波谱学杂志 ›› 2022, Vol. 39 ›› Issue (3): 278-290.doi: 10.11938/cjmr20212916

• 研究论文 • 上一篇    下一篇

基于新型支持向量机的影像组学在肝脏结节分类中的应用

李笛1,霍雷2,万梦云1,贾宁阳2,王丽嘉1,*()   

  1. 1. 上海理工大学 医疗器械与食品学院, 上海 200093
    2. 第二军医大学附属东方肝胆外科医院 影像科, 上海 200438
  • 收稿日期:2021-05-22 出版日期:2022-09-05 发布日期:2021-08-20
  • 通讯作者: 王丽嘉 E-mail:lijiawangmri@163.com
  • 基金资助:
    国家科技部十三五传染病重大专项课题(2018ZX10302207-004-005)

Application of Radiomics Based on New Support Vector Machine in the Classification of Hepatic Nodules

Di LI1,Lei HUO2,Meng-yun WAN1,Ning-yang JIA2,Li-jia WANG1,*()   

  1. 1. School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
    2. Department of Radiology, Eastern Hepatobiliary Hospital, Second Military Medical University, Shanghai 200438, China
  • Received:2021-05-22 Online:2022-09-05 Published:2021-08-20
  • Contact: Li-jia WANG E-mail:lijiawangmri@163.com

摘要:

肝癌是最常见的恶性肿瘤之一,亚洲地区最为常见的肝癌演变过程为肝炎-肝硬化结节-异型增生结节-肝细胞性肝癌.判断肝脏结节在演变过程所处分期,并采取干预措施,对降低肝癌的发生率非常关键.本文针对影像组学提出了更精确的支持向量机(SVM)分类算法——LFOA-F-SVM,用于对120名患者的腹部动态增强磁共振图像的肝脏结节进行四分类.该算法利用了考虑半径与几何间距的F-SVM,并结合莱维飞行策略(LF)的果蝇优化算法(FOA)寻求超参.为了验证方法的有效性,本文另外添加了5个UCI分类数据集(心脏、帕金森疾病、虹膜、葡萄酒和动物园),并与SVM、PSO-SVM、FOA-SVM、F-SVM进行比较.结果表明,在6个分类数据集(包括肝脏结节数据集和5个UCI分类数据集)中,相对于其他分类算法,LFOA-F-SVM的分类准确率最高,在肝脏结节数据集中的四分类精确率和查全率也较高.

关键词: 肝脏结节, 分类, 影像组学, LFOA-F-SVM

Abstract:

Liver cancer is one of the most common malignant tumors. In Asia, liver cancer often develops on a background of cirrhosis caused by chronic hepatitis. The procedure of hepatitis, cirrhotic nodules, dysplastic nodules, and then hepatocellular carcinoma is the most common liver cancer evolutionary process. Judging the stage of hepatic nodules in the evolution process and taking intervention measures are critical for reducing the incidence of liver cancer. In this paper, a more accurate support vector machine (SVM) classification algorithm, LFOA-F-SVM, was proposed for radiomics to classify hepatic nodules from 120 patients into four categories based on dynamic enhanced magnetic resonance images. The algorithm uses radius-margin-based F-SVM, and combines the fruit fly optimization algorithm (FOA) of Levy flight (LF) strategy to optimize the parameters. To verify the effectiveness of the method, five UCI classification data sets (hearts, Parkinson’s disease, iris, wine and zoo) were added and compared with SVM, PSO-SVM, FOA-SVM, F-SVM. The results showed that LFOA-F-SVM has the highest classification accuracy in six data sets compared to the other methods. And in the hepatic nodules data set, the classification precision and recall are relatively high.

Key words: hepatic nodules, classification, radiomics, LFOA-F-SVM

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