Periodic Reporting for period 3 - BOUNCE (Predicting Effective Adaptation to Breast Cancer to Help Women to BOUNCE Back)
Período documentado: 2020-11-01 hasta 2022-04-30
The goal of BOUNCE is to incorporate elements of a predictive model of patient outcomes in a decision-support module used in clinical practice to provide physicians and other health professionals with recommendations regarding optimal psychosocial support strategies of early breast cancer patients.
There is need to improve understanding and better predict resilience of women with stressful experiences and practical challenges related to breast cancer. BOUNCE will take into consideration clinical, cancer related, lifestyle, and psychosocial parameters in order to predict individual resilience trajectories and eventually improve their quality of life.
Objectives:
1.To survey personal, clinical, and biological measures available for patient cohorts, in order to construct a preliminary model of patient resilience and quality of life.
2.To construct a measurement model of patient resilience to the physical and emotional challenges associated with the disease itself and with the burden incurred by BC treatments using data from the clinical pilot.
3.To develop operational algorithms for predicting long-term patient outcomes by taking into account individual levels of resilience at each time point during the course of illness, and current/past biological, sociodemographic, psychosocial, personal, clinical, and life-style patient characteristics.
4. To address long-standing issues in the field of psycho-oncology regarding the dynamics of time-varying relationships between determinants of resilience and disease outcomes.
5. To define measurable and potentially modifiable social, psychological, and lifestyle parameters that optimally define successful adaptation to BC-related stressors as determinants of long-term patient outcomes.
6.To ensure wide communication and scientific dissemination of the innovative BOUNCE results to the research, academic, and international community, the efficient exploitation and business planning of the BOUNCE concepts.
There is need to improve understanding and better predict resilience of women with stressful experiences and practical challenges related to breast cancer. BOUNCE will take into consideration clinical, cancer related, lifestyle, and psychosocial parameters in order to predict individual resilience trajectories and eventually improve their quality of life.
Objectives:
1.To survey personal, clinical, and biological measures available for patient cohorts, in order to construct a preliminary model of patient resilience and quality of life.
2.To construct a measurement model of patient resilience to the physical and emotional challenges associated with the disease itself and with the burden incurred by BC treatments using data from the clinical pilot.
3.To develop operational algorithms for predicting long-term patient outcomes by taking into account individual levels of resilience at each time point during the course of illness, and current/past biological, sociodemographic, psychosocial, personal, clinical, and life-style patient characteristics.
4. To address long-standing issues in the field of psycho-oncology regarding the dynamics of time-varying relationships between determinants of resilience and disease outcomes.
5. To define measurable and potentially modifiable social, psychological, and lifestyle parameters that optimally define successful adaptation to BC-related stressors as determinants of long-term patient outcomes.
6.To ensure wide communication and scientific dissemination of the innovative BOUNCE results to the research, academic, and international community, the efficient exploitation and business planning of the BOUNCE concepts.
In WP1 the value chain for the project was defined, different stakeholders identified and interviewed, the user requirements and scenarios were defined.
In WP2 the definition and assessment of 1. resilience in women with breast cancer, 2. resilience as a dynamic process critically involved in effective illness adaptation and recovery, 3. multi-level factors potentially affecting resilience, 4. multi-scale factors related to the evolution of resilience, in relation to both constant and time-varying patient characteristics, 5. changes in social and other life circumstances was determined.
WP3 delivered the Data Cleaner (front-end and back-end), introducing several optimizations based on statistical methods, which are now applied on all the collected prospective data. All data collected were successfully ingested and provided as input to WP4/WP5, after being processed with the corresponding tools for data cleaning, recoding, identification of missing data, assessment of missing data patterns, integration and homogenization.
In WP4 both unsupervised and supervised learning analysis pipelines have been formulated and implemented in order to model resilience as a process and resilience as outcome over different intervals of the first 18 months post-diagnosis. In order to address resilience as a process, unsupervised clustering schemes and latent-class mixed-effects regression analysis to identify subgroups of patients who display distinct profiles of change in mental health symptoms, HADS depression, HADS anxiety and global QoL, over the first 12 and 18 months post diagnosis have been developed and tested. With respect to resilience as outcome, generalizable models for optimal prediction of 12- and 18-month patient outcomes (in terms of mental health symptoms, HADS depression, HADS anxiety and global QoL) by aggregating all available patient information from the early phase of illness (i.e. M0 and M3 measurement waves) have been developed and tested. Both pooled data across sites and generalizability analyses have been conducted to establish a robust framework that could be exploited to other clinically relevant problems. The present work extends the explainability domain by adopting model-agnostic analysis in the models developed. Accuracy evaluation, and quality assurance of the BOUNCE In Silico Resilience Trajectory Predictor have been performed.
In WP5, the final release of the integrated BOUNCE software platform has been delivered. It was redesigned, focusing on its frontend part, based on the suggestions from clinical partners in order to increase its usability. The final version of all components was successfully integrated, while enhancements and optimizations were added. It also incorporates predictive models developed in WP4.
