Predicting Effective Adaptation to Breast Cancer to Help Women to BOUNCE Back

Risultati finali

Individual and societal benefits of high levels of resilience

D23 contains description and quantification if possible of benefits from better resilience both for the individual as well as the society

Communication Activities Report

D8.3 Is as report documenting the communication activities of the project partners.

BOUNCE Requirements & Usage Scenarios

D1.2 is the direct outcome of T1.2 and documents the BOUNCE target group segments/characteristics, the BOUNCE usage scenarios, the functional and non-functional requirements, and their translation into systemic and technical requirements.

Data-validated model of the role of resilience and resilience correlates in efficient adaptation to breast cancer

D24 contains the conceptual model of resilince factors affecting it and its impacts Model is validated with the data from the pilots

Dissemination & Scientific Workshop Activities Report

D82 Is a report documenting the dissemination clustering and standardization activities as well as the CSIFG related activities of the project partners

Identification of Internal and External Data Sources and Registries

D3.1 identifies all pre-existing data sets (Consortium-internal and external) that can be used to build a preliminary model of resilience before the pilot studies begin.

Initail Design and Implementation of the Preliminary In Silico Resilience Trajectory Predictor

D4.2 contains the mathematical models generated from the data as well as the aggregation of the models. (first version)

BOUNCE Value Chain

D1.1 is the direct outcome of T1.1 and documents the value chain of BOUNCE as well as the identified scientific, technical, industrial and societal stakeholders related to the specific value chain.

Predictive accuracy of the In-Silico Resilience Trajectory Predictor

D71 aims at validating the predictive model developed based on the pilots and validates the use of predictive tools in BC disease management This deliverable is crucial for the costbenefit analysis

Final semantic model (using real data)

D3.3 contains the semantic model, ie the translations necessary to make when integrating separate data sets with different variables and variable names. For the final version real data from the pilot partners will be used

Solutions for Data Aggregation, Cleaning, Harmonization & Storage

D33 is the outcome of T32 and T33 It comprises the aggregated cleaned and integrated data sets that have been made interoparable based on the BOUNCE semantic model

Definition and assessment of multi-level factors potentially affecting resilience

D2.2 contains the results of task 2.2: a list of factors potentially affecting resilience, both constant and time-varying.

BOUNCE Conceptual & Reference Architecture

D5.1 is the direct outcome of Task 5.1, 5.2 and 5.3 and will document the innovative, open, reference architecture of the overall BOUNCE framework and the holistic security approach that will be adopted to safeguard the security of the sensitive information.

Definition and assessment of resilience in women with Breast Cancer

D2.1 contains the description of the definition of resilience and a list of measures for resilience.

Clinical pilot methodology and preparatory actions

D6.1. contains a description of the pilot protocol including inclusion and exclusion criteria, data to be gathered,points in the treatment process when collect what data, and how to collect and store the data.

Roadmap for developing a predictor for other disease groups

D73 aims at enabling the development and configuration of similar models for other disease groups thus making the development much faster than the development of the InSilico Resilience Trajectory Predictor for BC developed in this study This way the results of this study benefit also other patient groups

BOUNCE Methodology

D1.3 is the direct outcome of T1.3 and documents the elaborated BOUNCE methodology, and the state of the art analysis on existing open source / commercial methods, data services, components and tools that can be integrated into the BOUNCE services and platform infrastructure.

Initial semantic model (using existing data)

D3.2 contains the semantic model, ie the translations necessary to make when integrating separate data sets with different variables and variable names. For the initial version exiting data will be used

Cost-benefit analysis of In Silico Resilience Trajectory Predictor

D72 aims at demonstrating the benefits to different stakeholders The results of the costbenefit analysis are crucial in justifying the adoption of the predictor in clinical practice

Data Management Plan

D84 Contains the plan of data management especially storage and usage of data after the project The plan is based on the Guidelines on Open Access to Scientific Publications and Research Data in Horizon 2020 and will be submitted on a voluntary basis to the Open Research Data Pilot

Pubblicazioni

The mutual determination of self-efficacy to cope with cancer and cancer-related coping over time: a prospective study in women with breast cancer

