Breast cancer is the most common malignant tumor among women and the second cause of cancer-related death. Early diagnosis in clinical practice is crucial for timely treatment and prognosis. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has revealed great usability in the preoperative diagnosis and assessing therapy effects thanks to its capability to reflect the morphology and dynamic characteristics of breast lesions. However, most existing computer-assisted diagnosis algorithms only consider conventional radiomic features when classifying benign and malignant lesions in DCE-MRI. In this study, we propose to fully leverage the dynamic characteristics from the kinetic curves as well as the radiomic features to boost the classification accuracy of benign and malignant breast lesions. The proposed method is a fully automated solution by directly analyzing the 3D features from the DCE-MRI. The proposed method is evaluated on an in-house dataset including 200 DCE-MRI scans with 298 breast tumors (172 benign and 126 malignant tumors), achieving favorable classification accuracy with an area under curve (AUC) of 0.94. By simultaneously considering the dynamic and radiomic features, it is beneficial to effectively distinguish between benign and malignant breast lesions.

翻译：乳腺癌是女性最常见的恶性肿瘤，也是癌症相关死亡的第二大原因。临床实践中的早期诊断对及时治疗和预后至关重要。动态对比增强磁共振成像（DCE-MRI）因其能够反映乳腺病变的形态学和动力学特征，在术前诊断和评估治疗效果方面展现出巨大的实用性。然而，现有大多数计算机辅助诊断算法在分类DCE-MRI良恶性病变时仅考虑常规影像组学特征。本研究提出充分利用动力学曲线的动态特征及影像组学特征，以提高乳腺良恶性病变的分类准确性。所提出的方法是一种全自动解决方案，可直接分析DCE-MRI的三维特征。我们在包含200例DCE-MRI扫描（共298个乳腺肿瘤，其中良性172个、恶性126个）的内部数据集上评估了该方法，取得了良好的分类准确性，曲线下面积（AUC）为0.94。同时考虑动态特征与影像组学特征，有助于有效区分乳腺良恶性病变。