采用便携式拉曼光谱仪鉴别乳腺病灶良恶性及腋窝淋巴结转移相关模型的构建

项目名称： 采用便携式拉曼光谱仪鉴别乳腺病灶良恶性及腋窝淋巴结转移相关模型的构建

项目编号： No.81202078

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 肿瘤学2

项目作者： 韩冰

作者单位： 吉林大学

项目金额： 23万元

中文摘要： 目前对于乳腺检查所发现的异常，多只能通过手术活检明确病变性质，但是总体活检后约70-90%的患者证实为良性病变，而做了不必要的手术活检。前哨淋巴结活检转移与否的术中判定也一直没有准确而快速的方法。本研究采用便携式拉曼光谱检测新鲜乳腺病灶、正常乳腺组织及腋窝淋巴结，通过检测结果所反映的组织中化学成分的差异（包括钙化灶、蛋白质构成、脂肪构成等）构建数学模型，鉴别病变的性质（良性、恶性、癌前病变及淋巴结有无转移等）；而且利用拉曼光谱在检测早期病变上的优势，区分不同性质的癌前病变与乳腺癌光谱结果的差异，探索癌前病变与乳腺癌的内在联系，为改良预防性治疗方法提供理论依据。本研究采用新鲜组织和便携式拉曼光谱仪所得数据来建立模型主要目的是为进一步非手术方法确定病变性质和术中即时判断腋窝淋巴结转移与否奠定研究基础，减少不必要的手术活检，节约医疗资源，减轻患者的经济和心理负担。

中文关键词： 乳腺癌；拉曼光谱；非典型增生；支持向量机；表面增强拉曼光谱

英文摘要： Breast biopsy is usually recommended on most patients diagnosed with a breast irregularity such as a lump or an abnormal finding seen on a mammogram or a breast ultrasound. These screening methods have some limitations, and upon biopsy 70% to 90% of detected lesions are found to be benign. As a result patients often complain of having undergone an unnecessary surgical intervention. To date, no accurate and fast method can yet be satisfactorilly applied to identify metastasis accuratelly and/or determine sentinel lymph nodes during the time of the operation.In this study, portable Raman spectroscopy is used to compare fresh breast lesions and metastasis lymphy nodes with normal breast tissues and lymphy nodes. For this, we constructed a mathematical model using Raman spectra results obtained from normal and malignant tissues and based on various biochemical characteristics such as calcification, protein structure and fat structure. This algorithm allowed to identify early pathological changes. Most importantly it allowed to distinguish the nature of the breast tissue (normal, benign and malignant) and explore the inner link between breast cancer and premalignant lesions. In the study, the main purpose of using the fresh organization and portable Raman spectrometer to construct the model is to prepare to identify

英文关键词： breast cancer；Raman spectroscopy；atypical ductal hyperplasia (ADH)；support vector machine(SVM)；Surface-enhanced Raman scattering (SERS)