本研究旨在开发基于传统磁共振成像 (cMRI) 和放射组学特征的预测模型，用于预测乳腺癌 (BC) 的人表皮生长因子受体 2 (HER2) 状态，并比较其性能。共有 287 名侵袭性 BC 患者对我院进行回顾性分析。所有患者均接受术前乳腺 MRI 检查，包括脂肪抑制 T2 加权成像、轴向动态对比增强 MRI 和扩散加权成像序列。从这些序列中得出放射组学特征。利用 cMRI 特征、放射组学特征以及合并两者的综合模型建立了三个不同的模型。使用受试者工作特征曲线分析评估这些模型的预测能力。然后通过 DeLong 测试和净重分类改进 (NRI) 确定比较表现。在随机分组中，287 名 BC 患者被分配到任一训练（234 名；46 名 HER2-0 患者、107 名 HER2-low 患者、81 名 HER2-positive 患者） ) 或以 8:2 的比例进行测试（53 个；8 个 HER2-零，27 个 HER2-低，18 个 HER2-阳性）。 cMRI、放射组学和预测 HER2 状态的综合模型的平均曲线下面积 (AUC) 在训练集中分别为 0.705、0.819 和 0.859，在测试集中分别为 0.639、0.797 和 0.842。 DeLong 的测试表明，组合模型的 AUC 显着超过放射组学模型 (p < 0.05)。 NRI 分析验证了测试集中组合模型在 BC HER2 预测 (NRI 25.0) 方面优于放射组学的优越性。基于 cMRI 和放射组学特征相结合的综合模型在无创预测三级 HER2 状态方面优于单一放射组学模型BC 患者。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。

This study aimed to develop predictive models based on conventional magnetic resonance imaging (cMRI) and radiomics features for predicting human epidermal growth factor receptor 2 (HER2) status of breast cancer (BC) and compare their performance.A total of 287 patients with invasive BC in our hospital were retrospectively analyzed. All patients underwent preoperative breast MRI consisting of fat-suppressed T2-weighted imaging, axial dynamic contrast-enhanced MRI, and diffusion-weighted imaging sequences. From these sequences, radiomics features were derived. Three distinct models were established utilizing cMRI features, radiomics features, and a comprehensive model that amalgamated both. The predictive capabilities of these models were assessed using the receiver operating characteristic curve analysis. The comparative performance was then determined through the DeLong test and net reclassification improvement (NRI).In a randomized split, the 287 patients with BC were allotted to either training (234; 46 HER2-zero, 107 HER2-low, 81 HER2-positive) or test (53; 8 HER2-zero, 27 HER2-low, 18 HER2-positive) at an 8:2 ratio. The mean area under the curve (AUCs) for cMRI, radiomics, and comprehensive models predicting HER2 status were 0.705, 0.819, and 0.859 in training set and 0.639, 0.797, and 0.842 in test set, respectively. DeLong's test indicated that the combined model's AUC surpassed the radiomics model significantly (p < 0.05). NRI analysis verified superiority of the combined model over the radiomics for BC HER2 prediction (NRI 25.0) in the test set.The comprehensive model based on the combination of cMRI and radiomics features outperformed the single radiomics model in noninvasively predicting the three-tiered HER2 status in patients with BC.Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.