Artificial Intelligence Supporting CAncer Patients Across Europe - the ASCAPE Project

Study Overview
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Brief Summary
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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.

Official Title
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Artificial Intelligence Supporting CAncer Patients Across Europe - the ASCAPE Project

Conditions
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Breast Cancer, Prostate Cancer, Quality of Life, Survivorship, Toxicity

Intervention / Treatment
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* Other: ASCAPE-based follow-up strategy


Participation Criteria
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Eligibility Criteria
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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.

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Supportive Care
Intervention Model: Single Group Assignment
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: ASCAPE-based follow-up strategy<br>Follow-up through ASCAPE platform including AI-based predictions for health-related QoL issues and suggestions for personalized interventions. | Other: ASCAPE-based follow-up strategy<br>* 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.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Patients' experience using ASCAPE-based follow-up | 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 | At the end of intervention (month 12) | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Patients' engagement to ASCAPE-based follow-up | Number of questionnaires submitted per patients; total time that the patients used the wearables | Every three months until the end of intervention (12 months) | 
| 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) | 
| Physicians' views and experience regarding ASCAPE-based follow-up in terms of implementation into clinical practice | 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 | At the end of intervention (month 12) | 
| Physicians' views and experience regarding ASCAPE-based follow-up in terms of interaction | 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. | At the end of intervention (month 12) | 
| Physicians' experience in using ASCAPE-based follow-up | 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. | At the end of intervention (month 12) | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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breast cancer, prostate cancer, quality of life, survivorship, artificial intelligence