本研究调查了乳腺磁共振成像 (MRI) 放射组学对预测腋窝淋巴结转移 (ALNM) 的临床价值，并比较了不同 MRI 序列组合的判别能力。 本研究纳入了来自两个中心的 141 名诊断为浸润性乳腺癌的患者（中心1：n = 101，中心2：n = 40）。来自中心 1 的患者被随机分为训练集和测试集 1。来自中心 2 的患者被分配到测试集 2。所有参与者均接受了术前 MRI，并获得了四个不同的 MRI 序列。在动态对比增强（DCE）后对比2期序列上描绘乳腺肿瘤的感兴趣体积（VOI），并在需要时调整其他序列的VOI。随后，使用开源包从 VOI 中提取放射组学特征。单序列和多序列放射组学模型都是在训练集中使用逻辑回归方法构建的。计算测试集 1 和测试集 2 的放射组学模型的受试者工作特征曲线下面积 (AUC)、准确性、灵敏度、特异性和精密度。最后，将每个模型的诊断性能与初级和高级放射科医生的诊断水平进行比较。来自DCE对比后第1阶段的单序列ALNM分类器在测试集1（AUC = 0.891）和测试集2中均具有最佳性能（AUC = 0.619）。测试集 1 (AUC = 0.910) 和测试集 2 (AUC = 0.717) 的性能最佳的多序列 ALNM 分类器是从 DCE 对比后第 1 期、T2 加权成像和扩散加权成像单序列 ALNM 分类器生成的。两者的诊断水平均高于初级和高级放射科医生。 DCE 对比后 1 期、T2 加权成像和扩散加权成像放射组学特征的组合在预测乳腺癌 ALNM 方面具有最佳性能。我们的研究为乳腺癌患者的 ALNM 预测提供了一种性能良好的非侵入性工具。© 2024。作者。

This study investigated the clinical value of breast magnetic resonance imaging (MRI) radiomics for predicting axillary lymph node metastasis (ALNM) and to compare the discriminative abilities of different combinations of MRI sequences.This study included 141 patients diagnosed with invasive breast cancer from two centers (center 1: n = 101, center 2: n = 40). Patients from center 1 were randomly divided into training set and test set 1. Patients from center 2 were assigned to the test set 2. All participants underwent preoperative MRI, and four distinct MRI sequences were obtained. The volume of interest (VOI) of the breast tumor was delineated on the dynamic contrast-enhanced (DCE) postcontrast phase 2 sequence, and the VOIs of other sequences were adjusted when required. Subsequently, radiomics features were extracted from the VOIs using an open-source package. Both single- and multisequence radiomics models were constructed using the logistic regression method in the training set. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and precision of the radiomics model for the test set 1 and test set 2 were calculated. Finally, the diagnostic performance of each model was compared with the diagnostic level of junior and senior radiologists.The single-sequence ALNM classifier derived from DCE postcontrast phase 1 had the best performance for both test set 1 (AUC = 0.891) and test set 2 (AUC = 0.619). The best-performing multisequence ALNM classifiers for both test set 1 (AUC = 0.910) and test set 2 (AUC = 0.717) were generated from DCE postcontrast phase 1, T2-weighted imaging, and diffusion-weighted imaging single-sequence ALNM classifiers. Both had a higher diagnostic level than the junior and senior radiologists.The combination of DCE postcontrast phase 1, T2-weighted imaging, and diffusion-weighted imaging radiomics features had the best performance in predicting ALNM from breast cancer. Our study presents a well-performing and noninvasive tool for ALNM prediction in patients with breast cancer.© 2024. The Author(s).