Department of Radiology, Aerospace Center Hospital, Beijing, China.
Department of Ultrasound, Aerospace Center Hospital, Beijing, China.
Clin Exp Med. 2024 May 23;24(1):110. doi: 10.1007/s10238-024-01377-1.
We aimed to construct and validate a multimodality MRI combined with ultrasound based on radiomics for the evaluation of benign and malignant breast diseases. The preoperative enhanced MRI and ultrasound images of 131 patients with breast diseases confirmed by pathology in Aerospace Center Hospital from January 2021 to August 2023 were retrospectively analyzed, including 73 benign diseases and 58 malignant diseases. Ultrasound and 3.0 T multiparameter MRI scans were performed in all patients. Then, all the data were divided into training set and validation set in a 7:3 ratio. Regions of interest were drawn layer by layer based on ultrasound and MR enhanced sequences to extract radiomics features. The optimal radiomic features were selected by the best feature screening method. Logistic Regression classifier was used to establish models according to the best features, including ultrasound model, MRI model, ultrasound combined with MRI model. The model efficacy was evaluated by the area under the curve (AUC) of the receiver operating characteristic, sensitivity, specificity, and accuracy. The F-test based on ANOVA was used to screen out 20 best ultrasonic features, 11 best MR Features, and 14 best features from the combined model. Among them, texture features accounted for the largest proportion, accounting for 79%.The ultrasound combined with MR Image fusion model based on logistic regression classifier had the best diagnostic performance. The AUC of the training group and the validation group were 0.92 and 091, the sensitivity was 0.80 and 0.67, the specificity was 0.90 and 0.94, and the accuracy was 0.84 and 0.79, respectively. It was better than the simple ultrasound model (AUC of validation set was 0.82) or the simple MR model (AUC of validation set was 0.85). Compared with the traditional ultrasound or magnetic resonance diagnosis of breast diseases, the multimodal model of MRI combined with ultrasound based on radiomics can more accurately predict the benign and malignant breast diseases, thus providing a better basis for clinical diagnosis and treatment.
我们旨在构建和验证一种基于放射组学的多模态 MRI 联合超声，用于评估良恶性乳腺疾病。回顾性分析了 2021 年 1 月至 2023 年 8 月在航天中心医院经病理证实的 131 例乳腺疾病患者的术前增强 MRI 和超声图像，包括 73 例良性疾病和 58 例恶性疾病。所有患者均行超声及 3.0T 多参数 MRI 扫描。然后，将所有数据按照 7：3 的比例分为训练集和验证集。基于超声和 MR 增强序列逐层绘制感兴趣区，提取放射组学特征。使用最佳特征筛选方法选择最佳放射组学特征。根据最佳特征，使用逻辑回归分类器建立超声模型、MRI 模型、超声联合 MRI 模型。通过受试者工作特征曲线（AUC）的曲线下面积（AUC）、敏感性、特异性和准确性评估模型效能。基于方差分析的 F 检验筛选出联合模型中 20 个最佳超声特征、11 个最佳 MRI 特征和 14 个最佳特征。其中，纹理特征占比最大，占 79%。基于逻辑回归分类器的超声联合 MR 图像融合模型具有最佳的诊断性能。训练组和验证组的 AUC 分别为 0.92 和 0.91，灵敏度分别为 0.80 和 0.67，特异性分别为 0.90 和 0.94，准确性分别为 0.84 和 0.79。优于单纯超声模型（验证集 AUC 为 0.82）或单纯 MR 模型（验证集 AUC 为 0.85）。与传统的超声或磁共振诊断乳腺疾病相比，基于放射组学的 MRI 联合超声多模态模型能更准确地预测良恶性乳腺疾病，从而为临床诊断和治疗提供更好的依据。
