摘要
为探究影像组学方法诊断乳腺病灶良恶性的能力,比较磁共振(MR:Magnetic Resonance)影像组学与传统MR诊断在良恶性乳腺疾病鉴别中的效能。回顾分析2019年1月-2022年1月在吉林大学第一医院放射科进行乳腺MR平扫及增强检查的患者,收集病理结果证实为良性或恶性的乳腺病灶共190例。MR影像组学方法通过建立逻辑回归模型实现诊断;传统MR诊断由一名副高级职称的影像科医生完成。结果显示测试集MR影像组学模型的灵敏度0.92,特异度0.83,曲线下面积(AUC:AreaUnder Curve)为0.92,以上数值均高于传统MR诊断的对应值,且差异具有统计学意义(P=0.00)。MR影像组学的方法可以辅助诊断乳腺病灶的良恶性,且诊断效能优于传统MR诊断模式。
In order to explore the ability of imaging to diagnose benign and malignant breast lesions, and compare the value of MR(Magnetic Resonance) radiomics and traditional MRI(Magnetic Resonance Imaging) in diagnosing breast diseases, a total of 190 cases with benign or malignant breast lesions confirmed by pathological findings are collected from patients who underwent MR Plain and enhanced examination in the Department of Radiology in First Hospital of Jilin University from January 2019 to January 2022. MR radiomics is performed by building logistic regression model. The traditional MR Diagnosis is performed by a radiologist with an associate senior title. The results show that the sensitivity, specificity and AUC(Area Under Curve) of the MR radiomics test set are 0.92, 0.83 and 0.92 respectively. The above values are higher than the corresponding values of traditional MR diagnosis, and the differences are statistically significant(P=0.00). The method of MR radiomics can assist in the diagnosis of benign and malignant breast lesions, and the diagnostic ability is better than the traditional MR diagnostic mode.
作者
郑冲
李明洋
兰文婧
刘香玉
包磊
纪铁凤
ZHENG Chong;LI Mingyang;LAN Wenjing;LIU Xiangyu;BAO Lei;JI Tiefeng(Department of Radiology,The First Hospital of Jilin University,Changchun 130021,China)
出处
《吉林大学学报(信息科学版)》
CAS
2023年第2期315-320,共6页
Journal of Jilin University(Information Science Edition)
基金
吉林省医疗卫生人才专项基金资助项目(3D5205164428)。
关键词
MR影像组学
机器学习
乳腺病灶
乳腺癌
magnetic resonance(MR)radiomics
machine learning
breast lesions
breast cancer