Project description
Enhancing cancer care through data-driven decision support systems
Current clinical methods inadequately leverage unstructured data for the treatment of patients with prostate cancer (PCa) and kidney cancer (KC), resulting in suboptimal patient management and elevated costs. The effectiveness of diagnostics and therapeutics relies on a fusion of multimodal information. However, the deficiency of data access and the absence of collaborative validation between clinicians and computer scientists hinder the advancement of multimodal models. In this context, the EU-funded COMFORT project will develop commercially viable, data-driven, multimodal decision support systems. These systems seek to enhance clinical prognostication, patient stratification, and personalised therapy. It will also evaluate the confidence that healthcare professionals and patients place in such AI tools.
Objective
In the EU, treating patients with prostate (PCa) and kidney cancer (KC) costs more than €6.6 billion annually. Yet, PCa and KC are often managed inadequately, which is associated with high costs and negative consequences such as hospitalisation, psychosocial stress and poorer chances of survival. Diagnostic and therapeutic effectiveness depends on multimodal information, including cancer type, stage, and location as well as the patient’s age and health. Current clinical methods do not effectively use the large amount of mostly unstructured data. The main challenge in developing multimodal models is the lack of access to data sources and missing joint validation of data through collaboration between clinicians and computer scientists. A strength of our consortium is access to multiple sources of medical data, including the largest expert-annotated database for PCa and KC to date. Our overall goal is to develop and deploy marketable data-driven multimodal decision support systems to improve clinical prognosis, patient stratification and individual therapy for patients suffering from PCa or KC, defining a new state-of-the-art for the development of multimodal medical AI applications. We will develop AI models for PCa and KC that incorporate multimodal data, e.g. image data, unstructured medical text notes, laboratory information and biomarkers, and perform a prospective validation of the models in a large prospective multicentric international study. At the same time, we will assess the trust of healthcare professionals and patients in such AI tools and explore how this trust can be increased.
By providing improved, personalised diagnosis and prognosis assessment, the multimodal models will ultimately contribute to better patient outcomes and quality of life. The models developed in this study can be used as basis for any use case where imaging and electronic medical records are relevant, as they are easily adaptable and can help combat different types of cancer.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences computer and information sciences databases
- medical and health sciences clinical medicine oncology
You need to log in or register to use this function
We are sorry... an unexpected error occurred during execution.
You need to be authenticated. Your session might have expired.
Thank you for your feedback. You will soon receive an email to confirm the submission. If you have selected to be notified about the reporting status, you will also be contacted when the reporting status will change.
Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
-
HORIZON.2.1 - Health
MAIN PROGRAMME
See all projects funded under this programme -
HORIZON.2.1.5 - Tools, Technologies and Digital Solutions for Health and Care, including personalised medicine
See all projects funded under this programme
Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-RIA - HORIZON Research and Innovation Actions
See all projects funded under this funding scheme
Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-HLTH-2022-TOOL-12-two-stage
See all projects funded under this callCoordinator
Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
10117 Berlin
Germany
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.