Intraoperative margin assessment technique for breast conserving surgery using Raman spectroscopy imaging

基于拉曼光谱成像的保乳手术术中切缘评估技术

• 批准号: 548824-2019
• 负责人: Leblond, Frederic
• 金额: $ 19万
• 依托单位: École Polytechnique de Montréal
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=700985
• 关键词: Intraoperative; margin; assessment; technique; breast

Breast cancer is the most common among Canadian women excluding non-melanoma skin cancer: 1 in 8 Canadian women will be affected during their lifetime. Early diagnosis and effective treatment are critical factors for survival, and surgical tumor excision remains the front-line therapy. During these procedures, surgeons are often faced with critical decisions: remove too little tissue and risk recurrences or remove too much and risk physical and psychological damage. In breast cancer, 20-40% of all breast conserving surgery procedures result in margins with residual disease. Since cancer left at the margins is associated with an increased risk of recurrence, patients with positive margins always require a second operation. This high percentage of re-excision procedures leads to additional costs, patient anxiety and an increased risk of post-surgical complications. This project will lead to the development of a label-free (no need to inject a contrast agent), light-based technique to detect cancer during breast surgery. The intraoperative instrument will combine a new method called quantitative Raman and fluorescence spectroscopy with diffuse reflectance spatial-frequency domain imaging to provide tissue biomarkers able to detect the tissue signature associated with cancer cells based on morphological and biochemical properties. These developments will leverage recent advances in the Laboratory for Radiological Optics at Polytechnique Montreal and will set the stage for ODS Medical (the industry partner) to initiate commercialization of an instrument helping breast surgeons ensure completeness of tumour excision during lumpectomy procedures. The project will include the development of a cancer detection model based on machine learning techniques using data to be acquired in patients at the McGill University Health Center (MUHC). While there is a crucial need for the new label-free rapid imaging technology in breast cancer surgery, it can also find applications in a wide range of surgical oncology procedures, including neurosurgery, prostate and skin cancer surgery.

乳腺癌是不包括非黑色素瘤皮肤癌的加拿大妇女中最常见的：加拿大妇女一生中将受到影响。早期诊断和有效治疗是生存的关键因素，而手术肿瘤切除仍然是前线治疗。在这些过程中，外科医生通常会面临关键决定：清除过多的组织和风险复发或去除过多的物理和心理损害。在乳腺癌中，所有乳房保存手术程序的20-40％导致残留疾病的边缘。由于剩下的癌症与复发风险增加有关，因此阳性阳性的患者总是需要第二次手术。重新拆卸程序的这一比例很高会导致额外的成本，患者焦虑和后手术后并发症的风险增加。 该项目将导致不含标签的发展（无需注入对比剂），基于光的技术来检测乳腺手术期间的癌症。术中仪器将结合一种称为定量拉曼的新方法和荧光光谱与弥漫性反射率空间频率结构域成像，以提供能够基于形态和生物化学特性的癌细胞检测与癌细胞相关的组织特征的组织生物标志物。这些事态发展将利用蒙特利尔Polytechnique的放射学实验室实验室的最新进展，并将为ODS医疗（行业伙伴）奠定阶段，以启动仪器的商业化，以帮助乳房外科医生确保肿瘤切除术期间肿瘤切除的完整性。该项目将包括基于机器学习技术的癌症检测模型，使用麦吉尔大学健康中心（MUHC）的患者获得的数据。尽管在乳腺癌手术中对新的无标签快速成像技术的需求至关重要，但它还可以在包括神经外科，前列腺和皮肤癌手术在内的各种手术肿瘤学手术中找到应用。