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. The algorithm is publicly available at https://github.com/ryandok/JPA.

翻译：乳腺癌是女性最常见的恶性肿瘤，也是导致癌症相关死亡的第二大原因。临床实践中早期诊断对于及时治疗和预后判断至关重要。动态增强磁共振成像凭借其反映乳腺病变形态学和动力学特征的能力，在术前诊断和疗效评估中展现出显著价值。然而，现有大多数计算机辅助诊断算法在DCE-MRI图像中分类乳腺良恶性病变时仅考虑传统影像组学特征。本研究提出充分利用动力学曲线中的动态特征及影像组学特征，以提升乳腺良恶性病变的分类准确率。所提方法通过直接分析DCE-MRI的三维特征实现全自动化解决方案。基于包含200例DCE-MRI扫描图像（涵盖298个乳腺肿瘤，其中良性172例、恶性126例）的内部数据集评估，该方法取得了良好的分类准确率，曲线下面积达0.94。通过同时结合动态特征与影像组学特征，可有效区分乳腺良恶性病变。本算法开源地址为：https://github.com/ryandok/JPA。