DCE-MRI-based machine learning model for predicting axillary lymph node metastasis in breast cancer.

Abstract

Background: Accurate evaluation of the axillary lymph node (ALN) status is needed for determining the treatment protocol for breast cancer. This study aimed to build an artificial intelligence (AI) model to predict ALN metastasis based on pre-treatment dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) of breast cancer combined with radiomics algorithms.

Methods: Pre-treatment DCE-MRI dataset of 166 patients with pathologically confirmed breast cancer diagnosis from January 2017 to August 2020 was collected, and all patients were randomly divided into a training group and test group with a ratio of 7:3. Each patient underwent pre-enhancement as well as post-enhancement 1-6 MRI, and a total of 7,224 two-dimensional (2D) and 9,863 three-dimensional (3D) features were extracted, respectively. Radiomics models based on 2D, 3D, pre-enhancement, and the first post-enhancement images were established using the least absolute shrinkage selection operator (LASSO) algorithm based on machine learning, and the area under the curve (AUC), accuracy, sensitivity, and specificity of the models were calculated.

Results: The mean AUC, accuracy, sensitivity, and specificity of the 10-fold cross-validation of the 3D radiomics-based model were 82%, 82%, 83%, and 81%, respectively. The C-index of the combined model with combining radiomics features and clinical features was 90%, the AUC was 90%, the specificity was 91%, the sensitivity was 77% and the accuracy was 84%.

Conclusions: The comprehensive prediction model using DCE-MRI image combined with clinical features can accurately predict ALN metastasis in breast cancer.