Autori: E. C. Karademas,I. Roziner,K. Mazzocco,R. Pat-Horenczyk,B. Sousa,A. J Oliveira-Maia,G. Stamatakos,F. Cardoso,D. Frasquilho,E. Kolokotroni,R. Lemos,C. Marzorati,J. Mattson,G. Pettini,E. SpyropoulouORCID Icon,P. Poikonen-Saksela, P. Simos
Pubblicato in: Psychology & health., 2022, ISSN 1476-8321
Editore: Harwood Academic Publishers
DOI: 10.1080/08870446.2022.2038157

Applied machine learning in cancer research: A systematic review for patient diagnosis, classification and prognosis.

Autori: Konstantina Kourou; Konstantinos P. Exarchos; Costas Papaloukas; Prodromos Sakaloglou; Themis Exarchos; Dimitrios I. Fotiadis; Dimitrios I. Fotiadis
Pubblicato in: Computational and Structural Biotechnology Journal, Numero Volume 19, 2021, 2021, Pagina/e Pages 5546-5555, ISSN 2001-0370
Editore: XXX
DOI: 10.1016/j.csbj.2021.10.006

A graphical LASSO analysis of global quality of life, sub scales of the EORTC QLQ-C30 instrument and depression in early breast cancer

Autori: Paula Poikonen-Saksela, Eleni Kolokotroni , Leena Vehmanen , Johanna Mattson , Georgios Stamatakos , Riikka Huovinen , Pirkko-Liisa Kellokumpu-Lehtinen , Carl Blomqvist , Tiina Saarto
Pubblicato in: Scientific Reports, 2022, ISSN 2045-2322
Editore: Nature Publishing Group
DOI: 10.1038/s41598-022-06138-2

Cognitive, emotional, and behavioral mediators of the impact of coping self-efficacy on adaptation to breast cancer: An international prospective study

Autori: Evangelos C. Karademas,Panagiotis Simos,Ruth Pat-Horenczyk,Ilan Roziner,Ketti Mazzocco,Berta Sousa,Albino J. Oliveira-Maia,Georgios Stamatakos,Fatima Cardoso,Diana Frasquilho,Eleni Kolokotroni,Chiara Marzorati,Johanna Mattson,Greta Pettini,Paula Poikonen-Sakselaon behalf of BOUNCE consortium
Pubblicato in: Psychooncology, 2021, ISSN 1099-1611
Editore: Wiley
DOI: 10.1002/pon.5730

132P The psychological impact of the COVID-19 pandemic on patients with early breast cancer

Autori: S. Almeida; Diana Frasquilho; Gonçalo Cotovio; Fernando Luiz Emerenciano Viana; Berta Sousa; J. Oliveira; Johanna Mattson; Chiara Marzorati; Ilan Roziner; Evangelos C. Karademas; Eleni Kolokotroni; Georgios S. Stamatakos; Ketti Mazzocco; Ruth Pat-Horenczyk; Paula Poikonen-Saksela; Fatima Cardoso; Albino J. Oliveira-Maia
Pubblicato in: Annals of Oncology, Numero MAY 01, 2021, 2021, Pagina/e xx, ISSN 2692-7950
Editore: xx
DOI: 10.1016/j.annonc.2021.03.146

A machine learning-based pipeline for modeling medical, socio-demographic, lifestyle and self-reported psychological traits as predictors of mental health outcomes after breast cancer diagnosis: An initial effort to define resilience effects

Autori: Konstantina Kourou , Georgios Manikis , Paula Poikonen-Saksela , Ketti Mazzocco, Ruth Pat-Horenczyk , Berta Sousa , Albino J Oliveira-Maia , Johanna Mattson , Ilan Roziner , Greta Pettini , Haridimos Kondylakis , Kostas Marias , Evangelos Karademas , Panagiotis Simos , Dimitrios I Fotiadis
Pubblicato in: Computers in biology and medicine, Numero Volume 131, April 2021, 104266, 2021, ISSN 1879-0534
Editore: Elsevier
DOI: 10.1016/j.compbiomed.2021.104266

