基于多模态磁共振成像的影像组学用于乳腺良恶性病变的鉴别诊断
Radiomics Based on Multimodal MRI for the Differential Diagnosis of Benign and Malignant Breast Lesions.
作者信息
Zhang Qian, Peng Yunsong, Liu Wei, Bai Jiayuan, Zheng Jian, Yang Xiaodong, Zhou Lijuan
机构信息
Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China.
Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.
出版信息
J Magn Reson Imaging. 2020 Aug;52(2):596-607. doi: 10.1002/jmri.27098. Epub 2020 Feb 14.
BACKGROUND
MRI-based radiomics has been used to diagnose breast lesions; however, little research combining quantitative pharmacokinetic parameters of dynamic contrast-enhanced MRI (DCE-MRI) and diffusion kurtosis imaging (DKI) exists.
PURPOSE
To develop and validate a multimodal MRI-based radiomics model for the differential diagnosis of benign and malignant breast lesions and analyze the discriminative abilities of different MR sequences.
STUDY TYPE
Retrospective.
POPULATION
In all, 207 female patients with 207 histopathology-confirmed breast lesions (95 benign and 112 malignant) were included in the study. Then 159 patients were assigned to the training group, and 48 patients comprised the validation group.
FIELD STRENGTH/SEQUENCE: T -weighted (T W), T -weighted (T W), diffusion-weighted MR imaging (b-values = 0, 500, 800, and 2000 seconds/mm ) and quantitative DCE-MRI were performed on a 3.0T MR scanner.
ASSESSMENT
Radiomics features were extracted from T WI, T WI, DKI, apparent diffusion coefficient (ADC) maps, and DCE pharmacokinetic parameter maps in the training set. Models based on each sequence or combinations of sequences were built using a support vector machine (SVM) classifier and used to differentiate benign and malignant breast lesions in the validation set.
STATISTICAL TESTS
Optimal feature selection was performed by Spearman's rank correlation coefficients and the least absolute shrinkage and selection operator algorithm (LASSO). Receiver operating characteristic (ROC) curves were used to assess the diagnostic performance of the radiomics models in the validation set.
RESULTS
The area under the ROC curve (AUC) of the optimal radiomics model, including T WI, DKI, and quantitative DCE-MRI parameter maps was 0.921, with an accuracy of 0.833. The AUCs of the models based on T WI, T WI, ADC map, DKI, and DCE pharmacokinetic parameter maps were 0.730, 0.791, 0.770, 0.788, and 0.836, respectively.
DATA CONCLUSION
The model based on radiomics features from T WI, DKI, and quantitative DCE pharmacokinetic parameter maps has a high discriminatory ability for benign and malignant breast lesions.
LEVEL OF EVIDENCE
3 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:596-607.
背景
基于磁共振成像（MRI）的影像组学已用于诊断乳腺病变；然而，将动态对比增强MRI（DCE-MRI）的定量药代动力学参数与扩散峰度成像（DKI）相结合的研究较少。
目的
开发并验证基于多模态MRI的影像组学模型，用于鉴别乳腺良恶性病变，并分析不同磁共振序列的鉴别能力。
研究类型
回顾性研究。
研究对象
本研究共纳入207例经组织病理学证实的乳腺病变女性患者（95例良性病变和112例恶性病变）。然后将159例患者分配到训练组，48例患者组成验证组。
场强/序列：在3.0T MR扫描仪上进行T加权（TWI）、T加权（TWI）、扩散加权磁共振成像（b值 = 0、500、800和2000秒/平方毫米）以及定量DCE-MRI检查。
评估
从训练集中的TWI、TWI、DKI、表观扩散系数（ADC）图和DCE药代动力学参数图中提取影像组学特征。使用支持向量机（SVM）分类器建立基于每个序列或序列组合的模型，并用于在验证集中鉴别乳腺良恶性病变。
统计检验
通过Spearman等级相关系数和最小绝对收缩和选择算子算法（LASSO）进行最佳特征选择。采用受试者操作特征（ROC）曲线评估影像组学模型在验证集中的诊断性能。
结果
最佳影像组学模型（包括TWI、DKI和定量DCE-MRI参数图）的ROC曲线下面积（AUC）为0.921，准确率为0.833。基于TWI、TWI、ADC图、DKI和DCE药代动力学参数图的模型的AUC分别为0.730、0.791、0.770、0.788和0.836。
数据结论
基于TWI、DKI和定量DCE药代动力学参数图的影像组学特征模型对乳腺良恶性病变具有较高的鉴别能力。
证据水平
3 技术效能阶段：2 《磁共振成像杂志》2020年；52:596 - 607。
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