乳腺癌患者腋窝淋巴结转移（ALNM）的检测是腋窝手术和潜在治疗决策过程中的关键决定因素。本研究的目的是开发并验证放射组学列线图，将动态对比增强磁共振成像 (DCE-MRI) 的放射组学特征与临床因素相结合，以预测乳腺癌患者的 ALNM。 共有 177 名乳腺癌患者按 7:3 的比例随机分为训练集 (n = 123) 和验证集 (n = 54)。从 DCE-MRI 图像中，从原发肿瘤和腋窝淋巴结 (ALN) 中提取了 2818 个放射组学特征。随后，通过最小绝对收缩和选择算子算法选择最佳特征来构建 Radscore。使用单变量逻辑回归分析确定临床因素，并将其纳入多变量逻辑回归分析中。利用 Radscore 和临床因素，使用支持向量机方法开发了放射组学列线图。我们的模型的预测效果通过受试者操作特征（ROC）曲线进行直观评估，而其临床应用和预测准确性分别通过决策曲线分析（DCA）和校准曲线进行评估。结果显示Ki67、多焦性和MRI -将 ALN 状态报告为 ALNM 的独立危险因素。放射组学列线图显示出良好的校准和辨别力，训练集中的 ROC 曲线下面积为 0.92（95% 置信区间 [CI]，0.88-0.97），验证集中的 ROC 曲线下面积为 0.90（95% CI，0.72-0.90）。 DCA 揭示了放射组学列线图的临床实用性。基于 DCE-MRI 的放射组学列线图是评估乳腺癌患者 ALNM 的可靠工具。版权所有 © 2023 大学放射科医生协会。由爱思唯尔公司出版。保留所有权利。

The detection of axillary lymph node metastasis (ALNM) in patients with breast cancer is a crucial determinant in the decision-making process for axillary surgery and potential therapies. The objective of this study was to develop and validate a radiomics nomogram that integrates radiomics features from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) with clinical factors to predict ALNM in patients with breast cancer.A total of 177 patients with breast cancer were randomly divided into a training set (n = 123) and a validation set (n = 54) using a 7:3 ratio. From the DCE-MRI images, 2818 radiomics features were extracted from the primary tumor and axillary lymph node (ALN). Subsequently, optimal features were selected through the least absolute shrinkage and selection operator algorithm to construct the Radscore. Clinical factors were identified using univariate logistic regression analysis and included in a multivariate logistic regression analysis. Using the Radscore and clinical factors, a radiomics nomogram was developed using the Support Vector Machine method. The predicting efficacy of our model was visually appraised utilizing a receiver operator characteristic (ROC) curve, while its clinical application and predictive accuracy were assessed through decision curve analysis (DCA) and calibration curves, respectively.The results revealed Ki67, multifocality, and MRI-reported ALN status as independent risk factors for ALNM. The radiomics nomogram demonstrated good calibration and discrimination with areas under the ROC curve of 0.92 (95% confidence interval [CI], 0.88-0.97) in the training set and 0.90 (95% CI, 0.72-0.90) in the validation set. DCA revealed the clinical usefulness of the radiomics nomogram.The DCE-MRI-based radiomics nomogram is a reliable tool for assessing ALNM in patients with breast cancer.Copyright © 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.