College of Biomedical Engineering, Taiyuan University of Technology, Jinzhong, Shanxi, China.
College of Information and Computer, Taiyuan University of Technology, Jinzhong, China.
Acad Radiol. 2023 Jul;30(7):1281-1287. doi: 10.1016/j.acra.2022.10.015. Epub 2022 Nov 11.
Accurate identification of axillary lymph node (ALN) status in breast cancer patients is important for determining treatment options and avoiding axillary overtreatments. Our study aims to comprehensively compare the performance of the traditional radiomics model, deep learning radiomics model, and the fusion models in evaluating breast cancer ALN status based on dynamic contrast-enhanced-magnetic resonance imaging (DCE-MRI) images.
The handcrafted radiomics features and deep features were extracted from 3062 DCE-MRI images. The feature selection was performed by applying mutual information and feature recursive elimination algorithms. The traditional radiomics model and deep learning radiomics model were built using the optimal features and machine learning classifiers, respectively. The fusion models for distinguishing axillary lymph node status were constructed using two fusion strategies. The performance of the models with MRI-reported lymphadenopathy or suspicious nodes to evaluate axillary lymph node status was also compared.
The decision fusion model, with the integration of the radiomics features and deep learning features at the decision level, achieved an area under the curve (AUC) of 0.91 (95% confidence interval (CI): 0.879-0.937), which was higher than that of the traditional radiomics model and deep learning radiomics model. The results of the decision fusion model with clinical characteristic yielded an AUC of 0.93 (95% CI: 0.899-0.951), which was also superior to other models incorporating clinical characteristic.
This study demonstrates the effectiveness of the fusion models for predicting axillary lymph node metastasis in breast cancer.
准确识别乳腺癌患者腋窝淋巴结（ALN）状态对于确定治疗方案和避免过度治疗腋窝至关重要。我们的研究旨在综合比较传统放射组学模型、深度学习放射组学模型和融合模型在基于动态对比增强磁共振成像（DCE-MRI）图像评估乳腺癌 ALN 状态方面的性能。
从 3062 个 DCE-MRI 图像中提取了手工制作的放射组学特征和深度学习特征。通过应用互信息和特征递归消除算法进行特征选择。使用最优特征和机器学习分类器分别构建了传统放射组学模型和深度学习放射组学模型。使用两种融合策略构建了用于区分腋窝淋巴结状态的融合模型。还比较了使用 MRI 报告的淋巴结肿大或可疑节点评估腋窝淋巴结状态的模型的性能。
决策融合模型通过在决策层面整合放射组学特征和深度学习特征，获得了 0.91 的曲线下面积（AUC）（95%置信区间（CI）：0.879-0.937），高于传统放射组学模型和深度学习放射组学模型。包含临床特征的决策融合模型的结果得出 AUC 为 0.93（95%CI：0.899-0.951），也优于其他包含临床特征的模型。
本研究证明了融合模型在预测乳腺癌腋窝淋巴结转移方面的有效性。
