深度学习可以增强多模态图像分析在预测腋窝淋巴结（ALN）转移方面的性能，该分析以其无创属性和互补功效而闻名。因此，我们建立了结合超声（US）和磁共振成像（MRI）图像的多模态深度学习模型来预测乳腺癌患者的ALN转移。主要队列由来自两家医院的组织学确诊的乳腺癌患者组成的回顾性队列（n = 465）和外部验证队列（n = 123）。所有患者均接受了术前超声和核磁共振扫描。数据预处理后，三个卷积神经网络模型分别用于分析US和MRI图像。整合US和MRI深度学习预测结果（分别为DLUS和DLMRI）后，构建了多模态深度学习（DLMRI US临床参数）模型。将所提出模型的预测能力与 DLUS、DLMRI、组合双模 (DLMRI US) 和临床参数模型进行比较。使用受试者工作特征曲线（AUC）和决策曲线下面积进行评估。共有 588 名乳腺癌患者参与了这项研究。 DLMRI US 临床参数模型优于替代模型，在内部和外部验证集上分别实现了最高 AUC 0.819（95% 置信区间 [CI] 0.734-0.903）和 0.809（95% CI 0.723-0.895）。决策曲线分析证实了其临床实用性。DLMRI US+临床参数模型证明了其对乳腺癌患者 ALN 转移预测的可行性和可靠性。版权所有 © 2024 大学放射科医生协会。由爱思唯尔公司出版。保留所有权利。

Deep learning can enhance the performance of multimodal image analysis, which is known for its noninvasive attributes and complementary efficacy, in predicting axillary lymph node (ALN) metastasis. Therefore, we established a multimodal deep learning model incorporating ultrasound (US) and magnetic resonance imaging (MRI) images to predict ALN metastasis in patients with breast cancer.A retrospective cohort of patients with histologically confirmed breast cancer from two hospitals composed of the primary cohort (n = 465) and the external validation cohort (n = 123). All patients had undergone both preoperative US and MRI scans. After data preprocessing, three convolutional neural network models were used to analyze the US and MRI images, respectively. After integrating the US and MRI deep learning prediction results (DLUS and DLMRI, respectively), a multimodal deep learning (DLMRI+US+Clinical parameter) model was constructed. The predictive ability of the proposed model was compared to that of the DLUS, DLMRI, combined bimodal (DLMRI+US), and clinical parameter models. Evaluation was performed using the area under the receiver operating characteristic curves (AUCs) and decision curves.A total of 588 patients with breast cancer participated in this study. The DLMRI+US+Clinical parameter model outperformed the alternative models, achieving the highest AUCs of 0.819 (95% confidence interval [CI] 0.734-0.903) and 0.809 (95% CI 0.723-0.895) on the internal and external validation sets, respectively. The decision curve analysis confirmed its clinical usefulness.The DLMRI+US＋Clinical parameter model demonstrates the feasibility and reliability of its performance for ALN metastasis prediction in patients with breast cancer.Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.