Preoperative multiparametric magnetic resonance imaging and contrast-enhanced mammography radiomics based nonsentinel lymph node metastasis prediction in breast cancer

Abstract

Models based on magnetic resonance imaging (MRI) and contrast-enhanced mammography (CEM) radiomics for noninvasive prediction of nonsentinel lymph node (NSLN) metastasis were developed in this study. A total of 270 patients who had undergone axillary lymph node dissection from January 2018 to October 2022 and were diagnosed with 1–2 positive SLNs were included. Radiomic features were extracted from breast cancer voxels in CEM, T1 weighted MRI, T2 weighted MRI, and Diffision Weighted Imaging (DWI) images and kinetic parameter maps of dynamic contrast-enhanced-MRI. Correlation analysis, the least absolute shrinkage and selection operator (LASSO), and analysis of variance (ANOVA) were utilized for feature selection. The expectation maximum based matrix completion technique filled in the missing features in a specific modality depending on the features of another available modality. Finally, MRI-CEM-based prediction models were developed utilizing a support vector machine. The receiver operating characteristic curve was used to assess the models' performance. The mean AUCs from three-fold cross-validation for models based on clinical risk factors, MRI-CEM radiomic features, and their combination were 0.70, 0.70, and 0.76, respectively. Combining MRI and CEM radiomic features with clinical risk factors improved the predictive ability for NSLN metastasis compared to individual factors alone.

Keywords

breast cancer / dynamic contrast-enhanced magnetic resonance imaging / matrix completion / non-sentinel lymph node metastasis / radiomics

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