Objective
In the fight against cancer, it is well recognized that tumors are highly heterogeneous and they might differ considerably not only between tumors types but also among tumors of the same type or even for the same tumor during progression. As a result, the efficacy of standard cancer therapies varies, and while some patients respond to a particular treatment, other patients do not gain any benefit. Consequently, crucial in cancer therapy is the prediction of a patients response to treatment. Failure of standard therapies has led to the introduction of a new era of personalized, patient-specific treatments, which are based on the identification of biomarkers that characterize the state of a particular tumor. Many solid tumors (e.g. breast cancers and sarcomas) stiffen as they grow in a hosts normal tissue. Tumor stiffening is a known factor leading to compromised efficacy of therapeutics. Recently, it has been demonstrated by our team and co-workers that repurposing of common drugs with anti-fibrotic properties, known as mechanotherapeutics, target tumor stiffness and enhance therapy. Here, we aim to harness the power of deep learning (DL) methods in order to develop a robust biomarker based on ultrasound shear wave elastography (SWE). This biomarker will aim to: (i) predict patients response to treatment, separating responders and non-responders and (ii) monitor treatment outcomes, in the case of strategies that target tumor stiffness (i.e. mechanotherapeutics). The DL algorithms will be applied to a large set of existing preclinical data and to additional new data. A proof of concept clinical study on sarcoma patients will accommodate the clinical translation of the biomarker. Furthermore, we propose to develop a software product to be used as a commercial tool for the measurement of the DL-derived, SWE biomarker. A planned market research will highlight the best options for commercialization.
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 software
- medical and health sciences clinical medicine oncology
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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.
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HORIZON.1.1 - European Research Council (ERC)
MAIN PROGRAMME
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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-ERC-POC - HORIZON ERC Proof of Concept Grants
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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) ERC-2022-POC1
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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.
1678 Nicosia
Cyprus
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.