Fear of cancer recurrence in breast cancer: A moderated serial mediation analysis of a prospective international study.

OBJECTIVE: Women dealing with breast cancer (BC) face many challenges, one of which is the fear of cancer recurrence (FCR). This study examined whether disease severity predicts FCR 6 months after cancer diagnosis through psychological distress and whether cognitive-emotion regulation moderates this effect. METHOD: The study sample included 656 women from Italy (27.5%), Finland (31.9%), Israel (19.8%), and Portugal (20.8%) diagnosed with Stages I-III of BC. Participants' age ranged between 40 and 70 years (M = 54.92, SD = 8.22). Participants were tracked following BC diagnosis and at 3 and 6 months follow-up. Participants filled out self-report questionnaires, including the FCR inventory-short form, the Hospital Anxiety and Depression Scale, and the cognitive-emotion regulation questionnaire along with medical-social-demographic data. RESULTS: Greater disease severity at baseline indicated by higher cancer stage predicted greater psychological distress, which in turn predicted greater psychological distress at 3 months. The latter predicted greater FCR at 6 months. This serial mediation model was moderated by negative cognitive-emotion regulation. The mediating effect of disease severity on FCR through psychological distress was significant only in women with mean or higher levels of negative cognitive-emotion regulation. CONCLUSION: This study suggests that facilitating psychological well-being and effective cognitive-emotion regulation in the early stages after a cancer diagnosis may protect women from FCR. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Can mindfulness facilitate posttraumatic growth in breast cancer patients? The mediating role of illness perceptions and positive emotions.

The current observational study aimed to examine the relationship between mindfulness and posttraumatic growth (PTG) among patients with breast cancer. Additionally, it explores the mediating role of illness perceptions and positive emotions. A total of 697 women with breast cancer were recruited from four clinical sites as part of the Bounce project in Finland, Portugal, Italy, and Israel. The study measures were mindfulness (MAAS), illness perceptions (IPQ), positive affect (PANAS), and post-traumatic growth (PTGI) at three time points: near the time of diagnosis, 6 months, and 12 months post-diagnosis. A higher level of mindfulness was associated with perceptions of the illness as less chronic. Specifically, the perception of a limited timeline of breast cancer was associated with positive emotions, thus leading to enhanced PTG. Emphasis should be placed on promoting mindfulness, elaborating on illness perceptions, and maintaining positive affect as part of clinical interventions for PTG among breast cancer patients.

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

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.

Protective Factors against Fear of Cancer Recurrence in Breast Cancer Patients: A Latent Growth Model.

The current study aimed to examine the fear of cancer recurrence (FCR) trajectory and protective predictors in women coping with breast cancer (BC). The study's model investigated whether a higher coping self-efficacy and positive cognitive-emotion regulation at the time of the BC diagnosis would lead to reduced levels of FCR at six months and in later stages (12 and 18 months) post-diagnosis. The sample included 494 women with stages I to III BC from Finland, Italy, Portugal, and Israel. They completed self-report questionnaires, including the Fear of Cancer Recurrence Inventory (FCRI-SF), the Cancer Behavior Inventory-Brief Version (CBI-B), the Cognitive-Emotion Regulation Questionnaire (CERQ short), and medical-social-demographic data. Findings revealed that a higher coping self-efficacy at diagnosis predicted lower FCR levels after six months but did not impact the FCR trajectory over time. Surprisingly, positive cognitive-emotion regulation did not predict FCR levels or changes over 18 months. FCR levels remained stable from six to 18 months post-diagnosis. This study emphasizes the importance of developing specific cancer coping skills, such as coping self-efficacy. Enhancing coping self-efficacy in the first six months after BC diagnosis may lead to lower FCR levels later, as FCR tends to persist in the following year.

Predictive factors for prolonged sick leave in breast cancer patients treated with adjuvant therapies: a retrospective registry study.

