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.

Many human pathologies such as cancer are due to complex biochemical alterations that start at a sub-cellular level and lead to progressive changes that result in a heterogeneous tumor composition. The polyclonality of tumor cells hampers the diagnosis and the therapy giving rise to tumor clones that lead to therapy resistance and promote metastases. An accurate diagnosis of tumor biopsies to identify these particular cell clones is crucial to provide targeted therapy tailored to the tumor characteristics, to improve the patient outcomes and increase survival rates. For this vision to come true, we introduce ulTRafast hOlograPHic FT-IR microscopY (TROPHY) as a paradigm shift in vibrational microscopy, blending elements of photo-thermal infrared (PT-IR), Fourier transform (FT)-IR, and Digital Holography Microscopy (DHM). TROPHY brings these techniques to the unprecedented ultrafast timescale, where the refractive index change induced by coherent IR vibrations is probed at its peak value before thermal relaxation. TROPHY borrows from PT-IR the combination of IR vibrational excitation with visible probing for high spatial resolution, from FT-IR the use of time-domain interferometry to obtain a high spectral resolution from broadband excitation, from DHM highly sensitive and quantitative detection of the refractive index (phase) change. Combined with artificial intelligence algorithms, this technology will enable quantitative concentration imaging of molecular biomarkers with high spatial resolution, high chemical selectivity and high speed, with a transformative impact on medical research and clinics. In oncology, it will be applied to intraoperative diagnosis of tumor biopsies, providing tumor grading, staging and subtyping, and supporting complete tumor resection. It will also allow to determine the best therapeutic approach tailored to the patient and identify resistant tumor clones under targeted therapy, paving the way for precision medicine in cancer.