Association between hedonic hunger and body-mass index versus obesity status

Autori: Ribeiro, Gabriela; Camacho, Marta; Santos, Osvaldo; Pontes, Cristina; Torres, Sandra; Maia, Albino J. Oliveira
Pubblicato in: Association between hedonic hunger and body-mass index versus obesity status, Numero Published: 11 April 2018, 2018, Pagina/e 5857 (2018), ISSN 2045-2322
Editore: Nature Publishing Group
DOI: 10.1038/s41598-018-23988-x

The Interplay Between Trait Resilience and Coping Self-efficacy in Patients with Breast Cancer: An International Study

Autori: E. C. Karademas, P. Simos, R. Pat-Horenczyk, I. Roziner, K. Mazzocco, B. Sousa, G. Stamatakos, G. Tsakou, F. Cardoso, D. Frasquilho, E. Kolokotroni, C. Marzorati, J. Mattson, A. J. Oliveira-Maia, K. Perakis, G. Pettini, L. Vehmanen & P. Poikonen-Saksela
Pubblicato in: Journal of clinical psychology in medical settings, 2022, ISSN 1573-3572
Editore: Springer
DOI: 10.1007/s10880-022-09872-x

Predicting Effective Adaptation to Breast Cancer to Help Women to BOUNCE Back: study protocol for a multicenter clinical pilot. Accepted to JMIR Research Protocols.

Autori: Pettini, Virginia Sanchini, Ruth Pat-Horenczyk, Berta Sousa, Marianna Masiero, Chiara Marzorati, Viviana Galimberti, Elisabetta Munzone, Johanna Mattson, Leena Vehmanen, Meri Utriainen, Ilan Roziner, Raquel Lemos, Diana Frasquilho, Fatima Cardoso, Albino Oliveira-Maia, Eleni Kolokotroni, Georgios S Stamatakos, Riikka-Leena Leskelä, Ira Haavisto, Juha Salonen, Robert Richter, Evangelos C Karademas
Pubblicato in: XX, Numero XX, 2022, Pagina/e XX
Editore: XX

a scalable bottom-up approach for building data series indexes

Autori: Haridimos Kondylakis; Niv Dayan; Kostas Zoumpatianos; Themis Palpanas
Pubblicato in: Proceedings of the VLDB Endowment, Numero 1, 2018
Editore: VLDB Endowment
DOI: 10.14778/3199517.3199519

). Developing a Data Infrastructure for Enabling Breast Cancer Women to BOUNCE Back. Special Track on Technological and Data-driven Innovations in Cancer Care

Autori: Katehakis, D.G., Kondylakis, H., Koumakis, L., Kouroubali, A., Marias, K., Tsiknakis, M.N., Simos, P.G., & Karademas, E
Pubblicato in: 32nd IEEE International Symposium on Computer-Based Medical Systems (CBMS 2019), 2019
Editore: Ieee
DOI: 10.1109/cbms.2019.00134

RDF Query Answering Using Apache Spark: Review and Assessment

Autori: G. Agathangelos, G. Troullinou, H. Kondylakis, K. Stefanidis and D. Plexousakis
Pubblicato in: Conference on Data Engineering Workshops (ICDEW), 2018, pp. 54-59, 2018
Editore: IEEE
DOI: 10.1109/icdew.2018.00016

ShinyAnonymizer: A Tool for Anonymizing Health Data

Autori: Vardalachakis, v, Kondylakis, H., Koumakis, L., Kouroubali, A., & Katehakis, D.G
Pubblicato in: International Conference on Information and Communication Technologies for Ageing Well and e-Health, (ICT4AWE), 2019, Pagina/e 325-332
Editore: ICT4AWE
DOI: 10.5220/0007798603250332

Status and recommendations of technological and data-driven innovations in cancer care: Focus group study. Journal of medical Internet research

