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- Predicting effective adaptation to breast cancer to help women BOUNCE back: Protocol for a Multicenter Clinical Pilot StudyPublication . Pettini, Greta; Sanchini, Virginia; Pat-Horenczyk, Ruth; Sousa, Berta; Masiero, Marianna; Marzorati, Chiara; Galimberti, Viviana Enrica; Munzone, Elisabetta; Mattson, Johanna; Vehmanen, Leena; Utriainen, Meri; Roziner, Ilan; Lemos, Raquel; Frasquilho, Diana; Cardoso, Fatima; Oliveira-Maia, Albino J; Kolokotroni, Eleni; Stamatakos, Georgios; Leskelä, Riikka-Leena; Haavisto, Ira; Salonen, Juha; Richter, Robert; Karademas, Evangelos; Poikonen-Saksela, Paula; Mazzocco, KettiBackground: Despite the continued progress of medicine, dealing with breast cancer is becoming a major socioeconomic challenge, particularly due to its increasing incidence. The ability to better manage and adapt to the entire care process depends not only on the type of cancer but also on the patient’s sociodemographic and psychological characteristics as well as on the social environment in which a person lives and interacts. Therefore, it is important to understand which factors may contribute to successful adaptation to breast cancer. To our knowledge, no studies have been performed on the combination effect of multiple psychological, biological, and functional variables in predicting the patient’s ability to bounce back from a stressful life event, such as a breast cancer diagnosis. Here we describe the study protocol of a multicenter clinical study entitled “Predicting Effective Adaptation to Breast Cancer to Help Women to BOUNCE Back” or, in short, BOUNCE. Objective: The aim of the study is to build a quantitative mathematical model of factors associated with the capacity for optimal adjustment to cancer and to study resilience through the cancer continuum in a population of patients with breast cancer. Methods: A total of 660 women with breast cancer will be recruited from five European cancer centers in Italy, Finland, Israel, and Portugal. Biomedical and psychosocial variables will be collected using the Noona Healthcare platform. Psychosocial, sociodemographic, lifestyle, and clinical variables will be measured every 3 months, starting from presurgery assessment (ie, baseline) to 18 months after surgery. Temporal data mining, time-series prediction, sequence classification methods, clustering time-series data, and temporal association rules will be used to develop the predictive model. Results: The recruitment process stared in January 2019 and ended in November 2021. Preliminary results have been published in a scientific journal and are available for consultation on the BOUNCE project website. Data analysis and dissemination of the study results will be performed in 2022. Conclusions: This study will develop a predictive model that is able to describe individual resilience and identify different resilience trajectories along the care process. The results will allow the implementation of tailored interventions according to patients’ needs, supported by eHealth technologies. Trial Registration: ClinicalTrials.gov NCT05095675; https://clinicaltrials.gov/ct2/show/NCT05095675 International Registered Report Identifier (IRRID): DERR1-10.2196/34564
- Well‐being trajectories in breast cancer and their predictors: A machine‐learning approachPublication . Karademas, Evangelos; Mylona, Eugenia; Mazzocco, Ketti; Pat‐Horenczyk, Ruth; Sousa, Berta; Oliveira‐Maia, Albino J.; Oliveira, Jose; Roziner, Ilan; Stamatakos, Georgios; Cardoso, Fatima; Kondylakis, Haridimos; Kolokotroni, Eleni; Kourou, Konstantina; Lemos, Raquel; Manica, Isabel; Manikis, George; Marzorati, Chiara; Mattson, Johanna; Travado, Luzia; Tziraki‐Segal, Chariklia; Fotiadis, Dimitris; Poikonen‐Saksela, Paula; Simos, PanagiotisObjective:This study aimed to described istinct trajectories of anxiety/depression symptoms and overall health status/quality of life over a period of 18 months followinga breast cancer diagnosis,and identify the medical, socio-demographic,lifestyle, and psychologica lfactors that predict these trajectories.Methods:474 females (mean age=55.79 years) were enrolled in the first weeksafter surgery or biopsy. Data from seven assessmentpoints over 18 months, at 3-month intervals,were used. The two outcomeswere assessedat all points. Potential predictors were assessed at baseline and the first follow‐up. Machine‐ Learning techniques were used to detect latent patterns of change and identify the most important predictors. Results: Five trajectories were identified for each outcome: stably high, high with fluctuations, recovery, deteriorating/delayed response, and stably poor well‐being (chronic distress). Psychological factors (i.e., negative affect, coping, sense of control, social support), age, and a few medical variables (e.g., symptoms, immune‐ related inflammation) predicted patients' participation in the delayed response and the chronic distress trajectories versus all other trajectories. Conclusions: There is a strong possibility that resilience does not always reflect a stable response pattern, as there might be some interim fluctuations. The use of machine‐learning techniques provides a unique opportunity for the identification of illness trajectories and a shortlist of major bio/behavioral predictors. This will facilitate the development of early interventions to prevent a significant deterioration in patient well‐being