BACKGROUND: Being able to work during and after breast cancer treatments is important for patients to have a sense of normalcy, financial security, and improved quality of life, and for society due to the economic burden of sick leave. Factors influencing the length of sick leave can be sociodemographic factors, workplace adaptations, recurrences, symptoms, and type of treatment. The aim of this study is to analyse factors associated with prolonged sick leave after adjuvant breast cancer treatments. METHODS: The population of this registry study consists of 1333 early breast cancer patients diagnosed and treated in Helsinki University Hospital between 2016 and 2018. Data on patient demographics, disease characteristics, treatment, and healthcare resource utilization were obtained from Helsinki University Hospital and data on income level and sick leave were obtained from Kela sickness benefits registry. Prolonged sick leave was determined as the patient accumulating 30 or more reimbursed sick leave days during a 60-day follow-up period after the end of active oncological treatment. Univariate analysis and multivariate analysis were conducted. RESULTS: A total of 26% of the patients in this study were on sick leave for 30 or more days after the active treatments ended. Study findings show that chemotherapy, triple-negative breast cancer, reconstructive surgery, amount of outpatient visits, and income are associated with prolonged sick leave. Independent predictors of prolonged sick leave were treatment line, number of outpatient contacts, reconstruction, and triple-negative breast cancer. CONCLUSIONS: Our study shows that prolonged sick leave affects a substantial number of working-age women with early breast cancer. Independent predictors for prolonged sick leave were all treatment-related. Targeted support for treatment-related side-effects already during the treatment period could lead to better recovery and earlier return to work.

Personalized Risk Analysis to Improve the Psychological Resilience of Women Undergoing Treatment for Breast Cancer: Development of a Machine Learning-Driven Clinical Decision Support Tool.

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.

Digital solution in the follow-up of early breast cancer a randomized study.

BACKGROUND: After primary treatment, patients with early breast cancer (EBC) are followed-up for at least 5 years. At the Helsinki University Hospital (HUS) surveillance includes appointments at 1, 3 and 5 years, and between pre-planned visits a phone call service operated by a nurse practitioner for counseling about symptoms related to side-effects or potential recurrence. In 2015 HUS launched a digital solution for cancer patients. This study was designed to find out patient preference, Health related (HR) quality of life (QOL) and satisfaction with a digital solution compared to a phone call service during the first year of follow-up. MATERIAL AND METHODS: Patients with EBC were randomized at the final visit of radiotherapy to surveillance by phone calls or by the digital Noona solution during the first year outside pre-planned visits. After six months the groups were crossed over to the other arm. Primary endpoint was patient preference for either follow-up method among those who had contacted the study nurse at least once by both phone service and digital solution. RESULTS: Out of the 765 patients randomized, 142 had contacted the hospital with both methods and were eligible for inclusion in the analyses of the present study. Out of the 142 patients, 56 preferred phone calls, 43 the digital solution while 43 considered both modalities equal. Preference for the digital solution was higher among patients aged 65 or less. There were no differences in HR QoL or overall satisfaction between the modalities. However, the patients rated the timeliness of response better while using the digital solution. CONCLUSION: Of the patients 30% preferred the digital solution, 40% phone calls while 30% found them equal as the primary follow-up method for EBC during the first year outside pre-planned visits. There is a need to include also digital solutions in surveillance of EBC. CLINICALTRIALS.GOV IDENTIFIER: NCT04980989.

Trajectories of Quality of Life among an International Sample of Women during the First Year after the Diagnosis of Early Breast Cancer: A Latent Growth Curve Analysis.

The current study aimed to track the trajectory of quality of life (QoL) among subgroups of women with breast cancer in the first 12 months post-diagnosis. We also aimed to assess the number and portion of women classified into each distinct trajectory and the sociodemographic, clinical, and psychosocial factors associated with these trajectories. The international sample included 699 participants who were recruited soon after being diagnosed with breast cancer as part of the BOUNCE Project. QoL was assessed at baseline and after 3, 6, 9, and 12 months, and we used Latent Class Growth Analysis to identify trajectory subgroups. Sociodemographic, clinical, and psychosocial factors at baseline were used to predict latent class membership. Four distinct QoL trajectories were identified in the first 12 months after a breast cancer diagnosis: medium and stable (26% of participants); medium and improving (47%); high and improving (18%); and low and stable (9%). Thus, most women experienced improvements in QoL during the first year post-diagnosis. However, approximately one-third of women experienced consistently low-to-medium QoL. Cancer stage was the only variable which was related to the QoL trajectory in the multivariate analysis. Early interventions which specifically target women who are at risk of ongoing low QoL are needed.

Personalized prediction of one-year mental health deterioration using adaptive learning algorithms: a multicenter breast cancer prospective study.

