Zhang Hao, Miao Qing, Fu Yan, Pan Ruike, Jin Qing, Gu Changjiang, Ni Xuejun
From the Department of Medical Ultrasound, Affiliated Hospital of Nantong University, Nantong, China.
From the Department of Ultrasound, Jiangsu Cancer Hospital, Nanjing, China.
Front Oncol. 2025 Apr 16;15:1556317. doi: 10.3389/fonc.2025.1556317. eCollection 2025.
To develop an intratumoral and peritumoral radiomics model using Automated Breast Volume Scanner (ABVS) for noninvasive preoperative prediction of Human Epidermal Growth Factor Receptor 2 (HER2) status.
This retrospective study analyzed 384 lesions from 379 patients with pathologically confirmed breast cancer across four hospitals. Two tasks were defined: Task 1 to distinguish HER2-negative from HER2-positive cases and Task 2 to differentiate HER2-zero from HER2-low status. For each classification task, three models were built: Model 1 included radiomics features from the tumor region alone; Model 2 included features from both the tumor region and a 5mm peritumoral region; and Model 3 incorporated features from the tumor region, the 5mm peritumoral region, and the 5-10mm peritumoral region. The performance of the model was evaluated using receiver operating characteristic (ROC) curves, with key metrics including the area under the curve (AUC), sensitivity, specificity, and accuracy.
In the classification tasks, Model 2 demonstrated superior predictive performance across multiple datasets. For Task 1, it achieved the highest AUC (0.844), exceptional sensitivity (0.955), and satisfactory accuracy (0.787) in the validation set, and outperformed other models in the test set with an AUC of 0.749 and sensitivity of 0.885, highlighting its robustness and clinical applicability. For Task 2, Model 2 exhibited the highest AUC (0.801), sensitivity (0.862), and accuracy (0.808) in the test set, with consistent performance across the training (AUC 0.850) and validation sets (AUC 0.801). Model 3, which combines intratumoral and peritumoral features, did not demonstrate significant improvements over the intratumoral-only model in the two classification tasks. These results underscore the value of incorporating peritumoral radiomics features, particularly within a 5mm margin, to enhance predictive performance compared to intratumoral-only models.
The radiomics model integrating intratumoral and appropriate peritumoral features significantly outperformed the model based on intratumoral features alone. This integrated approach holds strong potential for noninvasive, preoperative prediction of HER2 status.
利用自动乳腺容积扫描仪（ABVS）建立瘤内和瘤周放射组学模型，用于术前无创预测人表皮生长因子受体2（HER2）状态。
这项回顾性研究分析了来自四家医院的379例病理确诊乳腺癌患者的384个病灶。定义了两项任务：任务1为区分HER2阴性与HER2阳性病例，任务2为区分HER2零表达与HER2低表达状态。对于每项分类任务，构建了三个模型：模型1仅包括肿瘤区域的放射组学特征；模型2包括肿瘤区域和5mm瘤周区域的特征；模型3纳入了肿瘤区域、5mm瘤周区域和5 - 10mm瘤周区域的特征。使用受试者操作特征（ROC）曲线评估模型的性能，关键指标包括曲线下面积（AUC）、敏感性、特异性和准确性。
在分类任务中，模型2在多个数据集中表现出卓越的预测性能。对于任务1，它在验证集中实现了最高的AUC（0.844）、出色的敏感性（0.955）和令人满意的准确性（0.787），并且在测试集中以0.749的AUC和0.885的敏感性优于其他模型，突出了其稳健性和临床适用性。对于任务2，模型2在测试集中表现出最高的AUC（0.801）、敏感性（0.862）和准确性（0.808），在训练集（AUC 0.850）和验证集（AUC 0.801）中表现一致。在两项分类任务中，结合瘤内和瘤周特征的模型3相较于仅基于瘤内特征的模型未显示出显著改善。这些结果强调了纳入瘤周放射组学特征的价值，特别是在5mm边缘范围内，与仅基于瘤内特征的模型相比可提高预测性能。
整合瘤内和适当瘤周特征的放射组学模型明显优于仅基于瘤内特征的模型。这种综合方法在术前无创预测HER2状态方面具有强大潜力。
