Department of Radiology, Shandong Provincial Hospital, Affiliated to Shandong First Medical University, No. 324, Jingwu Road, Huaiyin District, Jinan 250012, Shandong, China.
Department of Research Collaboration, R&D Center, Beijing Deepwise & League of, PHD Technology Co. Ltd, Beijing, China.
Clin Radiol. 2024 Jan;79(1):60-66. doi: 10.1016/j.crad.2023.09.013. Epub 2023 Sep 28.
To investigate the value of multiparametric magnetic resonance imaging (MRI)-based radiomics nomograms for predicting the hormone receptor (HR) status of HER2-positive breast cancer.
Patients with HER2-positive invasive breast cancer were divided randomly into training (68 patients) and validation (30 patients) sets. All were classified as either HR-positive (HR+) or negative (HR-) at histopathology. Two radiologists outlined the three-dimensional (3D) volumetric regions of interest (VOI) on the MRI images. Features (n=1,096) were extracted from the T2-weighted imaging (WI), apparent diffusion coefficient (ADC), and dynamic contrast-enhanced (DCE) images separately. Dimensionality was reduced using feature screening. Binary radiomics prediction models were established using a logistic regression classifier and were validated in the validation set. To construct a nomogram, independent predictors were identified using multivariate logistic regression analysis. The predictive efficacy of the model was assessed using the area under the receiver operating characteristic curve (AUC).
Ten radiomics features were obtained after feature dimensionality reduction based on the merged T2WI, ADC, and DCE images. The diagnostic efficacy of the radiomics signature using the three sequences was better than that of any single sequence (training set AUC: 0.797; validation set AUC: 0.75). Using multivariate logistic regression analysis, the independent predictors for identifying HR status were combined radiomics signature and peritumoural oedema. Nomograms constructed by combining the radiomics signature and peritumoural oedema showed good discrimination in both the training and validation sets (AUC: 0.815 and 0. 805, respectively).
A multiparametric MRI-based nomogram incorporating the radiomics signature and peritumoural oedema can assess the HR status of HER2-positive breast cancer. The resulting model can improve diagnostic accuracy, improving patient outcomes.
探讨基于多参数磁共振成像（MRI）的放射组学列线图预测 HER2 阳性乳腺癌激素受体（HR）状态的价值。
将 HER2 阳性浸润性乳腺癌患者随机分为训练集（68 例）和验证集（30 例）。所有患者在组织病理学上均分为 HR 阳性（HR+）或阴性（HR-）。两位放射科医生在 MRI 图像上勾画三维（3D）容积感兴趣区（VOI）。分别从 T2 加权成像（WI）、表观扩散系数（ADC）和动态对比增强（DCE）图像中提取特征（n=1096）。使用特征筛选法降低维度。使用逻辑回归分类器建立二元放射组学预测模型，并在验证集中进行验证。使用多变量逻辑回归分析确定独立预测因子，构建列线图。使用受试者工作特征曲线下面积（AUC）评估模型的预测效能。
基于合并的 T2WI、ADC 和 DCE 图像进行特征降维后，得到 10 个放射组学特征。放射组学特征在使用三个序列时的诊断效能优于任何单个序列（训练集 AUC：0.797；验证集 AUC：0.75）。使用多变量逻辑回归分析，用于识别 HR 状态的独立预测因子为联合放射组学特征和肿瘤周围水肿。结合放射组学特征和肿瘤周围水肿构建的列线图在训练集和验证集均具有良好的区分度（AUC：分别为 0.815 和 0.805）。
一种基于多参数 MRI 的列线图，结合放射组学特征和肿瘤周围水肿，可评估 HER2 阳性乳腺癌的 HR 状态。该模型可以提高诊断准确性，改善患者预后。