Identifying individual patient characteristics that contribute to long-term mental health deterioration following diagnosis of breast cancer (BC) is critical in clinical practice. The present study employed a supervised machine learning pipeline to address this issue in a subset of data from a prospective, multinational cohort of women diagnosed with stage I-III BC with a curative treatment intention. Patients were classified as displaying stable HADS scores (Stable Group; n = 328) or reporting a significant increase in symptomatology between BC diagnosis and 12 months later (Deteriorated Group; n = 50). Sociodemographic, life-style, psychosocial, and medical variables collected on the first visit to their oncologist and three months later served as potential predictors of patient risk stratification. The flexible and comprehensive machine learning (ML) pipeline used entailed feature selection, model training, validation and testing. Model-agnostic analyses aided interpretation of model results at the variable- and patient-level. The two groups were discriminated with a high degree of accuracy (Area Under the Curve = 0.864) and a fair balance of sensitivity (0.85) and specificity (0.87). Both psychological (negative affect, certain coping with cancer reactions, lack of sense of control/positive expectations, and difficulties in regulating negative emotions) and biological variables (baseline percentage of neutrophils, thrombocyte count) emerged as important predictors of mental health deterioration in the long run. Personalized break-down profiles revealed the relative impact of specific variables toward successful model predictions for each patient. Identifying key risk factors for mental health deterioration is an essential first step toward prevention. Supervised ML models may guide clinical recommendations toward successful illness adaptation.

Psychological and somatic symptoms among breast cancer patients in four European countries: A cross-lagged panel model.

Psychological and physical health among women with breast cancer are linked. However, more research is needed to test the interrelations between psychological and somatic symptoms, over time and throughout the different phases of breast cancer treatment, to determine when and which interventions should be prioritized. Six hundred and eighty nine women from four countries (Finland, Israel, Italy and Portugal) completed questionnaires during their first clinical consultation following diagnosis with breast cancer, and again after 3 and 6 months. The questionnaires included self-reported measures of psychological symptoms (Hospital Anxiety and Depression Scale; the Positive and Negative Affect Schedule Short Form) and somatic symptoms [selected items from the International European Organization for Research and Treatment of Cancer (EORTC) questionnaires]. Psychological and somatic symptoms were relatively stable across the three time-points. Cross-lagged paths leading from somatic to psychological symptoms (beta coefficients of 0.08-0.10), as well as vice-versa (beta 0.11-0.12), were found to be significant. No evidence was found for cross-cultural differences in mutual effects of psychological and somatic symptoms. The findings of this study call for tailoring personal interventions for breast cancer patients-either from a somatic perspective or a psychological perspective-and adjust them to the specific experiences of the individual patient.

Associations between Physical Exercise, Quality of Life, Psychological Symptoms and Treatment Side Effects in Early Breast Cancer.

Background: Identifying and understanding modifiable factors for the well-being of cancer patients is critical in survivorship research. We studied variables associated with the exercise habits of breast cancer patients and investigated if the achievement of exercise recommendations was associated with enhanced quality of life and/or psychological well-being. Material and Methods. 311 women from Finland, Portugal, Israel, and Italy receiving adjuvant therapy for stage I-III breast cancer answered questions about sociodemographic factors and physical exercise. Quality of life was assessed by the EORTC C30 and BR23 questionnaires. Anxiety and depression were evaluated using the HADS scale. Results: At the beginning of adjuvant therapy and after twelve months, 32% and 26% of participants were physically inactive, 27% and 30% exercised between 30 and 150 minutes per week, while 41% and 45% exercised the recommended 150 minutes or more per week. Relative to other countries, Finnish participants were more likely to be active at baseline and at twelve months (89% vs. 50%, p < 0.001 and 87% vs. 64%, p < 0.001). Participants with stage I cancer were more likely to be active at twelve months than those with a higher stage (80% vs. 70%,p < 0.05). The inactive participants reported more anxiety (p < 0.05) and depression (p < 0.001), lower global quality of life (p < 0.001), and more side effects (p < 0.05) than the others at twelve months. Accordingly, those who remained inactive or decreased their level of exercise from baseline to twelve months reported more anxiety (p < 0.01) and depression (p < 0.001), lower global quality of life (p < 0.001), and more side effects (p < 0.05) than those with the same or increased level of exercise. Conclusion: For women with early breast cancer, exercise was associated with a better quality of life, less depression and anxiety, and fewer adverse events of adjuvant therapy. Trial registration number: NCT05095675. Paula Poikonen-Saksela on behalf of Bounce consortium (https://www.bounce-project.eu/).

