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BackgroundAs the incidence of cancer is on the rise, there is a need to develop modern communication tools between patients and the medical personnel. Electronic patient-reported outcome (ePRO) measures increase the safety of cancer treatments and may have an impact on treatment outcome as well. ePRO may also provide a cost-efficient way to organize follow-up for patients with cancer. Noona is an internet-based system for patients to self-report symptoms and adverse events of cancer treatments from home via a computer or a smart device (eg, smartphone, tablet). ObjectiveIn this pilot study, we assessed the suitability of a novel ePRO application (Noona) for patients with cancer, nurses, and doctors at the Helsinki University Hospital, Finland. MethodsThe study included 44 patients with cancer (different solid tumor types) and 17 health care professionals (nurses or medical doctors). Patients were either operated or received systemic treatment or radiotherapy. Patients reported their symptoms to the medical staff via Noona. In addition, patients and clinicians answered a questionnaire, based on which Noona’s suitability for clinical use was evaluated in terms of usability (ease of use, operability, and learnability), reliability (subjective opinion of the participant), and incidence of harmful events reported by the participants. ResultsA total of 41/44 (93%) patients and 15/17 (88%) professionals reported that the program was easy or quite easy to use; 38/44 (86%) patients and 11/17 (65%) professionals found Noona reliable, and 38/44 (86%) patients and 10/17 (59%) professionals would recommend Noona to other patients or their colleagues. No harmful incidences caused by the use of Noona were reported by the patients; however, 1 harmful incidence was reported by one of the professionals. ConclusionsThe majority of the participants felt that Noona appeared reliable and it was easy to use. Noona seems to be a useful tool for monitoring patient’s symptoms during cancer therapy. Future studies will determine the impact of this ePRO platform in routine clinical practice.

Background. Despite excellent prognosis of early breast cancer, the patients face problems related to decreased quality of life and mental health. There is a need for easily available interventions targeting modifiable factors related to these problems. The aim of this study was to test the use of a new digital supportive intervention platform for early breast cancer patients. Material and Methods. Ninety‐seven early breast cancer patients answered questions on wellbeing, exercise, and sociodemographic factors before systemic adjuvant treatment at the Helsinki University Hospital. Based on these answers and predictive algorithms for anxiety and depression, they were guided onto one or several digital intervention paths. Patients under 56 years of age were guided onto a nutrition path, those who exercised less than the current guideline recommendations onto an exercise path, and those at risk of mental health deterioration onto an empowerment path. Information on compliance was collected at 3 months on the amount of exercise and quality of life using EORTC‐C30 scale, anxiety and depression using HADS scale at baseline and 12 months, and log‐in information at 3 and 12 months. Results. Thirty‐two patients followed the empowerment path, 43 the nutrition path, and 75 the exercise path. On a scale of 1–5, most of the participants (mean = 3.4; SD 0.815) found the interventions helpful and would have recommended testing and supportive interventions to their peers (mean = 3.70; SD 0.961). During the 10‐week intervention period, the mean number of log‐ins to the empowerment path was 3.69 (SD = 4.24); the nutrition path, 4.32 (SD = 2.891); and the exercise path, 8.33 (SD = 6.293). The higher number of log‐ins to the empowerment (rho = 0.531, P = 0.008, and n = 24) and exercise paths (rho = 0.330, P = 0.01, and n = 59) was related to better global quality of life at one year. The number of log‐ins correlated to the weekly amount of exercise in the exercise path (cc 0.740, P value <0.001, and n = 20). Conclusion. Patients' attitudes towards the interventions were positive, but they used them far less than was recommended. A randomized trial would be needed to test the effect of interventions on patients' QoL and mental health. [ABSTRACT FROM AUTHOR]

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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. [ABSTRACT FROM AUTHOR]

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., (© 2023 John Wiley & Sons Ltd.)

Being diagnosed with breast cancer (BC) can be a traumatic experience for patients who may experience symptoms of depression. In order to facilitate the prevention of such symptoms, it is crucial to understand how and why depressive symptoms emerge and evolve for each individual, from diagnosis through treatment and recovery. In the present work, data from a multicentric study of 706 BC patients followed for 12 months are analyzed. First, a trajectory-based unsupervised clustering based on K-means is performed to capture the dynamic patterns of change in patients' depressive symptoms after BC diagnosis and to identify distinct trajectory clusters. Then a supervised learning approach was employed to build a classification model of depression progression and to identify potential predictors. Patients were clustered into 4 groups: stable low, stable high, improving, and worsening depressive symptoms. In a nested cross-validation pipeline, the performance of the Support Vector Machine model for discriminating between "good" and "poor" progression was 0.78±0.05 in terms of AUC. Several psychological variables emerged as highly predictive of the evolution of depressive symptoms with the most important ones being negative affectivity and anxious preoccupation. Clinical Relevance-The findings of the present study may help clinicians tailor individualized psychological interventions aiming at alleviating the burden of these symptoms in women with breast cancer and improving their overall well-being.

Breast cancer diagnosis has been associated with poor mental health, with significant impairment of quality of life. In order to ensure support for successful adaptation to this illness, it is of paramount importance to identify the most prominent factors affecting well-being that allow for accurate prediction of mental health status across time. Here we exploit a rich set of clinical, psychological, socio-demographic and lifestyle data from a large multicentre study of patients recently diagnosed with breast cancer, in order to classify patients based on their mental health status and further identify potential predictors of such status. For this purpose, a supervised learning pipeline using cross-sectional data was implemented for the formulation of a classification scheme of mental health status 6 months after diagnosis. Model performance in terms of AUC ranged from 0.81± 0.04 to 0.90± 0.03. Several psychological variables, including initial levels of anxiety and depression, emerged as highly predictive of short-term mental health status of women diagnosed with breast cancer.