Zhao You-Fan, Chen Zhongwei, Zhang Yang, Zhou Jiejie, Chen Jeon-Hor, Lee Kyoung Eun, Combs Freddie J, Parajuli Ritesh, Mehta Rita S, Wang Meihao, Su Min-Ying
Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Department of Radiological Sciences, University of California, Irvine, Irvine, CA, United States.
Front Oncol. 2021 Nov 17;11:774248. doi: 10.3389/fonc.2021.774248. eCollection 2021.
To build radiomics models using features extracted from DCE-MRI and mammography for diagnosis of breast cancer.
266 patients receiving MRI and mammography, who had well-enhanced lesions on MRI and histologically confirmed diagnosis were analyzed. Training dataset had 146 malignant and 56 benign, and testing dataset had 48 malignant and 18 benign lesions. Fuzzy-C-means clustering algorithm was used to segment the enhanced lesion on subtraction MRI maps. Two radiologists manually outlined the corresponding lesion on mammography by consensus, with the guidance of MRI maximum intensity projection. Features were extracted using PyRadiomics from three DCE-MRI parametric maps, and from the lesion and a 2-cm bandshell margin on mammography. The support vector machine (SVM) was applied for feature selection and model building, using 5 datasets: DCE-MRI, mammography lesion-ROI, mammography margin-ROI, mammography lesion+margin, and all combined.
In the training dataset evaluated using 10-fold cross-validation, the diagnostic accuracy of the individual model was 83.2% for DCE-MRI, 75.7% for mammography lesion, 64.4% for mammography margin, and 77.2% for lesion+margin. When all features were combined, the accuracy was improved to 89.6%. By adding mammography features to MRI, the specificity was significantly improved from 69.6% (39/56) to 82.1% (46/56), p<0.01. When the developed models were applied to the independent testing dataset, the accuracy was 78.8% for DCE-MRI and 83.3% for combined MRI+Mammography.
The radiomics model built from the combined MRI and mammography has the potential to provide a machine learning-based diagnostic tool and decrease the false positive diagnosis of contrast-enhanced benign lesions on MRI.
利用从动态对比增强磁共振成像（DCE-MRI）和乳腺钼靶摄影中提取的特征构建放射组学模型，用于乳腺癌诊断。
分析266例接受MRI和乳腺钼靶摄影检查且MRI上有强化良好的病变并经组织学确诊的患者。训练数据集包含146例恶性病变和56例良性病变，测试数据集包含48例恶性病变和18例良性病变。采用模糊C均值聚类算法在减影MRI图像上分割强化病变。两名放射科医生在MRI最大强度投影的指导下，通过协商手动勾勒出乳腺钼靶摄影上的相应病变。使用PyRadiomics从三个DCE-MRI参数图以及乳腺钼靶摄影上的病变和2厘米带状边缘提取特征。使用支持向量机（SVM）进行特征选择和模型构建，使用5个数据集：DCE-MRI、乳腺钼靶摄影病变感兴趣区（ROI）、乳腺钼靶摄影边缘ROI、乳腺钼靶摄影病变+边缘以及所有数据组合。
在使用10折交叉验证评估的训练数据集中，DCE-MRI个体模型的诊断准确率为83.2%，乳腺钼靶摄影病变为75.7%，乳腺钼靶摄影边缘为64.4%，病变+边缘为77.2%。当所有特征组合时，准确率提高到89.6%。通过将乳腺钼靶摄影特征添加到MRI中，特异性从69.6%（39/56）显著提高到82.1%（46/56），p<0.01。当将开发的模型应用于独立测试数据集时，DCE-MRI的准确率为78.8%，MRI+乳腺钼靶摄影组合的准确率为83.3%。
由MRI和乳腺钼靶摄影组合构建的放射组学模型有可能提供一种基于机器学习的诊断工具，并减少MRI上对比增强良性病变的假阳性诊断。
