SubsidieMeestersChemometric histopathology via coherent Raman imaging for precision medicine
The CHARM project aims to revolutionize cancer diagnosis with a novel AI-integrated, label-free tissue analysis system, achieving high accuracy in tumor identification and classification.
Projectdetails
Introduction
The CHARM project aims to radically transform the cancer diagnosing process and bring the emerging field of digital histopathology to the next level. It introduces a novel technology for tissue analysis, capable of measuring the molecular composition of patient tissue samples and recognizing and classifying tumors in a completely label/stain-free way.
Technology Overview
The instrument, integrated with artificial intelligence (AI), will offer histopathologists a reliable, fast, and low-cost Clinical Decision Support System (CDSS) for cancer diagnosis and personalized cancer therapy.
Medical Device Development
We will develop a Class C medical device (IVDR, In-Vitro Diagnostic Regulation) consisting of a turnkey low-cost broadband Coherent Raman Scattering (CRS) microscope. This device will be enabled by our patented graphene-based fiber laser technology and will be named the Chemometric Pathology System (CPS).
AI Integration
The CPS will integrate an AI module based on deep learning, statistics, and machine learning. It will be capable of automatically analyzing unstained tissues, providing fast and accurate tumor identification by differentiating normal versus neoplastic tissues with accuracy greater than 98%.
Diagnostic Accuracy
The system will also predict final tumor diagnosis by differentiating and grading histologic subtypes with accuracy greater than 90%. This will offer histopathologists a decision tree compatible with existing clinical protocols but with biomolecular-based objectivity and reduced time to result (TRL6).
Business Development
We will develop a robust business case for the application and ensure the project's continuation to higher TRLs and the final market entrance.
Project Foundation
This proposal builds on the results of the ERC POC project GSYNCOR.
Financiële details & Tijdlijn
Financiële details
| Subsidiebedrag | € 2.441.979 |
| Totale projectbegroting | € 2.441.979 |
Tijdlijn
| Startdatum | 1-5-2022 |
| Einddatum | 31-10-2025 |
| Subsidiejaar | 2022 |
Partners & Locaties
Projectpartners
- CAMBRIDGE RAMAN IMAGING SRLpenvoerder
- POLITECNICO DI MILANO
- UNIVERSITATSKLINIKUM JENA
- INSPIRALIA SOCIEDAD LIMITADA
- IN SRL IMPRESA SOCIALE
- CONSIGLIO NAZIONALE DELLE RICERCHE
- THE CHANCELLOR MASTERS AND SCHOLARS OF THE UNIVERSITY OF CAMBRIDGE
Land(en)
Vergelijkbare projecten binnen EIC Transition
| Project | Regeling | Bedrag | Jaar | Actie |
|---|---|---|---|---|
Enabling the transition to 3D digital pathology3DPATH aims to develop a clinically viable 3D tissue scanner using advanced light-sheet fluorescence microscopy to enhance histopathology accuracy and improve patient care globally. | EIC Transition | € 2.493.683 | 2025 | Details |
AI and Robotics for Prostate BiopsyThe ROBIOPSY project aims to develop a robotic prostate biopsy prototype for clinical trials, enhancing diagnostic accuracy and addressing health economics for improved cancer treatment. | EIC Transition | € 2.499.141 | 2023 | Details |
Radically New Cancer Therapy Based on Advances in Nanotechnology and Photonics for Simultaneous Imaging and Treatment of Solid TumoursScanNanoTreat aims to revolutionize cancer treatment by integrating advanced imaging and therapy technologies to improve patient outcomes and reduce costs, targeting clinical trials by 2027. | EIC Transition | € 2.499.911 | 2025 | Details |
Early detection of treatment response in breast cancerThe project aims to enhance breast cancer treatment through Hyperpolarized Magnetic Resonance imaging for early detection of non-responders, improving outcomes and reducing side effects. | EIC Transition | € 2.499.229 | 2024 | Details |
Enabling the transition to 3D digital pathology
3DPATH aims to develop a clinically viable 3D tissue scanner using advanced light-sheet fluorescence microscopy to enhance histopathology accuracy and improve patient care globally.
