4 PB - Frontiers CY - Lausanne ER - TY - JOUR A1 - Axenie, Cristian A1 - Kurz, Daria A1 - Saveriano, Matteo T1 - Antifragile Control Systems: The Case of an Anti-Symmetric Network Model of the Tumor-Immune-Drug Interactions JF - Symmetry N2 - 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. UR - https://doi.org/10.3390/sym14102034 KW - antifragility KW - cancer KW - computational oncology KW - control theory Y1 - 2022 UR - https://doi.org/10.3390/sym14102034 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-29539 SN - 2073-8994 VL - 14 IS - 10 PB - MDPI CY - Basel ER - TY - CHAP A1 - Becher, Armin A1 - Axenie, Cristian A1 - Grauschopf, Thomas T1 - VIRTOOAIR: Virtual Reality TOOlbox for Avatar Intelligent Reconstruction BT - System for VR motion reconstruction based on a VR tracking system and a single RGB camera T2 - 2018 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) UR - https://doi.org/10.1109/ISMAR-Adjunct.2018.00085 KW - machine learning KW - supervised learning by regression KW - virtual reality KW - motion capture Y1 - 2019 UR - https://doi.org/10.1109/ISMAR-Adjunct.2018.00085 SN - 978-1-5386-7592-2 SP - 275 EP - 279 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Axenie, Cristian A1 - Becher, Armin A1 - Kurz, Daria A1 - Grauschopf, Thomas T1 - Meta-Learning for Avatar Kinematics Reconstruction in Virtual Reality Rehabilitation T2 - Proceedings 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE) UR - https://doi.org/10.1109/BIBE.2019.00117 KW - Neural Networks KW - Virtual Reality KW - Inverse Kinematics KW - Meta Learning KW - Rehabilitation KW - Chemotherapy Induced Peripheral Neuropathy Y1 - 2019 UR - https://doi.org/10.1109/BIBE.2019.00117 SN - 978-1-7281-4617-1 SN - 2471-7819 SP - 617 EP - 624 PB - IEEE CY - Los Alamitos ER - TY - JOUR A1 - Axenie, Cristian A1 - Kurz, Daria T1 - Role of Kinematics Assessment and Multimodal Sensorimotor Training for Motion Deficits in Breast Cancer Chemotherapy-Induced Polyneuropathy BT - A Perspective on Virtual Reality Avatars JF - Frontiers in Oncology N2 - 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. UR - https://doi.org/10.3389/fonc.2020.01419 KW - breast cancer KW - chemotherapy-induced peripheral neuropathy KW - virtual reality KW - machine learning KW - sensorimotor rehabilitation KW - body sensors Y1 - 2020 UR - https://doi.org/10.3389/fonc.2020.01419 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-9418 SN - 2234-943X VL - 10 PB - Frontiers Media CY - Lausanne ER - TY - JOUR A1 - Axenie, Cristian A1 - Bauer, Roman A1 - Rodríguez Martínez, María T1 - The Multiple Dimensions of Networks in Cancer: A Perspective JF - Symmetry N2 - 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. UR - https://doi.org/10.3390/sym13091559 KW - mathematical and computational oncology KW - cancer KW - networks KW - mechano-biology KW - machine learning Y1 - 2021 UR - https://doi.org/10.3390/sym13091559 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-13053 SN - 2073-8994 VL - 13 IS - 9 PB - MDPI CY - Basel ER -