A Radiomics Model for Preoperative Predicting Sentinel Lymph Node Metastasis in Breast Cancer Based on Dynamic Contrast-Enhanced MRI

医学 无线电技术 乳腺癌 乳房磁振造影 接收机工作特性 列线图 放射科 转移 前哨淋巴结 淋巴结 动态增强MRI 磁共振成像 内科学 肿瘤科 癌症 乳腺摄影术
作者
Mingming Ma,Yuan Jiang,Naishan Qin,Xiaodong Zhang,Yaofeng Zhang,Xiangpeng Wang,Xiaoying Wang
出处
期刊:Frontiers in Oncology [Frontiers Media SA]
卷期号:12 被引量:4
标识
DOI:10.3389/fonc.2022.884599
摘要

Purpose To develop a radiomics model based on preoperative dynamic contrast-enhanced MRI (DCE-MRI) to identify sentinel lymph node (SLN) metastasis in breast cancer (BC) patients. Materials and Methods The MRI images and clinicopathological data of 142 female primary BC patients from January 2017 to December 2018 were included in this study. The patients were randomly divided into the training and testing cohorts at a ratio of 7:3. Four types of radiomics models were built: 1) a radiomics model based on the region of interest (ROI) of breast tumor; 2) a radiomics model based on the ROI of intra- and peri-breast tumor; 3) a radiomics model based on the ROI of axillary lymph node (ALN); 4) a radiomics model based on the ROI of ALN and breast tumor. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were used to assess the performance of the three radiomics models. The technique for order of preference by similarity to ideal solution (TOPSIS) through decision matrix analysis was used to select the best model. Results Models 1, 2, 3, and 4 yielded AUCs of 0.977, 0.999, 0.882, and 1.000 in the training set and 0.699, 0.817, 0.906, and 0.696 in the testing set, respectively, in terms of predicting SLN metastasis. Model 3 had the highest AUC in the testing cohort, and only the difference from Model 1 was statistically significant ( p = 0.022). DCA showed that Model 3 yielded a greater net benefit to predict SLN metastasis than the other three models in the testing cohort. The best model analyzed by TOPSIS was Model 3, and the method’s names for normalization, dimensionality reduction, feature selection, and classification are mean, principal component analysis (PCA), ANOVA, and support vector machine (SVM), respectively. Conclusion ALN radiomics feature extraction on DCE-MRI is a potential method to evaluate SLN status in BC patients.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
fiona完成签到,获得积分10
1秒前
PaoPao发布了新的文献求助10
1秒前
¥#¥-11发布了新的文献求助10
1秒前
科研通AI5应助和花花采纳,获得10
1秒前
yellow完成签到 ,获得积分10
1秒前
2秒前
晴悦发布了新的文献求助10
2秒前
2秒前
Owen应助xiao.yang采纳,获得10
2秒前
谨慎飞丹完成签到 ,获得积分10
4秒前
Jeffrey完成签到,获得积分10
4秒前
dropwater完成签到,获得积分10
5秒前
牧析山发布了新的文献求助10
5秒前
5秒前
MHJJJ发布了新的文献求助30
5秒前
微笑完成签到,获得积分10
6秒前
闪闪纸飞机完成签到,获得积分10
6秒前
7秒前
Nextone完成签到,获得积分10
7秒前
赘婿应助疯狂的小蘑菇采纳,获得30
8秒前
123完成签到,获得积分10
8秒前
Akim应助niekyang采纳,获得10
8秒前
实验室扛把子完成签到,获得积分10
9秒前
舒心的钻石完成签到,获得积分10
10秒前
11秒前
12秒前
右旋王小二完成签到,获得积分10
12秒前
123发布了新的文献求助30
13秒前
13秒前
酷酷宝马发布了新的文献求助10
13秒前
13秒前
14秒前
16秒前
英俊的铭应助流浪采纳,获得10
16秒前
17秒前
和花花发布了新的文献求助10
17秒前
小魏哥哥发布了新的文献求助10
18秒前
自然函完成签到,获得积分10
19秒前
centlay发布了新的文献求助10
19秒前
高分求助中
Comparative Anatomy of the Vertebrates 9th 3000
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Les Mantodea de Guyane Insecta, Polyneoptera 1000
Structural Load Modelling and Combination for Performance and Safety Evaluation 1000
Conference Record, IAS Annual Meeting 1977 820
England and the Discovery of America, 1481-1620 600
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3571872
求助须知:如何正确求助?哪些是违规求助? 3142287
关于积分的说明 9446687
捐赠科研通 2843683
什么是DOI,文献DOI怎么找? 1562971
邀请新用户注册赠送积分活动 731530
科研通“疑难数据库(出版商)”最低求助积分说明 718557