Cui Yanhua, Li Yun, Xing Dong, Bai Tong, Dong Jiwen, Zhu Jian
Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
Front Oncol. 2021 Mar 22;11:629321. doi: 10.3389/fonc.2021.629321. eCollection 2021.
Breast cancer is one of the leading causes of death in female cancer patients. The disease can be detected early using Mammography, an effective X-ray imaging technology. The most important step in mammography is the classification of mammogram patches as benign or malignant. Classically, benign or malignant breast tumors are diagnosed by radiologists' interpretation of mammograms based on clinical parameters. However, because masses are heterogeneous, clinical parameters supply limited information on mammography mass. Therefore, this study aimed to predict benign or malignant breast masses using a combination of image biomarkers and clinical parameters. We trained a deep learning (DL) fusion network of VGG16 and Inception-V3 network in 5,996 mammography images from the training cohort; DL features were extracted from the second fully connected layer of the DL fusion network. We then developed a combined model incorporating DL features, hand-crafted features, and clinical parameters to predict benign or malignant breast masses. The prediction performance was compared between clinical parameters and the combination of the above features. The strengths of the clinical model and the combined model were subsequently validated in a test cohort ( = 244) and an external validation cohort ( = 100), respectively. Extracted features comprised 30 hand-crafted features, 27 DL features, and 5 clinical features (shape, margin type, breast composition, age, mass size). The model combining the three feature types yielded the best performance in predicting benign or malignant masses (AUC = 0.961) in the test cohort. A significant difference in the predictive performance between the combined model and the clinical model was observed in an independent external validation cohort (AUC: 0.973 vs. 0.911, p = 0.019). The prediction of benign or malignant breast masses improves when image biomarkers and clinical parameters are combined; the combined model was more robust than clinical parameters alone.
乳腺癌是女性癌症患者的主要死因之一。使用乳房X线摄影术（一种有效的X射线成像技术）可以早期检测出这种疾病。乳房X线摄影术中最重要的步骤是将乳房X线照片斑块分类为良性或恶性。传统上，良性或恶性乳腺肿瘤是由放射科医生根据临床参数对乳房X线照片进行解读来诊断的。然而，由于肿块具有异质性，临床参数提供的关于乳房X线摄影肿块的信息有限。因此，本研究旨在结合图像生物标志物和临床参数来预测良性或恶性乳腺肿块。我们在来自训练队列的5996张乳房X线摄影图像中训练了一个由VGG16和Inception-V3网络组成的深度学习（DL）融合网络；DL特征是从DL融合网络的第二个全连接层中提取的。然后，我们开发了一个结合DL特征、手工特征和临床参数的组合模型，以预测良性或恶性乳腺肿块。比较了临床参数与上述特征组合之间的预测性能。随后，分别在一个测试队列（n = 244）和一个外部验证队列（n = 100）中验证了临床模型和组合模型的优势。提取的特征包括30个手工特征、27个DL特征和5个临床特征（形状、边缘类型、乳腺组成、年龄、肿块大小）。在测试队列中，结合三种特征类型的模型在预测良性或恶性肿块方面表现最佳（AUC = 0.961）。在一个独立的外部验证队列中，观察到组合模型和临床模型在预测性能上存在显著差异（AUC：0.973对0.911，p = 0.019）。当结合图像生物标志物和临床参数时，良性或恶性乳腺肿块的预测得到改善；组合模型比单独的临床参数更稳健。
