本研究旨在建立一种基于MRI的深度学习放射组学（DLR）标记，以预测人类表皮生长因子受体2（HER2）低阳性状态，并通过DLR模型进一步验证预后差异。共纳入两个机构共481例乳腺癌术前进行MRI的患者。分别从分割肿瘤中提取传统放射组学特征和基于深度语义分割特征的放射组学（DSFR）特征来构建模型。然后，通过对两个模型的输出概率进行平均，构建DLR模型来评估HER2状态。最后，进行Kaplan-Meier生存分析以探索HER2低阳性状态患者的无病生存期（DFS）。通过构建多元Cox比例风险模型进一步确定与DFS相关的因素。首先，DLR模型在训练集和验证集中分别以0.868和0.763的AUC值区分HER2阴性和HER2过度表达患者。此外，DLR模型以0.855和0.750的AUC值区分HER2低阳性患者和HER2阴性患者。Cox回归分析显示，使用DLR模型的预测得分（HR，0.175；p = 0.024）和病变大小（HR，1.043；p = 0.009）是DFS的显著、独立预测因子。我们成功地基于MRI构建了一个DLR模型，用于非侵入性评估HER2状态，并进一步揭示了预测HER2低阳性患者DFS的前景。MRI基于DLR模型可以非侵入性地鉴别HER2低阳性状态，被认为是一种新的预后预测因子和治疗靶点。• DLR模型有效区分乳腺癌患者的HER2状态，尤其是HER2低阳性状态。• DLR模型在区分HER2表达方面优于传统放射组学模型或DSFR模型。• 使用该模型的预测得分和病变大小是DFS的显著独立预测因子。© 2023年。作者和欧洲放射学会独家授权。

This study aimed to establish a MRI-based deep learning radiomics (DLR) signature to predict the human epidermal growth factor receptor 2 (HER2)-low-positive status and further verified the difference in prognosis by the DLR model.A total of 481 patients with breast cancer who underwent preoperative MRI were retrospectively recruited from two institutions. Traditional radiomics features and deep semantic segmentation feature-based radiomics (DSFR) features were extracted from segmented tumors to construct models separately. Then, the DLR model was constructed to assess the HER2 status by averaging the output probabilities of the two models. Finally, a Kaplan‒Meier survival analysis was conducted to explore the disease-free survival (DFS) in patients with HER2-low-positive status. The multivariate Cox proportional hazard model was constructed to further determine the factors associated with DFS.First, the DLR model distinguished between HER2-negative and HER2-overexpressing patients with AUCs of 0.868 and 0.763 in the training and validation cohorts, respectively. Furthermore, the DLR model distinguished between HER2-low-positive and HER2-zero patients with AUCs of 0.855 and 0.750, respectively. Cox regression analysis showed that the prediction score obtained using the DLR model (HR, 0.175; p = 0.024) and lesion size (HR, 1.043; p = 0.009) were significant, independent predictors of DFS.We successfully constructed a DLR model based on MRI to noninvasively evaluate the HER2 status and further revealed prospects for predicting the DFS of patients with HER2-low-positive status.The MRI-based DLR model could noninvasively identify HER2-low-positive status, which is considered a novel prognostic predictor and therapeutic target.• The DLR model effectively distinguished the HER2 status of breast cancer patients, especially the HER2-low-positive status. • The DLR model was better than the traditional radiomics model or DSFR model in distinguishing HER2 expression. • The prediction score obtained using the model and lesion size were significant independent predictors of DFS.© 2023. The Author(s), under exclusive licence to European Society of Radiology.