LifeChamps Project

Big Data for Cancer Treatment

What will we do?

Project Overview

The steady increase in life expectancy, mean age and cancer survivorship across the developed countries together with evidence from cancer and geriatrics care research bring forward the urgent need to deal with the “age issue” as a key component of global cancer care strategies. In 2012, an estimated 6.7 million new cases of cancer were diagnosed in adults aged 65 years and older, representing 47.5% of the total number of new cancer cases worldwide while 8% of the world population in that time was aged 65 years and older. On the other side, increasing age and comorbidities are often associated with a discriminant lower use of aggressive cancer therapies, as well as a higher neglection for their preferences in health‐related quality of life (HRQOL) care support.

To address the above, LifeChamps delivers a novel, context-aware and large-scale analytics framework capable of delivering multi-dimensional Quality of Life (QOL) support to all the different cancer life champions during and after their treatments. LifeChamps is providing support to middle aged and older (pre-frail and frail) cancer patients, as well as their caregivers and healthcare professionals, with an integrated Big Data-driven solution capable to improve their QOL via a timely and more accurate clinical decision support at the point of care. Its Artificial Intelligence (AI) and analytics engine, running both at the cloud and at the mobile edge, can determine accurately which factors affect the oncological patients’ QOL the most, during and after their treatment. Furthermore, complemented by a health recommender system LifeChamps offers personalized healthcare services (such as symptom monitoring, treatment and rehabilitation) to these patients and their caregivers. Finally, a multi-factorial frailty model will allow to stratify sub-clinical frail groups of geriatric cancer patients towards more personalized treatment.

Overall, LifeChamps aims at shifting the discrimination barrier for older cancer patients through the integration of different clinical and digital tools such as PROMs, PREMs, symptomatic and geriatric measures, mHealth and wearables, machine learning and artificial intelligence algorithms for Big Data. As its end result, Lifechamps will develop a novel, sophisticated and multifaceted clinical decision support tool for a comprehensive geriatric assessment. By doing so, LifeChamps, aims at including geriatric cancer patients as active members of the integrated care model, since care team coordination is feasible through the combination of cancer symptom monitoring and frailty modeling. Additionally, Lifechamps aims to expand the current base of knowledge for the healthcare support of older people with cancer as well as to create a digital intervention reflecting the suggested best practices in geriatric. From a technological viewpoint, multiple and heterogeneous data are constantly produced during the many cancer patients’ treatment stages. Up to now such data evolve in silos and are accessible by separate stakeholders (clinics, oncologists, geriatricians, caregivers, etc.) with no evident data correlations detection which could lead to patients-centric disruptive treatments. The LifeChamps project recognizes this and puts at the centre-stage of the efforts the use of state-of-the-art Big Data technologies to design strategies to address the QOL of cancer patients during and after cancer treatments. More specifically, based on a collaborative and personalized approach, LifeChamps envisages delivering an open, data-centric, secure and smart platform capable of supporting cancer champions in their endeavors from the moment of diagnosis to therapy and recovery.

The vision of LifeChamps is to address the inherent complexity caused by cancer treatments and to act in the monitoring of health status and improvement of quality of life in a significant manner by using emerging developments in the fields of Big Data, Data Analytics and Artificial Intelligence. Its innovative components will be built upon three pillars:

1

PREDICTION

Α prediction engine capable of providing comprehensive insights at the point of care in order to predict and prevent disorders, morbidities or cancer relapse associated with cancer treatment at an early stage; based on a Big Data-enabled HPC infrastructure with a broad knowledge base, where heterogeneous data from multiple sources is mapped, in a reachable and manageable way to create valuable insights;
2

CARE

A smart care model to timely address, in a continuous monitoring approach, symptoms responsible for affecting QOL, in particular, frailty;
3

ADVICE

Provide a collaborative structure to all target users: (i) older cancer patients - counseling in day to day activities with recommendations and advises on transversal lifestyle domains (e.g., nutrition, physical activity, social inclusion), (ii) physicians – supporting the clinical work by providing actionable insights on individual patient health and QOL trajectory, as well as visualization of aggregated data and their causalities and (iii) healthcare providers and healthcare systems – business intelligence dashboard to provide quality of service estimation.

