Tian Peirong, Zhang Weitao, Zhao Hongmei, Lei Yutao, Cui Long, Wang Wei, Li Qingbo, Zhu Qing, Zhang Yuanfu, Xu Zhi
Department of General Surgery, Peking University Third Hospital Beijing, China.
School of Instrumentation Science and Opto-Electronics Engineering, Precision Opto-Mechatronics Technology Key Laboratory of Education Ministry, Beihang University Beijing, China.
Int J Clin Exp Med. 2015 Jan 15;8(1):972-81. eCollection 2015.
Fourier transform infrared (FTIR) spectroscopy has shown its unique advantages in distinguishing cancerous tissue from normal one. The aim of this study was to establish a quick and accurate diagnostic method of FTIR spectroscopy to differentiate malignancies from benign breast tissues intraoperatively.
In this study, a total of 100 breast tissue samples obtained from 100 patients were taken on surgery. All tissue samples were scanned for spectra intraoperatively before being processed for histopathological diagnosis. Standard normal variate (SNV) method was adopted to reduce scatter effects. Support vector machine (SVM) classification was used to discriminate spectra between malignant and benign breast tissues. Leave-one-out cross validation (LOOCV) was used to evaluate the discrimination.
According to histopathological examination, 50 cases were diagnosed as fibroadenoma and 50 cases as invasive ductal carcinoma. The results of SVM algorithm showed that the sensitivity, specificity and accuracy rate of this method are 90.0%, 98.0% and 94.0%, respectively.
FTIR spectroscopy technique in combination with SVM classification could be an accurate, rapid and objective tool to differentiate malignant from benign tumors during operation. Our studies establish the feasibility of FTIR spectroscopy with chemometrics method to guide surgeons during the surgery as an effective supplement for pathological diagnosis on frozen section.
傅里叶变换红外（FTIR）光谱在区分癌组织与正常组织方面已显示出其独特优势。本研究的目的是建立一种快速准确的FTIR光谱诊断方法，以便在术中区分乳腺恶性肿瘤与良性组织。
本研究共采集了100例患者手术时获取的100份乳腺组织样本。所有组织样本在进行组织病理学诊断前，术中均进行光谱扫描。采用标准正态变量（SNV）方法减少散射效应。使用支持向量机（SVM）分类法区分乳腺恶性组织与良性组织的光谱。采用留一法交叉验证（LOOCV）评估鉴别效果。
根据组织病理学检查，50例诊断为纤维腺瘤，50例诊断为浸润性导管癌。SVM算法结果显示，该方法的灵敏度、特异度和准确率分别为90.0%、98.0%和94.0%。
FTIR光谱技术结合SVM分类法可能是术中区分恶性肿瘤与良性肿瘤的一种准确、快速且客观的工具。我们的研究证实了FTIR光谱结合化学计量学方法在手术中指导外科医生的可行性，可作为冰冻切片病理诊断的有效补充。
