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 patient’s 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 host’s 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 patient’s 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 natural sciencescomputer and information sciencessoftwaremedical and health sciencesclinical medicineoncologynatural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learningnatural sciencesphysical sciencesacousticsultrasound Programme(s) HORIZON.1.1 - European Research Council (ERC) Main Programme Topic(s) ERC-2022-POC1 - ERC PROOF OF CONCEPT GRANTS1 Call for proposal ERC-2022-POC1 See other projects for this call Funding Scheme HORIZON-AG-LS - HORIZON Lump Sum Grant Host institution UNIVERSITY OF CYPRUS Net EU contribution € 150 000,00 Address AVENUE PANEPISTIMIOU 2109 AGLANTZI 1678 Nicosia Cyprus See on map Region Κύπρος Κύπρος Κύπρος Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Total cost No data Beneficiaries (1) Sort alphabetically Sort by Net EU contribution Expand all Collapse all UNIVERSITY OF CYPRUS Cyprus Net EU contribution € 150 000,00 Address AVENUE PANEPISTIMIOU 2109 AGLANTZI 1678 Nicosia See on map Region Κύπρος Κύπρος Κύπρος Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Total cost No data
