Project description
Coherent Raman microscopy in cancer diagnostics
The EU-funded CHARM project aims to bring cancer digital histopathology to the next level, introducing a technology capable of measuring a tissue’s molecular composition and characterising tumours in a label-free way. It will develop a medical instrument based on a broadband coherent Raman scattering microscope with an integrated AI module based on deep learning, statistics and machine learning. The integration with AI will offer a fast and reliable clinical decision support system for cancer diagnosis and personalised therapy. The developed chemometric pathology system will be capable of analysing unstained tissues, providing tumour identification with an accuracy of more than 98 % and tumour diagnosis prediction with an accuracy of more than 90 %.
Objective
The CHARM project aims to radically transform the cancer diagnosing process and bring the emerging field of digital histopathology to the next level, introducing a novel technology for tissue analysis, capable to measure the molecular composition of the patient tissue samples and to recognize and classify the tumor in a completely label/stain-free way. The instrument, integrated with artificial intelligence (AI), will offer to histopathologists a reliable, fast and low-cost Clinical Decision Support System (CDSS) for cancer diagnosis and personalized cancer therapy. We will develop a Class C, (IVDR, In-Vitro Diagnostic Regulation) medical device consisting of a turnkey low-cost broadband Coherent Raman Scattering (CRS) microscope (enabled by our patented graphene-based fiber laser technology), named the Chemometric Pathology System (CPS), integrating an AI module based on deep learning, statistics and machine learning. The CPS will be capable of automatically analyzing unstained tissues, providing fast and accurate tumour identification (differentiating normal vs neoplastic tissues) with accuracy >98% and final tumour diagnosis prediction (differentiating and grading histologic subtypes) with accuracy >90%, thus offering to the histopathologist a decision tree compatible with existing clinical protocols but with biomolecular-based objectivity and reduced time to result (TRL6). We will develop a robust business case for the application and ensure the project continuation to higher TRLs and the final market entrance. This proposal builds on the results of the ERC POC project GSYNCOR.
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: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
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Keywords
Programme(s)
- HORIZON.3.1 - The European Innovation Council (EIC) Main Programme
Topic(s)
Call for proposal
(opens in new window) HORIZON-EIC-2021-TRANSITION-CHALLENGES-01
See other projects for this callFunding Scheme
HORIZON-EIC - HORIZON EIC GrantsCoordinator
37126 Verona
Italy
The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.