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  • Writer's picturegestam

In Silico Psycho-oncology: predicting resilience and effective adaptation to breast cancer


Coping with breast cancer constitutes a major socio-economic challenge on a global scale. There is a growing need for clinically relevant in silico approaches to predict and enhance the resilience of women to a host of stressful experiences and practical challenges related to breast cancer. In order to address these challenges, a prospective clinical study was deployed in four major clinical centres in the European Union (Finland, Italy and Portugal) and Israel, within the framework of the EU funded project BOUNCE ( The 4.5 year project BOUNCE was funded under the Horizon 2020 call entitled “Personalised computer models and in-silico systems for well-being”.

Exploitation of the data generated by the study has led to the development of pioneering clinically relevant artificial intelligence (AI) and advanced statistics based in silico models. The latter aim at predicting several resilience related psychological, psychiatric and functional parameter trajectories in women with early breast cancer. Numerous factors including clinical, biological, psychological, functional and socio-demographic data have been considered as potential determiners and predictors of the resilience related trajectories. A practical use of the validated predictive models is to prescribe adequate and timely interventions (e.g. psychological support) following treatment. The BOUNCE project was completed in April 2022. Its final review is to take place in July 2022.

The action (workpackage) of the BOUNCE project entitled "Development of the Predictive Breast Cancer Resilience Computer Models" was led by Research Professor G. S. Stamatakos, ICCS, SECE, Nat. Tech. Univ. Athens (NTUA) and Member of the Board of Trustees, Virtual Physiological Human Institute (VPHi) ( )

Within the framework of the BOUNCE implementation, one of its EU “excellent innovations” produced was entitled "In silico tool for predicting resilience in women diagnosed with breast cancer" ( A key innovator for this was ICCS, SECE, NTUA, In Silico Oncology and In Silico Medicine Group (

A recent publication in Scientific Reports, Nature Portfolio, outlines part of the modelling and analysis approaches (P. Poikonen‑Saksela(*1), E. Kolokotroni (*2), L.Vehmanen, J. Mattson, G. Stamatakos(2), R. Huovinen, P.‑L. K.‑Lehtinen, C. Blomqvist, T. Saarto, “A graphical LASSO analysis of global quality of life, sub scale of the EORTC QLQ‑C30 instrument and depression in early breast cancer,” Scientifc Reports, Nature Portfolio, 12, 2112 (2022), (* contributed equally, (1) Univ. Hospital of Helsinki, (2) ICCS- NTUA)


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