Samiei Sanaz, Granzier Renée W Y, Ibrahim Abdalla, Primakov Sergey, Lobbes Marc B I, Beets-Tan Regina G H, van Nijnatten Thiemo J A, Engelen Sanne M E, Woodruff Henry C, Smidt Marjolein L
Department of Surgery, Maastricht University Medical Center+, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands.
Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands.
Cancers (Basel). 2021 Feb 12;13(4):757. doi: 10.3390/cancers13040757.
Radiomics features may contribute to increased diagnostic performance of MRI in the prediction of axillary lymph node metastasis. The objective of the study was to predict preoperative axillary lymph node metastasis in breast cancer using clinical models and radiomics models based on T2-weighted (T2W) dedicated axillary MRI features with node-by-node analysis. From August 2012 until October 2014, all women who had undergone dedicated axillary 3.0T T2W MRI, followed by axillary surgery, were retrospectively identified, and available clinical data were collected. All axillary lymph nodes were manually delineated on the T2W MR images, and quantitative radiomics features were extracted from the delineated regions. Data were partitioned patient-wise to train 100 models using different splits for the training and validation cohorts to account for multiple lymph nodes per patient and class imbalance. Features were selected in the training cohorts using recursive feature elimination with repeated 5-fold cross-validation, followed by the development of random forest models. The performance of the models was assessed using the area under the curve (AUC). A total of 75 women (median age, 61 years; interquartile range, 51-68 years) with 511 axillary lymph nodes were included. On final pathology, 36 (7%) of the lymph nodes had metastasis. A total of 105 original radiomics features were extracted from the T2W MR images. Each cohort split resulted in a different number of lymph nodes in the training cohorts and a different set of selected features. Performance of the 100 clinical and radiomics models showed a wide range of AUC values between 0.41-0.74 and 0.48-0.89 in the training cohorts, respectively, and between 0.30-0.98 and 0.37-0.99 in the validation cohorts, respectively. With these results, it was not possible to obtain a final prediction model. Clinical characteristics and dedicated axillary MRI-based radiomics with node-by-node analysis did not contribute to the prediction of axillary lymph node metastasis in breast cancer based on data where variations in acquisition and reconstruction parameters were not addressed.
影像组学特征可能有助于提高MRI在预测腋窝淋巴结转移方面的诊断性能。本研究的目的是使用基于T2加权（T2W）专用腋窝MRI特征并进行逐个淋巴结分析的临床模型和影像组学模型，预测乳腺癌术前腋窝淋巴结转移情况。2012年8月至2014年10月期间，对所有接受过专用腋窝3.0T T2W MRI检查并随后接受腋窝手术的女性进行回顾性识别，并收集可用的临床数据。在T2W MR图像上手动勾勒出所有腋窝淋巴结，并从勾勒出的区域中提取定量影像组学特征。数据按患者进行划分，使用不同的训练集和验证集分割来训练100个模型，以考虑每位患者的多个淋巴结和类别不平衡情况。在训练集中使用带有重复5折交叉验证的递归特征消除方法选择特征，随后建立随机森林模型。使用曲线下面积（AUC）评估模型的性能。共纳入75名女性（中位年龄61岁；四分位间距51 - 68岁），其腋窝淋巴结共有511个。最终病理检查显示，36个（7%）淋巴结有转移。从T2W MR图像中总共提取了105个原始影像组学特征。每个队列分割导致训练集中的淋巴结数量不同，以及所选特征集不同。100个临床模型和影像组学模型在训练集中的AUC值范围分别为0.41 - 0.74和0.48 - 0.89，在验证集中分别为0.30 - 0.98和0.37 - 0.99。基于这些结果，无法获得最终的预测模型。在未解决采集和重建参数变化的数据基础上，临床特征以及基于专用腋窝MRI并进行逐个淋巴结分析的影像组学，对乳腺癌腋窝淋巴结转移的预测并无帮助。