Autori: Haridimos Kondylakis, Cristian Axenie, Dhundy Kiran Bastola, Dimitrios G Katehakis, Angelina Kouroubali , Daria Kurz, Nekane Larburu, Iván Macía, Roma Maguire, Christos Maramis , Kostas Marias , Philip Morrow , Naiara Muro , Francisco José Núñez-Benjumea, Andrik Rampun, Octavio Rivera-Romero, Bryan Scotney, Gabriel Signorelli, Hui Wang, Manolis Tsiknakis, Reyer Zwiggelaar
Pubblicato in: J Med Internet Res. 2020 Dec 15;22(12):e22034., 2020
Editore: J Med Internet Res
DOI: 10.2196/22034

INTEGRA: a web-based differential diagnosis system combining multiple knowledge bases

Autori: Papakonstantinou, Aris & Kondylakis, Haridimos & Marakakis, Emmanouil
Pubblicato in: PETRA '20: Proceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments, Numero Article No.: 45, 2020, Pagina/e Pages 1–6
Editore: Association for Computing Machinery
DOI: 10.1145/3389189.3397980

COSMOS: A Web-Based, Collaborative Knowledge System Using Ontologies and Managing Uncertainty

Autori: Giannoulis, Michail & Kondylakis, Haridimos & Marakakis, Emmanouil
Pubblicato in: 2018, Pagina/e 441-448
Editore: Association for Computing Machinery
DOI: 10.1145/3197768.3201555

Prediction of Poor Mental Health Following Breast Cancer Diagnosis Using Random Forests

Autori: Eugenia Mylona, Konstantina Kourou, Georgios Manikis, Haridimos Kondylakis, Kostas Marias, Evangelos Karademas, Paula Poikonen-Saksela, Ketti Mazzocco, Chiara Marzorati, Ruth Pat-Horenczyk, Ilan Roziner, Berta Sousa, Albino Oliveira-Maia, Panagiotis Simos, Dimitrios I Fotiadis
Pubblicato in: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2021
Editore: IEEE
DOI: 10.1109/embc46164.2021.9629589

Computational modeling of psychological resilience trajectories during breast cancer treatment

Autori: Manikis, G., Kourou, K, Poikonen-Saksela, P, Kondylakis, H., Karademas, E, Marias, K., Katehakis, D.G., Koumakis, L., Kouroubali, A., PatHorenczyk, R, Fotiadis, D.I., Tsiknakis, M.N., & Simos, P.G
Pubblicato in: IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE), 2019
Editore: Ieee
DOI: 10.1109/bibe.2019.00082

In-silico systems for well-being: Artificial Intelligence based analysis of psychological, mental, functional and quality of life aspects of life after breast cancer treatment.

Autori: Chatzidimitriou, Evangelos & Kolokotroni, Eleni & Pat-Horenczyk, Ruth & Pery, Shahar & Hamama-Raz, Yaira & Stemmer, Salomon & Tziraki, Chariklia & Stamatakos, Georgios.
Pubblicato in: 2020, Pagina/e Volume: pp. 573-574
Editore: Conference: VPH2020 (Virtual Physiological Human Conference 2020

Prediction of COVID-19 Infection Based on Symptoms and Social Life Using Machine Learning Techniques. In The 14th PErvasive Technologies Related to Assistive Environments Conference

Autori: Stefanos Zervoudakis, Emmanouil Marakakis, Haridimos Kondylakis, and Stefanos Goumas
Pubblicato in: 2021, Pagina/e Pages 277–283
Editore: Association for Computing Machinery
DOI: 10.1145/3453892.3462696

A Reference Architecture for Predicting Resilience Levels of Women with Breast Cancer

Autori: Kourou, K, Kondylakis, H., Koumakis, L., Manikis, G., Marias, K., Tsiknakis, M.N., Simos, P.G., & Karademas, E, Fotiadis
Pubblicato in: IEEE International Conference on Biomedical and Health Informatics (BHI'19), 2019
Editore: IEEE International Conference on Biomedical and Health Informatics (BHI'19)

Computational Models for Predicting Resilience Levels of Women with Breast Cancer

Autori: Kourou, K, Kondylakis, H., Koumakis, L., Manikis, G., Marias, K., Tsiknakis, M.N., Simos, P.G., & Karademas, E, Fotiadis, D.I.
Pubblicato in: 2019, Pagina/e 518-525
Editore: Springer

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