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.

乳腺癌是加拿大女性中最常见的癌症（不包括非黑色素瘤皮肤癌）：八分之一的加拿大女性会在一生中受到影响。早期诊断和有效治疗是生存的关键因素，手术肿瘤切除仍然是一线治疗方法。在这些手术过程中，外科医生经常面临关键的决定：切除太少的组织有复发的风险，或者切除太多的组织有身体和心理损伤的风险。在乳腺癌中，20-40% 的保乳手术会导致边缘存在残留病灶。由于留在边缘的癌症与复发风险增加相关，因此边缘阳性的患者总是需要进行第二次手术。这种高比例的再切除手术会导致额外的费用、患者的焦虑以及术后并发症的风险增加。 该项目将开发一种无标记（无需注射造影剂）、基于光的技术，用于在乳房手术期间检测癌症。该术中仪器将一种称为定量拉曼和荧光光谱的新方法与漫反射空间频域成像相结合，提供组织生物标志物，能够根据形态和生化特性检测与癌细胞相关的组织特征。这些发展将利用蒙特利尔理工学院放射光学实验室的最新进展，并为 ODS Medical（行业合作伙伴）启动仪器的商业化奠定基础，帮助乳腺外科医生确保肿瘤切除术中肿瘤切除的完整性。该项目将包括使用从麦吉尔大学健康中心（MUHC）患者获取的数据开发基于机器学习技术的癌症检测模型。虽然乳腺癌手术迫切需要新型无标记快速成像技术，但它也可以在广泛的肿瘤外科手术中找到应用，包括神经外科、前列腺癌和皮肤癌手术。