Zhou Jiejie, Liu Yan-Lin, Zhang Yang, Chen Jeon-Hor, Combs Freddie J, Parajuli Ritesh, Mehta Rita S, Liu Huiru, Chen Zhongwei, Zhao Youfan, Pan Zhifang, Wang Meihao, Yu Risheng, Su Min-Ying
Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Front Oncol. 2021 Nov 1;11:728224. doi: 10.3389/fonc.2021.728224. eCollection 2021.
A wide variety of benign and malignant processes can manifest as non-mass enhancement (NME) in breast MRI. Compared to mass lesions, there are no distinct features that can be used for differential diagnosis. The purpose is to use the BI-RADS descriptors and models developed using radiomics and deep learning to distinguish benign from malignant NME lesions.
A total of 150 patients with 104 malignant and 46 benign NME were analyzed. Three radiologists performed reading for morphological distribution and internal enhancement using the 5th BI-RADS lexicon. For each case, the 3D tumor mask was generated using Fuzzy-C-Means segmentation. Three DCE parametric maps related to wash-in, maximum, and wash-out were generated, and PyRadiomics was applied to extract features. The radiomics model was built using five machine learning algorithms. ResNet50 was implemented using three parametric maps as input. Approximately 70% of earlier cases were used for training, and 30% of later cases were held out for testing.
The diagnostic BI-RADS in the original MRI report showed that 104/104 malignant and 36/46 benign lesions had a BI-RADS score of 4A-5. For category reading, the kappa coefficient was 0.83 for morphological distribution (excellent) and 0.52 for internal enhancement (moderate). Segmental and Regional distribution were the most prominent for the malignant group, and focal distribution for the benign group. Eight radiomics features were selected by support vector machine (SVM). Among the five machine learning algorithms, SVM yielded the highest accuracy of 80.4% in training and 77.5% in testing datasets. ResNet50 had a better diagnostic performance, 91.5% in training and 83.3% in testing datasets.
Diagnosis of NME was challenging, and the BI-RADS scores and descriptors showed a substantial overlap. Radiomics and deep learning may provide a useful CAD tool to aid in diagnosis.
多种良性和恶性病变在乳腺MRI中可表现为非肿块强化（NME）。与肿块病变相比，没有可用于鉴别诊断的明显特征。目的是使用BI-RADS描述符以及利用放射组学和深度学习开发的模型来区分良性和恶性NME病变。
共分析了150例患者，其中有104例恶性NME和46例良性NME。三名放射科医生使用第5版BI-RADS词典对形态分布和内部强化进行解读。对于每个病例，使用模糊C均值分割生成3D肿瘤掩码。生成与流入、最大值和流出相关的三个DCE参数图，并应用PyRadiomics提取特征。使用五种机器学习算法构建放射组学模型。以三个参数图作为输入实现ResNet50。大约70%的早期病例用于训练，30%的后期病例留作测试。
原始MRI报告中的诊断性BI-RADS显示，104/104例恶性病变和36/46例良性病变的BI-RADS评分为4A-5。对于类别解读，形态分布的kappa系数为0.83（优秀），内部强化的kappa系数为0.52（中等）。恶性组中节段性和区域性分布最为突出，良性组中局灶性分布最为突出。支持向量机（SVM）选择了八个放射组学特征。在五种机器学习算法中，SVM在训练数据集中的准确率最高，为80.4%，在测试数据集中为77.5%。ResNet50具有更好的诊断性能，在训练数据集中为91.5%，在测试数据集中为83.3%。
NME的诊断具有挑战性，BI-RADS评分和描述符显示出大量重叠。放射组学和深度学习可能提供一种有用的计算机辅助诊断工具来辅助诊断。
