探讨基于全肿瘤和亚区域的对比增强乳腺X线摄影（CEM）放射组学在区分乳腺癌HER2表达状态中的价值。连续入组来自两个中心的352例术前接受CEM的患者，分为训练组、内部验证组和外部验证队列。将病变分为HER2阳性组和HER2阴性组。除了放射学特征外，还从 CEM 重组图像的头尾视图中提取了捕获基于整个肿瘤 (wITH) 和基于子区域的瘤内异质性 (sITH) 的放射组学特征。 XGBoost 分类器用于开发放射学、sITH 和 wITH 模型。融合三个模型的预测结果构建组合模型。患者的平均年龄为51.1±10.7岁。选择两个放射学特征、四个wITH特征和三个sITH特征来建立模型。组合模型在训练中将 AUC 显着提高至 0.80±0.03 (95% CI: 0.73-0.86)、0.79±0.06 (95% CI: 0.67-0.90) 和 0.79±0.05 (95% CI: 0.69-0.89)分别为，内部验证和外部验证队列（所有 P < 0.05）。组合模型显示预测概率和观察概率之间具有良好的一致性，并且在验证队列中具有良好的净临床效益。CEM 的基于整个肿瘤和亚区域的 ITH 放射组学特征均表现出区分 HER2 表达状态的潜力。结合传统的放射学特征和 ITH 特征可以提高模型的性能。© 2024。作者，获得 Springer Nature Limited 的独家许可。

To explore the value of whole tumour- and subregion-based radiomics of contrast-enhanced mammography (CEM) in differentiating the HER2 expression status of breast cancers.352 patients underwent preoperative CEM from two centres were consecutively enroled and divided into the training, internal validation, and external validation cohorts. The lesions were divided into HER2-positive and HER2-negative groups. Besides the radiological features, radiomics features capturing the whole tumour-based (wITH) and subregion-based intratumoral heterogeneity (sITH) were extracted from the craniocaudal view of CEM recombined images. The XGBoost classifier was applied to develop the radiological, sITH, and wITH models. A combined model was constructed by fusing the prediction results of the three models.The mean age of the patients was 51.1 ± 10.7 years. Two radiological features, four wITH features, and three sITH features were selected to establish the models. The combined model significantly improved the AUC to 0.80 ± 0.03 (95% CI: 0.73-0.86), 0.79 ± 0.06 (95% CI: 0.67-0.90), and 0.79 ± 0.05 (95% CI: 0.69-0.89) in the training, internal validation, and external validation cohorts, respectively (All P < 0.05). The combined model showed good agreement between the predicted and observed probabilities and favourable net clinical benefit in the validation cohorts.Both whole tumour- and subregion-based ITH radiomics features of CEM exhibited potential for differentiating the HER2 expression status. Combining conventional radiological features and ITH features can improve the model's performance.© 2024. The Author(s), under exclusive licence to Springer Nature Limited.