Predictive value of dynamic contrast-enhanced-MRI-based machine learning model for lymphovascular invasion status in node-negative invasive breast cancer

Abstract

Abstract Purpose To retrospectively evaluate breast magnetic resonance imaging (MRI)-based machine learning (ML) model that can preoperatively predict lymphovascular invasion (LVI) status in invasive breast cancer patients with negative axillary lymph nodes (LNs). Methods We retrospectively enrolled 280 patients who underwent pre-operative breast MRI and were confirmed with invasive breast cancer by surgical pathology, with absence of axillary LN metastasis. This cohort included 148 LVI-positive lesions and 141 LVI-negative lesions, randomly divided (7:3) into training and validation cohort. The tumor segmentation from the first postcontrast dynamic contrast-enhanced (DCE)-MRI was semi-automatically implemented using 3D Slicer 5.1.0 software, and radiomics features of each lesion were extracted. Least absolute shrinkage and selection operator (LASSO) regression method was used to select the significant radiomics features, and five different ML algorithms were used to build models. A clinical model was also built and integrated with the radiomics models. The predictive performance of models were evaluated by discrimination and compared using DeLong test. Results A total of 4 clinico‑radiological parameters and 10 selected radiomics features were selected for model construction. The RF model that was built based on the radiomics and clinico‑radiological feature information exhibited the best predictive performance, yielding high AUC (0.97 for train and 0.82 for validation, respectively). The integrated model significantly outperformed the clinical model, whereas showed no significant difference from the radiomics model. Conclusions The RF model integrating radiomics features with clinical information facilitate LVI detection in invasive breast cancer patient with negative axillary LNs preoperatively, which was valuable for clinical decision-making.