Breast Cancer, Prostate Cancer, Quality of Life, Survivorship, Toxicity
Conditions
Keywords
breast cancer, prostate cancer, quality of life, survivorship, artificial intelligence
Brief summary
ASCAPE (Artificial intelligence Supporting CAncer Patients across Europe) is a collaborative research project involving 15 partners from 7 countries, including academic medical centers, SMEs (small and medium-sized enterprises), research centers and universities, aiming to leverage the recent advances in Big Data and AI (Artificial Intelligence) to support cancer patients' Quality of Life (QoL) and health status. Specifically, ASCAPE aims to provide personalized- and AI-based predictions for QoL issues in breast- and prostate cancer patients as well as suggest potential interventions to their physicians. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 875351.
Interventions
OTHERASCAPE-based follow-up strategy
Follow-up through ASCAPE platform including AI-based predictions for health-related QoL issues and suggestions for personalized interventions depending on the type of QoL issue that needs to be tackled. The ASCAPE-based follow-up strategy includes follow-up through validated QoL questionnaires, wearables for capturing active monitoring data, and mobile apps for answering the questionnaires and capturing potential health-related issues.
Sponsors
UBITECH
ATOS
Siemens Corporation, Corporate Technology
Intrasoft
University of Patras
FORTH - Foundation for Research and Technology Hellas
Sphynx Technology Solutions AG
Faculty of Sciences, University of Novi Sad, Serbia
DFKI - German Research Center for Artificial Intelligence
CareAcross
National and Kapodistrian University of Athens, Greece
Fundacio Clinic Barcelona
Arthur's Legal
Fundacion iSYS
Region Örebro County
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
SUPPORTIVE_CARE
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
The patient selection process varies among the different study sites. Three Hospitals (Sismanogleio General Hospital Athens (SGHA), Örebro University Hospital (ÖUH), Uppsala University Hospital (UUH)) will include patients with newly diagnosed breast- (ÖUH, UUH) or prostate cancer (SGHA, ÖUH) who are eligible for curative treatment with surgery (breast cancer in ÖUH and UUH; prostate cancer in NKUA) or radiotherapy (prostate cancer; ÖUH). One Hospital (Barcelona) will include breast cancer survivors (at least 12 months after surgery or chemotherapy) with follow-up through the Hospital. Finally, CareAcross will include patients with breast- or prostate cancer through its online platform for patients seeking for the CareAcross services. Inclusion Criteria (breast cancer; ORB and Uppsala): * breast cancer diagnosis * no clinical evidence of metastatic disease * able for curative treatment with surgery with or without oncological treatment. Inclusion Criteria (breast cancer; Barcelona) * prior early breast cancer who are at follow-up with at least 12 months after surgery or chemotherapy (whichever occurred last). Inclusion Criteria (breast cancer; CareAcross) * breast cancer diagnosis (as per self-reported) irrespective of stage and treatment. Inclusion Criteria (prostate cancer; SGHA and ORB) * proostate cancer diagnosis * no clinical evidence of metastatic disease * able for curative treatment with surgery with or without oncological treatment (SGHA) or radiotherapy (with or without prior surgery) irrespectively the type of radiotherapy (external radiotherapy, brachytherapy, or combination). Inclusion Criteria (prostate cancer; CareAcross) * prostate cancer diagnosis (as per self-reported) irrespective of stage and treatment.
Exclusion criteria
(common in all study sites): * inability to give informed consent * inability / no access to smartphones, applications or internet services * patients with known medical history of allergy to the wearable material.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Patients' experience using ASCAPE-based follow-up | At the end of intervention (month 12) | Patients' experience to be followed with the help of an AI-based system per se, patients' satisfaction with this type of follow-up, potential barriers and facilitators of using wearables during follow-up, and motivation for following interventions based on AI-based follow-up |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Patients' adherence to AI-based proposed intervention | Every three months until the end of intervention (12 months) | — |
| Assessment of health-related QoL over time | Every three months until the end of intervention (12 months) | — |
| Patients' engagement to ASCAPE-based follow-up | Every three months until the end of intervention (12 months) | Number of questionnaires submitted per patients; total time that the patients used the wearables |
| Physicians' views and experience regarding ASCAPE-based follow-up in terms of interaction | At the end of intervention (month 12) | This outcome includes issues related to the interaction between the AI-based follow-up platform and physicians as usability, accessibility, and qualitative assessment of the interface. |
| Physicians' experience in using ASCAPE-based follow-up | At the end of intervention (month 12) | This outcome includes issues related to trustworthiness, how confident physicians are regarding the reliability of AI-based follow-up, and psychological aspects in using an AI-based platform in clinical practice as perceived substitution crisis and behavioural intention. |
| Physicians' views and experience regarding ASCAPE-based follow-up in terms of implementation into clinical practice | At the end of intervention (month 12) | The following aspects will be considered: improvement in patient-doctor relationship; AI-based follow-up's efficiency to capture relevant QoL issues on time; changes in management or referrals made due to AI-based predictions; usefulness of the information provided by AI-based models; acceptability of integrating AI-based follow-up into clinical practice; assessment of the time needed to use AI-based follow-up in clinical practice |
Countries
Greece, Spain, Sweden, United Kingdom
Outcome results
None listed