Chemometric histopathology via coherent Raman imaging for precision medicine (CHARM)

CHARM项目旨在从根本上改变癌症诊断过程，将新兴的数字组织病理学领域提升到一个新的水平，引入一种新的组织分析技术，能够测量患者组织样本的分子组成，并以完全无标记/无染色的方式识别和分类肿瘤。该仪器与人工智能（AI）集成，将为组织病理学家提供可靠，快速和低成本的临床决策支持系统（CDSS），用于癌症诊断和个性化癌症治疗。我们将开发一种C类（IVDR，体外诊断法规）医疗设备，包括一种交钥匙低成本宽带相干拉曼散射（CRS）显微镜（由我们的石墨烯基光纤激光专利技术实现），名为化学计量病理学系统（CPS），集成了基于深度学习，统计和机器学习的AI模块。CPS将能够自动分析未染色的组织，提供快速准确的肿瘤识别（区分正常组织与肿瘤组织），准确率>98%，并可预测最终肿瘤诊断（区分和分级组织学亚型）的准确性> 90%，从而为组织病理学家提供与现有临床协议兼容的决策树，但是具有基于生物分子的客观性和减少的结果时间（TRL6）。我们将为应用程序开发一个强大的商业案例，并确保项目继续达到更高的TRL和最终的市场准入。本提案以ERC POC项目GSYNCOR.no项目摘要的结果为基础。

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 biomolecularbased 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.no project summary.