Ultimately, LifeChamps will contribute decisively towards (a) increasing treatment safety, (b) improving efficiency of resource utilization, and (c) minimizing the conduct of unnecessary clinical procedures. The LifeChamps platform will be validated in four multi-national pilot use case scenarios aimed at demonstrating its applicability and validity for all the requirements of the SC1-DTH01-2019 Call (prediction, care, advice).

The LifeChamps project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875329. Horizon 2020 is the biggest EU Research and Innovation programme ever with nearly €80 billion of funding available over 7 years (2014 to 2020) – in addition to the private investment that this money will attract. It promises more breakthroughs, discoveries and world-firsts by taking great ideas from the lab to the market.

Project Details

ACRONYM
LifeChamps
FULL TITLE
LifeChamps: A Collective Intelligent Platform to Support Cancer Champions
GRANT AGREEMENT No
875329
TYPE OF ACTION
Research and Innovation Action (RIA)
TOPIC
H2020-SC1-DTH-2018-2020
START DATE
December 1, 2019
DURATION
3 Years (36 Months)
FUNDING
4,999,915.00 € (EUR)
COORDINATING PERSON
Professor Panagiotis Bamidis, AUTH, Greece
TECHNICAL MANAGER
Pedro Louro, Altran Portugal SA

Project Evaluation

To prove the applicability, usability, effectiveness and impact of the LifeChamps concepts, models and methods in industrial, real-life infrastructures, services and applications, the integrated LifeChamps platform will be validated in realistic clinical trials and conditions. Proof-of-concept pilots will run in four countries, following-up >250 older cancer patients (aged 50+), for the time frame of 12 months after they have been diagnosed with cancer. A comparative study between older adults at risk for frailty versus older adults at risk of frailty with a cancer diagnosis and active treatment will be conducted, allowing to study the underlying mechanisms, that the cancer treatment introduces and causes long-term effects related to frailty incidence. Multi-faceted assessment of the LifeChamps solution will be attempted, including the use of tools and methodologies, such as MAFEIP.

Overall Methodology

LifeChamps will follow an AGILE development approach comprising of the following four phases.

Phase 1 (Definition): The definition phase marks the beginning of the project and involves the identification of the user requirements that will drive the design of the LifeChamps platform, and the specification of the technical requirements of its components. To ensure a user driven approach, LifeChamps envisages a strong involvement of the end users in all three design and development cycles (1st, 2nd and final platform prototype).

Phase 2 (Implementation): The implementation phase takes off from the user requirements specification, and comprises a range of integrated, multidisciplinary research and technology tasks.

Phase 3 (Prototype & System Integration): With the successful completion of all tasks in phase 2, the stand-alone LifeChamps components will be integrated into an operational prototype platform that includes all the proposed LifeChamps components. The prototype will be tested, integrated and demonstrated in real environments for each PUC separately. Thus, there will be at least five Proof of Concepts (PoCs) to showcase the different features of the LifeChamps Platform.

Phase 4 (Testing & User Validation): In the final phase of the project, the LifeChamps platform will be validated in four real-life pilot use cases covering different scenarios.

Project Progress

START DATE
December 1, 2019
DURATION
3 Years (36 Months)

Overall Project Progress (in Months)

WP1

16%

WP2

33%

WP3

20%

WP4

20%

WP5

10%

WP6

0%

WP7

0%

WP8

16%

WP9

16%

*Progress data are updated every month

Work Plan Analysis

Work Plan Details

*Abbreviations explanation:
AUTH: Aristotle University of Thessaloniki, ALPT: Altran Portugal SA, ECPC: European Cancer Patient Coalition, EMC: Dell EMC, APC: Stockholm Health Provisions, UPV: Universitat Politècnica de València, HULAFE: Hospital La Fe, UOS: University of Surrey, KI: Karolinska Institutet, SALU: Salumedia Tecnologías SL, INTRA: Intrasoft International S.A., CLS: CyberLens B.V., MYSPHERA: MySphera SL, UofG: University of Glasgow, MDS: Massive Dynamic Sweden

