Well-being trajectories in breast cancer and their predictors: A machine-learning approach

Evangelos C. Karademas*, Eugenia Mylona, Ketti Mazzocco, Ruth Pat-Horenczyk, Berta Sousa, Albino J. Oliveira-Maia, Jose Oliveira, Ilan Roziner, Georgios Stamatakos, Fatima Cardoso, Haridimos Kondylakis, Eleni Kolokotroni, Konstantina Kourou, Raquel Lemos, Isabel Manica, George Manikis, Chiara Marzorati, Johanna Mattson, Luzia Travado, Chariklia Tziraki-SegalDimitris Fotiadis, Paula Poikonen-Saksela, Panagiotis Simos

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Objective: This study aimed to describe distinct trajectories of anxiety/depression symptoms and overall health status/quality of life over a period of 18 months following a breast cancer diagnosis, and identify the medical, socio-demographic, lifestyle, and psychological factors that predict these trajectories. Methods: 474 females (mean age = 55.79 years) were enrolled in the first weeks after surgery or biopsy. Data from seven assessment points over 18 months, at 3-month intervals, were used. The two outcomes were assessed at 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.

Original languageAmerican English
Pages (from-to)1762-1770
Number of pages9
Issue number11
StatePublished - Nov 2023

Bibliographical note

Publisher Copyright:
© 2023 John Wiley & Sons Ltd.


  • breast cancer
  • cancer
  • oncology
  • trajectories
  • trajectory predictors


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