Cancer treatment greatly impacts physical and psychological functioning of cancer patients, negatively affecting their quality of life (QoL). This Umbrella Review (UR) aims to systematically summarize psychological and social factors positively or negatively associated with QoL in cancer patients undergoing treatment. Four scientific databases (PubMed, Embase, Scopus, and PsycInfo) were searched to identify systematic reviews between 2012 and 2023 analyzing the relationship between QoL and psychosocial factors in cancer patients in treatment. The UR was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Joanna Briggs Institute (JBI) review guidelines. The methodological quality of the included studies was evaluated using Assessment of Multiple Systematic Reviews 2 (AMSTAR2). Eighteen systematic reviews were included. The major psychological factors influencing QoL are depression, coping strategies, anxiety, and distress. Results also demonstrate the significant impact of social factors on QoL: perceived social support has a positive influence on QoL of cancer patients, while lowered social support, impaired social functioning, interactions, and role limitations worsen their QoL and overall well-being. This UR provides a comprehensive overview of the psychosocial factors impacting QoL of cancer patients and serves as a prominent base for developing questionnaires and policies aimed at measuring QoL in cancer patients undergoing treatment. Moreover, the findings of the study can guide future research or the development of personalized clinical interventions aimed at improving QoL for this cancer population group.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

Purpose: An interplay of clinical and psychosocial variables affects breast cancer patients’ experiences and clinical trajectories. Several studies investigated the role of socio-demographic, clinical, and psychosocial factors in predicting relevant outcomes in breast cancer care, thus developing predictive models. Our aim is to summarize predictive models for specific psychological and behavioral outcomes: psychological distress, quality of life, and medication adherence. Specifically, we aim to map the determinants of the outcomes of interest, offering a thorough overview of these models. Methods: Databases (PubMed, Scopus, Embase) have been searched to identify studies meeting the inclusion criteria: a breast cancer patients’ sample, development/validation of a predictive model for selected psychological/behavioral outcomes (ie, psychological distress, quality of life, and medication adherence), and availability of English full-text. Results: Twenty-one papers describing predictive models for psychological distress, quality of life, and adherence to medication in breast cancer were included. The models were developed using different statistical approaches. It has been shown that treatment-related factors (eg, side-effects, type of surgery or treatment received), socio-demographic (eg, younger age, lower income, and inactive occupational status), clinical (eg, advanced stage of disease, comorbidities, physical symptoms such as fatigue, insomnia, and pain) and psychological variables (eg, anxiety, depression, body image dissatisfaction) might predict poorer outcomes. Conclusion: Predictive models of distress, quality of life, and adherence, although heterogeneous, showed good predictive values, as indicated by the reported performance measures and metrics. Many of the predictors are easily available in patients’ health records, whereas others (eg, coping strategies, perceived social support, illness perceptions) might be introduced in routine assessment practices. The possibility to assess such factors is a relevant resource for clinicians and researchers involved in developing and implementing psychological interventions for breast cancer patients.

The caregivers’ perceptions of the patients’ health condition may be biased and induce them to perceive higher needs than patients actually disclose. Our aim was to assess if the level of knowledge and awareness about cancer disease and treatment, and patient participation and assistance differs between caregivers and patients. A descriptive, cross-sectional study was conducted across five countries (Italy, United Kingdom, Spain, France and Germany) on a total of 510 participants who directly (patient) or indirectly (caregiver) faced a cancer diagnosis. Investigating this divergence could help to identify possible difficulties in patient–caregiver relationship, eventually improving patient empowerment.

Background: Health professionals are often faced with the need to identify women at risk of manifesting poor psychological resilience following the diagnosis and treatment of breast cancer. Machine learning algorithms are increasingly used to support clinical decision support (CDS) tools in helping health professionals identify women who are at risk of adverse well-being outcomes and plan customized psychological interventions for women at risk. Clinical flexibility, cross-validated performance accuracy, and model explainability permitting person-specific identification of risk factors are highly desirable features of such tools. Objective: This study aimed to develop and cross-validate machine learning models designed to identify breast cancer survivors at risk of poor overall mental health and global quality of life and identify potential targets of personalized psychological interventions according to an extensive set of clinical recommendations. Methods: A set of 12 alternative models was developed to improve the clinical flexibility of the CDS tool. All models were validated using longitudinal data from a prospective, multicenter clinical pilot at 5 major oncology centers in 4 countries (Italy, Finland, Israel, and Portugal; the Predicting Effective Adaptation to Breast Cancer to Help Women to BOUNCE Back [BOUNCE] project). A total of 706 patients with highly treatable breast cancer were enrolled shortly after diagnosis and before the onset of oncological treatments and were followed up for 18 months. An extensive set of demographic, lifestyle, clinical, psychological, and biological variables measured within 3 months after enrollment served as predictors. Rigorous feature selection isolated key psychological resilience outcomes that could be incorporated into future clinical practice. Results: Balanced random forest classifiers were successful at predicting well-being outcomes, with accuracies ranging between 78% and 82% (for 12-month end points after diagnosis) and between 74% and 83% (for 18-month end points after diagnosis). Explainability and interpretability analyses built on the best-performing models were used to identify potentially modifiable psychological and lifestyle characteristics that, if addressed systematically in the context of personalized psychological interventions, would be most likely to promote resilience for a given patient. Conclusions: Our results highlight the clinical utility of the BOUNCE modeling approach by focusing on resilience predictors that can be readily available to practicing clinicians at major oncology centers. The BOUNCE CDS tool paves the way for personalized risk assessment methods to identify patients at high risk of adverse well-being outcomes and direct valuable resources toward those most in need of specialized psychological interventions.