Predicting Effective Adaptation to Breast Cancer to Help Women BOUNCE Back: Protocol for a Multicenter Clinical Pilot Study.

BACKGROUND: 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.

Trajectories and Predictors of Depression After Breast Cancer Diagnosis: A 1-year longitudinal study.

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.

The moderating role of coping flexibility in reports of somatic symptoms among early breast cancer patients.

OBJECTIVE: The current study assessed breast cancer patients' somatic symptoms during the first six months post diagnosis and examined the moderating role of coping flexibility (i.e., trauma-focused and forward-focused coping strategies) on the association between reported somatic symptoms three months after breast cancer diagnosis and somatic symptoms six months after diagnosis. METHOD AND MEASURES: An international sample of 702 women diagnosed with breast cancer from four countries (Finland, Israel, Italy, Portugal) completed self-reported questionnaires at three time points: at the time of diagnosis (M0), three months post diagnosis (M3), and six months post diagnosis (M6). The questionnaires included the coping flexibility scale and questions about demographics, medical data, and somatic symptoms. RESULTS: The highest level of somatic symptoms was reported after three months post diagnosis (M3), as compared to M0 and M6. Both trauma-focused and forward-focused coping strategies moderated the relationship between somatic symptoms at M3 and somatic symptoms at M6. CONCLUSION: The findings highlight the importance of assessing somatic symptoms soon after breast cancer diagnosis and throughout the early phase of treatment. Coping flexibility can buffer the stability of the somatic symptoms during this initial phase.

A graphical LASSO analysis of global quality of life, sub scales of the EORTC QLQ-C30 instrument and depression in early breast cancer.

We aimed to (a) investigate the interplay between depression, symptoms and level of functioning, and (b) understand the paths through which they influence health related quality of life (QOL) during the first year of rehabilitation period of early breast cancer. A network analysis method was used. The population consisted of 487 women aged 35-68 years, who had recently completed adjuvant chemotherapy or started endocrine therapy for early breast cancer. At baseline and at the first year from randomization QOL, symptomatology and functioning by the EORTC QLQ-C30 and BR-23 questionnaires, and depression by the Finnish version of Beck's 13-item depression scale, were collected. The multivariate interplay between the related scales was analysed via regularized partial correlation networks (graphical LASSO). The median global quality of life (gQoL) at baseline was 69.9 ± 19.0 (16.7-100) and improved to 74.9 ± 19.0 (0-100) after 1 year. Scales related to mental health (emotional functioning, cognitive functioning, depression, insomnia, body image, future perspective) were clustered together at both time points. Fatigue was mediated through a different route, having the strongest connection with physical functioning and no direct connection with depression. Multiple paths existed connecting symptoms and functioning types with gQoL. Factors with the strongest connections to gQoL included: social functioning, depression and fatigue at baseline; emotional functioning and fatigue at month 12. Overall, the most important nodes were depression, gQoL and fatigue. The graphical LASSO network analysis revealed that scales related to fatigue and emotional health had the strongest associations to the EORTC QLQ-C30 gQoL score. When we plan interventions for patients with impaired QOL it is important to consider both psychological support and interventions that improve fatigue and physical function like exercise.Trial registration: http://www.clinicaltrials.gov/ (identifier number NCT00639210).

Adjuvant Capecitabine for Early Breast Cancer: 15-Year Overall Survival Results From a Randomized Trial.

