Predicting Effective Adaptation to Breast Cancer to Help Women to BOUNCE Back: study protocol for a multicenter clinical pilot (Preprint)

Abstract

Despite the continued progress of medicine, dealing with breast cancer is becoming a major socio-economic 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 socio-demographic and psychological characteristics and on the social environment in which a person lives and interacts. Therefore, it is important to understand which factors may affect or booster a successful adaptation to breast cancer. To our knowledge, no studies have been already performed on the combination effect of multiple psychological, biological and functional variables in predicting the patient’s ability to bounce back from the stressful life event, such as a breast cancer diagnosis.

The aim of the study will be to build a quantitative mathematical model of factors associated with optimal adjustment capacity to cancer and study resilience through the cancer continuum in a population of breast cancer patients.

660 women with breast cancer will be recruited in five European cancer centers: the European Institute of Oncology (IEO) in Italy, the Rabin Medical Center and the Shaare Zedek Medical Center coordinated by the Hebrew University of Jerusalem (HUJI) in Israel, the Helsinki University Hospital (HUS) in Finland, and the Champalimaud Foundation (CHAMP) in Portugal. Biomedical and psychosocial variables will be collected using the Noona Healthcare platform. Psychosocial, socio-demographic, lifestyle and clinical variables will be measured every 3 months, starting from pre-surgery assessment (baseline) to 18 months (M18) after surgery.

Data collection is still ongoing.

The present study will provide a predictive model able to describe individual resilience and identify different resilience’s trajectories along the care process. The results will allow the implementation of tailored interventions according to the patient’s need, supported by e-health technologies.

ClinicalTrials.gov Identifier: NCT05095675