AI and Robotics for Prostate Biopsy
The ROBIOPSY project aims to develop a robotic prostate biopsy prototype for clinical trials, enhancing diagnostic accuracy and addressing health economics for improved cancer treatment.
Radically New Cancer Therapy Based on Advances in Nanotechnology and Photonics for Simultaneous Imaging and Treatment of Solid Tumours
ScanNanoTreat aims to revolutionize cancer treatment by integrating advanced imaging and therapy technologies to improve patient outcomes and reduce costs, targeting clinical trials by 2027.
Early detection of treatment response in breast cancer
The project aims to enhance breast cancer treatment through Hyperpolarized Magnetic Resonance imaging for early detection of non-responders, improving outcomes and reducing side effects.
Vergelijkbare projecten uit andere regelingen
| Project | Regeling | Bedrag | Jaar | Actie |
|---|---|---|---|---|
Backscattering coherent Stokes Raman scattering (sCiSsoRS) for real-time cancer diagnosticsThis project aims to enhance real-time cancer diagnosis during surgery by developing backward Coherent Stokes Raman Scattering (CSRS) for rapid, HE-like imaging of thick tissue samples. | ERC Consolid... | € 2.432.705 | 2024 | Details |
Deep Label-Free Cell Imaging of Liquid Biopsies for Cancer MonitoringDevelop and commercialize a label-free interferometric phase microscopy device with AI for cost-effective cancer diagnosis and monitoring via liquid biopsies. | ERC Proof of... | € 150.000 | 2023 | Details |
Fast and simple biomarker detection by computational microscopyWe developed a fast, sensitive biomarker detection method for early diagnosis and monitoring of cancer treatments, aiming to improve patient outcomes through preventative diagnostics. | ERC Proof of... | € 150.000 | 2024 | Details |
AI-based medical assistant - Herkennen en classificeren van tumoren m.b.v. kunstmatige intelligentieDit project ontwikkelt een geavanceerde beeldherkenningstechnologie voor realtime screening van eiwittypologieën in biopten, om de behandeling van complexe tumoren te verbeteren. | Mkb-innovati... | € 200.000 | 2018 | Details |
ADAPTIVE OPTICAL METASURFACES FOR REAL-TIME, LABEL-FREE AND NON-DESTRUCTIVE 7D DIGITAL PATHOLOGYOPTIPATH aims to revolutionize tissue diagnosis by providing real-time, non-destructive 3D imaging using advanced optical technologies and machine learning to enhance accuracy and reduce variability. | EIC Pathfinder | € 3.276.577 | 2025 | Details |
Backscattering coherent Stokes Raman scattering (sCiSsoRS) for real-time cancer diagnostics
This project aims to enhance real-time cancer diagnosis during surgery by developing backward Coherent Stokes Raman Scattering (CSRS) for rapid, HE-like imaging of thick tissue samples.
Deep Label-Free Cell Imaging of Liquid Biopsies for Cancer Monitoring
Develop and commercialize a label-free interferometric phase microscopy device with AI for cost-effective cancer diagnosis and monitoring via liquid biopsies.
Fast and simple biomarker detection by computational microscopy
We developed a fast, sensitive biomarker detection method for early diagnosis and monitoring of cancer treatments, aiming to improve patient outcomes through preventative diagnostics.
AI-based medical assistant - Herkennen en classificeren van tumoren m.b.v. kunstmatige intelligentie
Dit project ontwikkelt een geavanceerde beeldherkenningstechnologie voor realtime screening van eiwittypologieën in biopten, om de behandeling van complexe tumoren te verbeteren.
ADAPTIVE OPTICAL METASURFACES FOR REAL-TIME, LABEL-FREE AND NON-DESTRUCTIVE 7D DIGITAL PATHOLOGY
OPTIPATH aims to revolutionize tissue diagnosis by providing real-time, non-destructive 3D imaging using advanced optical technologies and machine learning to enhance accuracy and reduce variability.