Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
Eur Radiol. 2022 Jan;32(1):639-649. doi: 10.1007/s00330-021-08134-y. Epub 2021 Jun 29.
To conduct perilesional region radiomics analysis of contrast-enhanced mammography (CEM) images to differentiate benign and malignant breast lesions.
This retrospective study included patients who underwent CEM from November 2017 to February 2020. Lesion contours were manually delineated. Perilesional regions were automatically obtained. Seven regions of interest (ROIs) were obtained for each lesion, including the lesion ROI, annular perilesional ROIs (1 mm, 3 mm, 5 mm), and lesion + perilesional ROIs (1 mm, 3 mm, 5 mm). Overall, 4,098 radiomics features were extracted from each ROI. Datasets were divided into training and testing sets (1:1). Seven classification models using features from the seven ROIs were constructed using LASSO regression. Model performance was assessed by the AUC with 95% CI.
Overall, 190 women with 223 breast lesions (101 benign; 122 malignant) were enrolled. In the testing set, the annular perilesional ROI of 3-mm model showed the highest AUC of 0.930 (95% CI: 0.882-0.977), followed by the annular perilesional ROI of 1 mm model (AUC = 0.929; 95% CI: 0.881-0.978) and the lesion ROI model (AUC = 0.909; 95% CI: 0.857-0.961). A new model was generated by combining the predicted probabilities of the lesion ROI and annular perilesional ROI of 3-mm models, which achieved a higher AUC in the testing set (AUC = 0.940).
Annular perilesional radiomics analysis of CEM images is useful for diagnosing breast cancers. Adding annular perilesional information to the radiomics model built on the lesion information may improve the diagnostic performance.
• Radiomics analysis of the annular perilesional region of 3 mm in CEM images may provide valuable information for the differential diagnosis of benign and malignant breast lesions. • The radiomics information from the lesion region and the annular perilesional region may be complementary. Combining the predicted probabilities of the models constructed by the features from the two regions may improve the diagnostic performance of radiomics models.
对对比增强乳腺摄影（CEM）图像的瘤周区域进行放射组学分析，以区分良性和恶性乳腺病变。
本回顾性研究纳入了 2017 年 11 月至 2020 年 2 月期间接受 CEM 的患者。手动勾画病变轮廓。自动获取瘤周区域。每个病变获得 7 个感兴趣区（ROI），包括病变 ROI、环形瘤周 ROI（1mm、3mm、5mm）和病变+瘤周 ROI（1mm、3mm、5mm）。总体而言，每个 ROI 提取了 4098 个放射组学特征。数据集分为训练集和测试集（1:1）。使用来自 7 个 ROI 的特征构建了 7 个 LASSO 回归分类模型。通过 95%置信区间的 AUC 评估模型性能。
共纳入 190 名女性 223 个乳腺病变（101 个良性；122 个恶性）。在测试集中，3mm 环形瘤周 ROI 模型的 AUC 最高，为 0.930（95%CI：0.882-0.977），其次是 1mm 环形瘤周 ROI 模型（AUC=0.929；95%CI：0.881-0.978）和病变 ROI 模型（AUC=0.909；95%CI：0.857-0.961）。通过结合病变 ROI 和 3mm 环形瘤周 ROI 模型的预测概率生成新模型，在测试集中获得了更高的 AUC（AUC=0.940）。
CEM 图像的环形瘤周放射组学分析有助于诊断乳腺癌。将环形瘤周信息添加到基于病变信息构建的放射组学模型中可能会提高诊断性能。
• CEM 图像中 3mm 环形瘤周的放射组学分析可能为良性和恶性乳腺病变的鉴别诊断提供有价值的信息。• 病变区域和环形瘤周区域的放射组学信息可能是互补的。结合由两个区域的特征构建的模型的预测概率可能会提高放射组学模型的诊断性能。
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