WP1: Project Management | Lead Partner: AUTH
Task 1.1
Project Administration and Coordination
Lead Partner: AUTH
Task 1.2
Technical Management
Lead Partner: ALPT
Task 1.3
Risk Management and Quality Assurance
Lead Partner: AUTH
Task 1.4
Data Management and Regulatory Compliance
Lead Partner: CLS
WP2: Requirements Analysis, Pilot Use Cases and Functional Architecture | Lead Partner: UPV
Task 2.1
Scenario Thinking and Initial Technical and Business Requirements Definition
Lead Partner: HULAFE
Task 2.2
Co-creation with End-users/Stakeholders
Lead Partner: UofG
Task 2.3
Person Outcome Metrics
Lead Partner: UofG
Task 2.4
Overall System Design and Architecture
Lead Partner: UPV
Task 2.5
Legal, Ethical and Privacy Aspects
Lead Partner: CLS
WP3: Big Data Platform and HPC Infrastructure | Lead Partner: EMC
Task 3.1
Container and HPC libraries development
Lead Partner: EMC
Task 3.2
HPC Cloud Engine
Lead Partner: EMC
Task 3.3
LifeChamps Sensing Platform
Lead Partner: KI
Task 3.4
Data Ingestion, Aggregation and Streams Processing
Lead Partner: AUTH
Task 3.5
Data Warehouse Management (including Security)
Lead Partner: CLS
WP4: AI-enabled Data Analytics Engine | Lead Partner: ALPT
Task 4.1
Advanced Analytics & Exploratory Analysis
Lead Partner: UOS
Task 4.2
Predictive Analytics and Modeling
Lead Partner: ALPT
Task 4.3
Scalable Analytics Engine
Lead Partner: ALPT
Task 4.4
Edge Analytics Engine
Lead Partner: AUTH
Task 4.5
Vast Data Stream Across Multiple Clouds
Lead Partner: EMC
WP5: End User Applications and Clinical Decision Support | Lead Partner: SALU
Task 5.1
Quality of Life Assessment Monitoring and Modeling
Lead Partner: AUTH
Task 5.2
Frailty Domain Knowledge Models
Lead Partner: HULAFE
Task 5.3
Patient-Centered Recommendation Engine
Lead Partner: SALU
Task 5.4
LifeChamps Web-based Analytical Dashboard for Healthcare Professionals
Lead Partner: UPV
Task 5.5
LifeChamps AI-based Interactive End User Application for QOL Assessment
Lead Partner: SALU
WP6: LifeChamps Platform Integration and Testing | Lead Partner: INTRA
Task 6.1
LifeChamps platform integration plan and development environment setup
Lead Partner: INTRA
Task 6.2
LifeChams Platform Continuous Integration
Lead Partner: INTRA
Task 6.3
LifeChamps Platform Functional and Integration Testing
Lead Partner: INTRA
WP7: Pilots Specification, Demonstration and Evaluation | Lead Partner: AUTH
Task 7.1
Pilot Methodology and Planning
Lead Partner: AUTH
Task 7.2
Patient Recruitment and deployment of LifeChamps prototype
Lead Partner: UPV
Task 7.3
Small scale pilots for training data collection
Lead Partner: KI
Task 7.4
Real-life Pilots in Clinical and Home Environment
Lead Partner: HULAFE
Task 7.5
User Acceptance Evaluation
Lead Partner: APC
Task 7.6
Analysis of Effectiveness on Person and Care Outcomes
Lead Partner: APC
Task 7.7
Health Economic Analysis and Economic Impact
Lead Partner: AUTH
WP8: Dissemination, Communication and Exploitation of Results | Lead Partner: CLS
Task 8.1
Dissemination and Communication
Lead Partner: CLS
Task 8.2
Market Analysis, Business Models and Exploitation
Lead Partner: ALPT
Task 8.3
Contributions to Standards
Lead Partner: MDS
Task 8.4
Liaison and Interaction with Relevant Stakeholders
Lead Partner: ECPC