Periodic Reporting for period 1 - ASCAPE (Artificial intelligence Supporting CAncer Patients across Europe)
Berichtszeitraum: 2020-01-01 bis 2021-06-30
The latest cancer statistics show promising advances in decreasing mortality related to cancer (e.g. EU 4.5% between 2015 and 2020, US 29% since 1991 to 2017). However, the number of patients living with cancer will grow significantly in the near future due to the fact that one in two people will be diagnosed with cancer in their lifetime, while at the same time the average life expectancy increases.
As far as breast cancer is concerned, according to the CONCORD-3 study and based on data from 2010 to 2014, the 5-year net survival age-adjusted probability in all adults, in the 28 countries of the European Union (EU), ranges from 79% in Croatia to 93% in Cyprus. In 2018, the 5-year prevalence (number of people who have had a cancer diagnosis in the last 5 years) for breast cancer was in the absolute number of 2,054,887, from a total of 12,132,287 total cancer prevalence.
Regarding prostate cancer, the approximate number of new cases in the EU in 2015 is about 365,000 and is the most frequently diagnosed type of cancer in men. The incidence rates (ASR: age-adjusted rate on the European standard population) in the EU range from ASR 175 in Sweden to ASR 34 in Greece. The 5-year prevalence of prostate cancer in the EU is about 1,300,000, while at the same time survival has raised in all the EU countries with the highest increase monitored in the Eastern countries. The introduction and wide use of Prostate Specific Antigen (PSA) testing and diagnostic procedures such as biopsy have changed the distribution of the disease.
According to the numbers reported before, breast and prostate cancer survivorship represent a huge health problem for European countries. Breast and prostate cancer patients present psycho-social needs. Physical, social, and emotional scars could compromise return to everyday life. Different studies showed that almost a third of cancer survivors experienced changes in their work situation after treatment [9]. Some of the most common problems in returning to normal life after cancer are obtaining life or health insurance and home loans. The patient-centred approach is fundamental for improving the Quality of Life (QoL) of cancer patients
Motivated by the above, the aim of ASCAPE is to build an open Artificial Intelligence (AI) infrastructure for cancer patient support where valuable patient data-derived knowledge in the form of Deep Learning AI models from healthcare providers across can be collected and shared through the cloud while advanced technological means ensure patient data remain confidential. This data-derived knowledge is made available to doctors to aid them in their decisions and help provide a better Quality of Life trajectory to their patients. ASCAPE challenges the Iron Triangle of Health orthodoxy by offering opportunities for both Quality of Care and Access to Care to improve while the Cost of Care decreases.
As far as breast cancer is concerned, according to the CONCORD-3 study and based on data from 2010 to 2014, the 5-year net survival age-adjusted probability in all adults, in the 28 countries of the European Union (EU), ranges from 79% in Croatia to 93% in Cyprus. In 2018, the 5-year prevalence (number of people who have had a cancer diagnosis in the last 5 years) for breast cancer was in the absolute number of 2,054,887, from a total of 12,132,287 total cancer prevalence.
Regarding prostate cancer, the approximate number of new cases in the EU in 2015 is about 365,000 and is the most frequently diagnosed type of cancer in men. The incidence rates (ASR: age-adjusted rate on the European standard population) in the EU range from ASR 175 in Sweden to ASR 34 in Greece. The 5-year prevalence of prostate cancer in the EU is about 1,300,000, while at the same time survival has raised in all the EU countries with the highest increase monitored in the Eastern countries. The introduction and wide use of Prostate Specific Antigen (PSA) testing and diagnostic procedures such as biopsy have changed the distribution of the disease.
According to the numbers reported before, breast and prostate cancer survivorship represent a huge health problem for European countries. Breast and prostate cancer patients present psycho-social needs. Physical, social, and emotional scars could compromise return to everyday life. Different studies showed that almost a third of cancer survivors experienced changes in their work situation after treatment [9]. Some of the most common problems in returning to normal life after cancer are obtaining life or health insurance and home loans. The patient-centred approach is fundamental for improving the Quality of Life (QoL) of cancer patients
Motivated by the above, the aim of ASCAPE is to build an open Artificial Intelligence (AI) infrastructure for cancer patient support where valuable patient data-derived knowledge in the form of Deep Learning AI models from healthcare providers across can be collected and shared through the cloud while advanced technological means ensure patient data remain confidential. This data-derived knowledge is made available to doctors to aid them in their decisions and help provide a better Quality of Life trajectory to their patients. ASCAPE challenges the Iron Triangle of Health orthodoxy by offering opportunities for both Quality of Care and Access to Care to improve while the Cost of Care decreases.
During the first 18 months of the project several activities have been performed towards the realization of ASCAPE objectives as summarized below:
• Uniform HL7 FHIR compliant data format for breast cancer and prostate cancer patient including 21 QOL issues and interventions
• Architecture defined and implemented for edge / cloud
• Data import from multiple sources and formats, including wearables and weather data
• Federated learning and HE model training, best model selection, support change in participating edge nodes in federated learning.
• Explanation algorithms to provide explanations for medical personnel and conduct investigations on key data determinants
• Intervention suggestions through advanced simulations using best QOL models.
• Privacy-by Design mechanisms
• Security-by-Design mechanisms
• Trial setup, with 475-645 patients in 4 sites
• multi-faceted evaluation framework for ASCAPE to be applied in the context of the ASCAPE Pilots
• coordination of time-plan for the four pilots
• framework for the health economic assessment
• Preparation of the Pilots’ ICT Infrastructures
• Planning of the Open Call
• Uniform HL7 FHIR compliant data format for breast cancer and prostate cancer patient including 21 QOL issues and interventions
• Architecture defined and implemented for edge / cloud
• Data import from multiple sources and formats, including wearables and weather data
• Federated learning and HE model training, best model selection, support change in participating edge nodes in federated learning.
• Explanation algorithms to provide explanations for medical personnel and conduct investigations on key data determinants
• Intervention suggestions through advanced simulations using best QOL models.
• Privacy-by Design mechanisms
• Security-by-Design mechanisms
• Trial setup, with 475-645 patients in 4 sites
• multi-faceted evaluation framework for ASCAPE to be applied in the context of the ASCAPE Pilots
• coordination of time-plan for the four pilots
• framework for the health economic assessment
• Preparation of the Pilots’ ICT Infrastructures
• Planning of the Open Call
During the reporting period, the conducted research and development activities have progressed the state of the art in different areas. Representative cases follow:
• FHIR HL7 compliant representation of QOL issues and uniform list of interventions etc.
• Privacy-ensuring edge-cloud architecture
• Interfaces with doctors?
• Incremental and semi-concurrent federated learning schemes for cancer-care predictions
• FHIR HL7 compliant representation of QOL issues and uniform list of interventions etc.
• Privacy-ensuring edge-cloud architecture
• Interfaces with doctors?
• Incremental and semi-concurrent federated learning schemes for cancer-care predictions