In WP6 prospective data on clinical, psychological, functional and quality of life factors were collected at baseline in Finland, Italy, Israel, Portugal for 746 breast cancer patients. Follow-up data collection was performed on months 3, 6, 9, 15, 18. Collected data have been provided to the technical partners for modelling. Following the analyses on prospective data, two pilot studies have been designed in HUS and IEO to test the efficacy of the preliminary resilience prediction model.
In WP7 additional validation has been conducted via an experiment. A cost-benefit model has been constructed were the cost and the benefit parameters have been defined and the data sources outlined, and the preliminary cost-benefit estimated.
In WP8 the work focused on dissemination and exploitation. Stakeholders have been engaged via conference presentations, workshops, individual interviews, and focus group interviews. The consortium has discussed and identified ownership interests, identified and evaluated different business models’ scenarios for the BOUNCE assets, and a preliminary marked analysis has been conducted.
In WP9 issues related to ethics, risk management and administration have been handled.
In WP2 the definition and assessment of 1. resilience in women with breast cancer, 2. resilience as a dynamic process critically involved in effective illness adaptation and recovery, 3. multi-level factors potentially affecting resilience, 4. multi-scale factors related to the evolution of resilience, in relation to both constant and time-varying patient characteristics, 5. changes in social and other life circumstances was determined.
WP3 delivered the Data Cleaner (front-end and back-end), introducing several optimizations based on statistical methods, which are now applied on all the collected prospective data. All data collected were successfully ingested and provided as input to WP4/WP5, after being processed with the corresponding tools for data cleaning, recoding, identification of missing data, assessment of missing data patterns, integration and homogenization.
In WP4 both unsupervised and supervised learning analysis pipelines have been formulated and implemented in order to model resilience as a process and resilience as outcome over different intervals of the first 18 months post-diagnosis. In order to address resilience as a process, unsupervised clustering schemes and latent-class mixed-effects regression analysis to identify subgroups of patients who display distinct profiles of change in mental health symptoms, HADS depression, HADS anxiety and global QoL, over the first 12 and 18 months post diagnosis have been developed and tested. With respect to resilience as outcome, generalizable models for optimal prediction of 12- and 18-month patient outcomes (in terms of mental health symptoms, HADS depression, HADS anxiety and global QoL) by aggregating all available patient information from the early phase of illness (i.e. M0 and M3 measurement waves) have been developed and tested. Both pooled data across sites and generalizability analyses have been conducted to establish a robust framework that could be exploited to other clinically relevant problems. The present work extends the explainability domain by adopting model-agnostic analysis in the models developed. Accuracy evaluation, and quality assurance of the BOUNCE In Silico Resilience Trajectory Predictor have been performed.
In WP5, the final release of the integrated BOUNCE software platform has been delivered. It was redesigned, focusing on its frontend part, based on the suggestions from clinical partners in order to increase its usability. The final version of all components was successfully integrated, while enhancements and optimizations were added. It also incorporates predictive models developed in WP4.
In WP6 prospective data on clinical, psychological, functional and quality of life factors were collected at baseline in Finland, Italy, Israel, Portugal for 746 breast cancer patients. Follow-up data collection was performed on months 3, 6, 9, 15, 18. Collected data have been provided to the technical partners for modelling. Following the analyses on prospective data, two pilot studies have been designed in HUS and IEO to test the efficacy of the preliminary resilience prediction model.
In WP7 additional validation has been conducted via an experiment. A cost-benefit model has been constructed were the cost and the benefit parameters have been defined and the data sources outlined, and the preliminary cost-benefit estimated.
In WP8 the work focused on dissemination and exploitation. Stakeholders have been engaged via conference presentations, workshops, individual interviews, and focus group interviews. The consortium has discussed and identified ownership interests, identified and evaluated different business models’ scenarios for the BOUNCE assets, and a preliminary marked analysis has been conducted.
In WP9 issues related to ethics, risk management and administration have been handled.
The main exploitable asset of BOUNCE is a decision support tool for clinicians treating breast cancer patients. The system is comprised of a set of clinically validated, in silico resilience trajectory prediction algorithms and the BOUNCE technological platform. The algorithms form the basis of a prediction system capable of providing individualized predictions of resilience levels. The resilience trajectory models can support the selection of support strategies and ultimately enhance the capacity of individual breast cancer patients to adapt to the illness.
Aims to achieve this:
-Making the prediction algorithms and the BOUNCE technological platform available for interested users
-Identifying consortium partners to support the users
-Describing the roadmap for commercialization
BOUNCE has aimed to contribute to the academic community and have a societal impact. Partners have strived and continue to strive to reach out and engage with project stakeholders. BOUNCE partners have built a scientific community around the project via organizing and participating in workshops and conferences.
Aims to achieve this:
-Making the prediction algorithms and the BOUNCE technological platform available for interested users
-Identifying consortium partners to support the users
-Describing the roadmap for commercialization
BOUNCE has aimed to contribute to the academic community and have a societal impact. Partners have strived and continue to strive to reach out and engage with project stakeholders. BOUNCE partners have built a scientific community around the project via organizing and participating in workshops and conferences.