Prediction of axillary lymph node metastasis in breast cancer based on MRI: A novel domain adaptative radiomics pipeline for multicenter studies

Abstract Background Breast cancer (BC) is the most common type of cancer among women. Axillary lymph node metastasis (ALNM) is strongly correlated with distant metastasis, recurrence, and overall survival rates in BC. Therefore, accurate detection of ALNM holds valuable implications for patient prognosis and treatment plan selection. Purpose The objective of this study is to develop a novel domain adaptative radiomics pipeline based on domain adaptation (DA) to predict ALNM based on multi‐parametric magnetic resonance imaging (MRI) for multicenter studies. Methods 396 BC lesions collected from 391 patients at the first three centers were used as source domain data for model training and internal validation. 105 BC lesions collected from 105 patients at the fourth center were used as target domain data for model external validation. Each BC lesion was scanned with eight MRI sequences, including T2‐weighted, non‐fat‐saturated T1‐weighted and dynamic contrast‐enhanced sequences (phases 0–5). From each MRI sequence, 1648 radiomics features were extracted, resulting in a total of 13184 features extracted from each lesion. Variance threshold, the max‐relevance and min‐redundancy (mRMR), and the least absolute shrinkage and selection operator (LASSO) were employed for feature selection. Then a classifier based on balanced distribution adaptation (BDA) was developed for ALNM prediction. Unlike traditional radiomics models, the BDA classifier was designed to reduce data distribution differences across the three centers in the source domain by minimizing maximum mean discrepancy (MMD). The design of the BDA classifier is based on the assumption that the average distribution difference between the three centers in the source domain is similar to the distribution difference between the target domain data and the source domain data. Thus, when the BDA classifier is externally validated in the target domain, we can expect to achieve good predictive performance. The area under the receiver operating characteristic curve (AUC) with 95% confidence intervals (CI) was used to evaluate the predictive performance of the domain adaptative radiomics pipeline in the external validation cohort. To highlight the effectiveness of our proposed pipeline, the traditional radiomics pipeline with six machine learning models (support vector machine (SVM), k‐nearest neighbor (KNN), random forest (RF), decision tree (DT), extreme gradient boosting (XGBoost), linear discriminant analysis (LDA)) was also trained. The traditional radiomics pipeline with six models was compared with the domain adaptative radiomics pipeline in terms of AUC. Results After feature selection, 12 radiomics features were selected for the following modeling tasks. The external validation of the domain adaptative radiomics pipeline outperformed the other models, with an AUC of 0.781 (95%CI: 0.692–0.870). The best‐performing RF model among the six traditional radiomics models demonstrated an AUC of only 0.700 (95%CI: 0.605–0.795). The p ‐value for the comparison between BDA and RF model was 0.048. Conclusion Compared with the traditional radiomics pipeline, the proposed domain adaptative radiomics pipeline based on multi‐parametric MRI achieved better performance for ALNM prediction in this multicenter study.