Radiomics Analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging for the Prediction of Sentinel Lymph Node Metastasis in Breast Cancer

Jia Liu; Dong Sun; Linli Chen; Zheng Fang; Weixiang Song; Dajing Guo; Tiangen Ni; Chuan Liu; Lin Feng; Yuwei Xia; Xiong Zhang; Chuanming Li

doi:10.3389/fonc.2019.00980

Radiomics Analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging for the Prediction of Sentinel Lymph Node Metastasis in Breast Cancer

Front Oncol. 2019 Sep 30:9:980. doi: 10.3389/fonc.2019.00980. eCollection 2019.

Authors

Jia Liu¹, Dong Sun¹, Linli Chen¹, Zheng Fang¹, Weixiang Song¹, Dajing Guo¹, Tiangen Ni², Chuan Liu³, Lin Feng³, Yuwei Xia⁴, Xiong Zhang⁴, Chuanming Li¹

Affiliations

¹ Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
² Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
³ Department of Radiology, Affiliated Hospital of Chuanbei Medical College, Nanchong, China.
⁴ Huiying Medical Technology, Beijing, China.

Abstract

Purpose: To investigate whether a combination of radiomics and automatic machine learning applied to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of primary breast cancer can non-invasively predict axillary sentinel lymph node (SLN) metastasis. Methods: 62 patients who received a DCE-MRI breast scan were enrolled. Tumor resection and sentinel lymph node (SLN) biopsy were performed within 1 week after the DCE-MRI examination. According to the time signal intensity curve, the volumes of interest (VOIs) were delineated on the whole tumor in the images with the strongest enhanced phase. Datasets were randomly divided into two sets including a training set (~80%) and a validation set (~20%). A total of 1,409 quantitative imaging features were extracted from each VOI. The select K best and least absolute shrinkage and selection operator (Lasso) were used to obtain the optimal features. Three classification models based on the logistic regression (LR), XGboost, and support vector machine (SVM) classifiers were constructed. Receiver Operating Curve (ROC) analysis was used to analyze the prediction performance of the models. Both feature selection and models construction were firstly performed in the training set, then were further tested in the validation set by the same thresholds. Results: There is no significant difference between all clinical and pathological variables in breast cancer patients with and without SLN metastasis (P > 0.05), except histological grade (P = 0.03). Six features were obtained as optimal features for models construction. In the validation set, with respect to the accuracy and MSE, the SVM demonstrated the highest performance, with an accuracy, AUC, sensitivity (for positive SLN), specificity (for positive SLN) and Mean Squared Error (MSE) of 0.85, 0.83, 0.71, 1, 0.26, respectively. Conclusions: We demonstrated the feasibility of combining artificial intelligence and radiomics from DCE-MRI of primary tumors to predict axillary SLN metastasis in breast cancer. This non-invasive approach could be very promising in application.

Keywords: DCE-MRI; automatic machine learning; breast cancer; radiomics; sentinel lymph node metastasis.