PURPOSE: Few data are available regarding the influence of adjuvant capecitabine on long-term survival of patients with early breast cancer. METHODS: The Finland Capecitabine Trial (FinXX) is a randomized, open-label, multicenter trial that evaluates integration of capecitabine to an adjuvant chemotherapy regimen containing a taxane and an anthracycline for the treatment of early breast cancer. Between January 27, 2004, and May 29, 2007, 1,500 patients with axillary node-positive or high-risk node-negative early breast cancer were accrued. The patients were randomly allocated to either TX-CEX, consisting of three cycles of docetaxel (T) plus capecitabine (X) followed by three cycles of cyclophosphamide, epirubicin, and capecitabine (CEX, 753 patients), or to T-CEF, consisting of three cycles of docetaxel followed by three cycles of cyclophosphamide, epirubicin, and fluorouracil (CEF, 747 patients). We performed a protocol-scheduled analysis of overall survival on the basis of approximately 15-year follow-up of the patients. RESULTS: The data collection was locked on December 31, 2020. By this date, the median follow-up time of the patients alive was 15.3 years (interquartile range, 14.5-16.1 years) in the TX-CEX group and 15.4 years (interquartile range, 14.8-16.0 years) in the T-CEF group. Patients assigned to TX-CEX survived longer than those assigned to T-CEF (hazard ratio 0.81; 95% CI, 0.66 to 0.99; P = .037). The 15-year survival rate was 77.6% in the TX-CEX group and 73.3% in the T-CEF group. In exploratory subgroup analyses, patients with estrogen receptor-negative cancer and those with triple-negative cancer treated with TX-CEX tended to live longer than those treated with T-CEF. CONCLUSION: Addition of capecitabine to a chemotherapy regimen that contained docetaxel, epirubicin, and cyclophosphamide prolonged the survival of patients with early breast cancer.

Prediction of Poor Mental Health Following Breast Cancer Diagnosis Using Random Forests1.

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.

A Novel Digital Patient-Reported Outcome Platform (Noona) for Clinical Use in Patients With Cancer: Pilot Study Assessing Suitability.

BACKGROUND: As 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). OBJECTIVE: In 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. METHODS: The 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. RESULTS: A 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. CONCLUSIONS: The 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.

Cognitive, emotional, and behavioral mediators of the impact of coping self-efficacy on adaptation to breast cancer: An international prospective study.

OBJECTIVE: The main objective of this prospective multicenter study was to examine whether illness representations of control, affect, and coping behaviors mediate the effects of self-efficacy to cope with cancer on psychological symptoms and overall quality of life, in breast cancer patients. METHOD: Data from 413 women (Mean age = 54.87; SD = 8.01), coming from four countries (i.e., Finland, Israel, Italy, Portugal), who received medical therapy for their early breast cancer, were analyzed. Coping self-efficacy was assessed at baseline. Potential mediators were assessed three months later, and outcomes after six months. RESULTS: Coping self-efficacy was related to all mediators and outcomes. Illness representations of treatment control, positive and negative affect, and certain coping behaviors (mostly, anxiety preoccupation) mediated the effects of coping self-efficacy. Coping self-efficacy was related to each outcome through a different combination of mediators. CONCLUSIONS: Coping self-efficacy is a major self-regulation factor which is linked to well-being through multiple cognitive, emotional, and behavioral pathways. Enhancement of coping self-efficacy should be a central intervention goal for patients with breast cancer, towards promotion of their well-being.

A machine learning-based pipeline for modeling medical, socio-demographic, lifestyle and self-reported psychological traits as predictors of mental health outcomes after breast cancer diagnosis: An initial effort to define resilience effects.

Displaying resilience following a diagnosis of breast cancer is crucial for successful adaptation to illness, well-being, and health outcomes. Several theoretical and computational models have been proposed toward understanding the complex process of illness adaptation, involving a large variety of patient sociodemographic, lifestyle, medical, and psychological characteristics. To date, conventional multivariate statistical methods have been used extensively to model resilience. In the present work we describe a computational pipeline designed to identify the most prominent predictors of mental health outcomes following breast cancer diagnosis. A machine learning framework was developed and tested on the baseline data (recorded immediately post diagnosis) from an ongoing prospective, multinational study. This fully annotated dataset includes socio-demographic, lifestyle, medical and self-reported psychological characteristics of women recently diagnosed with breast cancer (N = 609). Nine different feature selection and cross-validated classification schemes were compared on their performance in classifying patients into low vs high depression symptom severity. Best-performing approaches involved a meta-estimator combined with a Support Vector Machines (SVMs) classification algorithm, exhibiting balanced accuracy of 0.825, and a fair balance between sensitivity (90%) and specificity (74%). These models consistently identified a set of psychological traits (optimism, perceived ability to cope with trauma, resilience as trait, ability to comprehend the illness), and subjective perceptions of personal functionality (physical, social, cognitive) as key factors accounting for concurrent depression symptoms. A comprehensive supervised learning pipeline is proposed for the identification of predictors of depression symptoms which could severely impede adaptation to illness.