Pan F, Wu B, Jian X, Li C, Liu D, Zhang N
School of Biomedical Engineering, Capital Medical University, Beijing, 100069, China; Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, 100069, China; Department of Radiology, Beijing Fengtai Youanmen Hospital, Beijing, 100069, China.
School of Biomedical Engineering, Capital Medical University, Beijing, 100069, China; Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, 100069, China.
Clin Radiol. 2025 Jul;86:106941. doi: 10.1016/j.crad.2025.106941. Epub 2025 Apr 24.
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has proven to be highly sensitive in diagnosing breast tumours, due to the kinetic and volumetric features inherent in it. To utilise the kinetics-related and volume-related information, this paper aims to develop and validate a classification for differentiating benign and malignant breast tumours based on DCE-MRI, though fusing deep features and cross-attention-encoded radiomics features using discriminant correlation analysis (DCA).
Classification experiments were conducted on a dataset comprising 261 individuals who underwent DCE-MRI including those with multiple tumours, resulting in 137 benign and 163 malignant tumours. To improve the strength of correlation between features and reduce features' redundancy, a novel fusion method that fuses deep features and encoded radiomics features based on DCA (eFF-DCA) is proposed. The eFF-DCA includes three components: (1) a feature extraction module to capture kinetic information across phases, (2) a radiomics feature encoding module employing a cross-attention mechanism to enhance inter-phase feature correlation, and (3) a DCA-based fusion module that transforms features to maximise intra-class correlation while minimising inter-class redundancy, facilitating effective classification.
The proposed eFF-DCA method achieved an accuracy of 90.9% and an area under the receiver operating characteristic curve of 0.942, outperforming methods using single-modal features.
The proposed eFF-DCA utilises DCE-MRI kinetic-related and volume-related features to improve breast tumour diagnosis accuracy, but non-end-to-end design limits multimodal fusion. Future research should explore unified end-to-end deep learning architectures that enable seamless multimodal feature fusion and joint optimisation of feature extraction and classification.
动态对比增强磁共振成像（DCE-MRI）因其固有的动力学和体积特征，已被证明在诊断乳腺肿瘤方面具有高度敏感性。为了利用与动力学和体积相关的信息，本文旨在通过使用判别相关分析（DCA）融合深度特征和交叉注意力编码的放射组学特征，开发并验证一种基于DCE-MRI区分乳腺良恶性肿瘤的分类方法。
对一个包含261名接受DCE-MRI检查的个体（包括患有多个肿瘤的个体）的数据集进行分类实验，共得到137个良性肿瘤和163个恶性肿瘤。为了提高特征之间的相关性强度并减少特征冗余，提出了一种基于DCA融合深度特征和编码放射组学特征的新型融合方法（eFF-DCA）。eFF-DCA包括三个组件：（1）一个特征提取模块，用于跨阶段捕获动力学信息；（2）一个采用交叉注意力机制增强阶段间特征相关性的放射组学特征编码模块；（3）一个基于DCA的融合模块，该模块对特征进行变换，以最大化类内相关性，同时最小化类间冗余，便于进行有效分类。
所提出的eFF-DCA方法实现了90.9%的准确率和0.942的受试者工作特征曲线下面积，优于使用单模态特征的方法。
所提出的eFF-DCA利用DCE-MRI的动力学相关和体积相关特征提高了乳腺肿瘤诊断的准确性，但非端到端设计限制了多模态融合。未来的研究应探索统一的端到端深度学习架构，以实现无缝的多模态特征融合以及特征提取和分类的联合优化。
