Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI

Objectives To predict sentinel lymph node (SLN) metastasis in breast cancer patients using radiomics based on T 2 -weighted fat suppression (T 2 -FS) and diffusion-weighted MRI (DWI). Methods We enrolled 146 patients with histologically proven breast cancer. All underwent pretreatment T 2 -FS and DWI MRI scan. In all, 10,962 texture and four non-texture features were extracted for each patient. The 0.623 + bootstrap method and the area under the curve (AUC) were used to select the features. We constructed ten logistic regression models (orders of 1–10) based on different combination of image features using stepwise forward method. Results For T 2 -FS, model 10 with ten features yielded the highest AUC of 0.847 in the training set and 0.770 in the validation set. For DWI, model 8 with eight features reached the highest AUC of 0.847 in the training set and 0.787 in the validation set. For joint T 2 -FS and DWI, model 10 with ten features yielded an AUC of 0.863 in the training set and 0.805 in the validation set. Conclusions Full utilisation of breast cancer-specific textural features extracted from anatomical and functional MRI images improves the performance of radiomics in predicting SLN metastasis, providing a non-invasive approach in clinical practice. Key Points • SLN biopsy to access breast cancer metastasis has multiple complications. • Radiomics uses features extracted from medical images to characterise intratumour heterogeneity. • We combined T 2 -FS and DWI textural features to predict SLN metastasis non-invasively.