Pharmacokinetic parameters and radiomics model based on dynamic contrast enhanced MRI for the preoperative prediction of sentinel lymph node metastasis in breast cancer

Cancer Imaging

Ning Mao¹  Haizhu Xie¹  Jianjun Dong¹  Kun Zhang¹  Kaili Che¹  Heng Ma¹  Yinghong Shi¹  Xuexi Zhang²  Shaofeng Duan²  Meijie Liu³ 

[1] Department of Radiology, Yantai Yuhuangding Hospital, No. 20 Yuhuangding road, 264000, Yantai, Shandong, P. R. China;GE Healthcare, China, 200000, Shanghai, P. R. China;School of Clinical Medicine, Binzhou Medical University, 264000, Yantai, Shandong, P. R. China;Department of Radiology, Yantai Yuhuangding Hospital, No. 20 Yuhuangding road, 264000, Yantai, Shandong, P. R. China;

关键词: Breast cancer;    Sentinel lymph node;    Magnetic resonance imaging;    Radiomics;    Pharmacokinetic parameters;   

DOI  :  10.1186/s40644-020-00342-x

来源: Springer

【 摘 要 】

BackgroundTo establish pharmacokinetic parameters and a radiomics model based on dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) for predicting sentinel lymph node (SLN) metastasis in patients with breast cancer.MethodsA total of 164 breast cancer patients confirmed by pathology were prospectively enrolled from December 2017 to May 2018, and underwent DCE-MRI before surgery. Pharmacokinetic parameters and radiomics features were derived from DCE-MRI data. Least absolute shrinkage and selection operator (LASSO) regression method was used to select features, which were then utilized to construct three classification models, namely, the pharmacokinetic parameters model, the radiomics model, and the combined model. These models were built through the logistic regression method by using 10-fold cross validation strategy and were evaluated on the basis of the receiver operating characteristics (ROC) curve. An independent validation dataset was used to confirm the discriminatory power of the models.ResultsSeven radiomics features were selected by LASSO logistic regression. The radiomics model, the pharmacokinetic parameters model, and the combined model yielded area under the curve (AUC) values of 0.81 (95% confidence interval [CI]: 0.72 to 0.89), 0.77 (95% CI: 0.68 to 0.86), and 0.80 (95% CI: 0.72 to 0.89), respectively, for the training cohort and 0.74 (95% CI: 0.59 to 0.89), 0.74 (95% CI: 0.59 to 0.90), and 0.76 (95% CI: 0.61 to 0.91), respectively, for the validation cohort. The combined model showed the best performance for the preoperative evaluation of SLN metastasis in breast cancer.ConclusionsThe model incorporating radiomics features and pharmacokinetic parameters can be conveniently used for the individualized preoperative prediction of SLN metastasis in patients with breast cancer.

【 授权许可】

CC BY