Assessment of Lymphovascular Invasion in Breast Cancer Using a Combined MRI Morphological Features, Radiomics, and Deep Learning Approach Based on Dynamic Contrast‐Enhanced MRI

Background Assessment of lymphovascular invasion (LVI) in breast cancer (BC) primarily relies on preoperative needle biopsy. There is an urgent need to develop a non‐invasive assessment method. Purpose To develop an effective model to assess the LVI status in patients with BC using magnetic resonance imaging morphological features (MRI‐MF), Radiomics, and deep learning (DL) approaches based on dynamic contrast‐enhanced MRI (DCE‐MRI). Study Type Cross‐sectional retrospective cohort study. Population The study included 206 BC patients, with 136 in the training set [97 LVI(−) and 39 LVI(+) cases; median age: 51.5 years] and 70 in the test set [52 LVI(−) and 18 LVI(+) cases; median age: 48 years]. Field Strength/Sequence 1.5 T/T1‐weighted images, fat‐suppressed T2‐weighted images, diffusion‐weighted imaging (DWI), and DCE‐MRI. Assessment The MRI‐MF model was developed with conventional MR features using logistic analyses. The Radiomic feature extraction process involved collecting data from categorized DCE‐MRI datasets, specifically the first and second post‐contrast images (A1 and A2). Next, a DL model was implemented to determine LVI. Finally, we established a joint diagnosis model by combining the MRI‐MF, Radiomics, and DL approaches. Statistical Tests Diagnostic performance was compared using receiver operating characteristic curve analysis, confusion matrix, and decision curve analysis. Results Rim sign and peritumoral edema features were used to develop the MRI‐MF model, while six Radiomics signature from the A1 and A2 images were used for the Radiomics model. The joint model (MRI‐MF + Radiomics + DL models) achieved the highest accuracy (area under the curve [AUC] = 0.857), being significantly superior to the MRI‐MF (AUC = 0.724), Radiomics (AUC = 0.736), or DL (AUC = 0.740) model. Furthermore, it also outperformed the pairwise combination models: Radiomics + MRI‐MF (AUC = 0.796), DL + MRI‐MF (AUC = 0.796), or DL + Radiomics (AUC = 0.826). Data Conclusion The joint model incorporating MRI‐MF, Radiomics, and DL approaches can effectively determine the LVI status in patients with BC before surgery. Level of Evidence 4 Technical Efficacy Stage 2