Prior-Radiomics-Guided Multi-Scale Feature Extraction Network Utilizing Preoperative MRI: A Pioneering Approach for Lymphovascular Invasion Prediction in Invasive Breast Cancer.

IntroductionIn patients with invasive breast cancer (IBC), the presence of lymphovascular invasion (LVI) is strongly associated with elevated risks of local recurrence, tumor metastasis, and poor prognosis. In this study, we aimed to develop a prior-radiomics-guided multiscale feature extraction network (PRM-Net) for predicting LVI in IBC using preoperative magnetic resonance imaging (MRI).MethodsThis retrospective study involved a cohort of 303 female patients with IBC who underwent MRI and surgical resection at our hospital between January 2019 and December 2023. The enrolled patients were randomly split into training and validation cohorts at a 7:3 ratio, with the training set used for model development and the validation set reserved for performance evaluation. Four predictive models were developed: 1) a diagnostic imaging hallmark model using logistic regression to analyze MRI morphological features; 2) a radiomics classifier incorporating feature engineering and operator-based feature selection; 3) a multiscale feature extraction deep learning network (M-Net), designed for end-to-end extraction of multiscale features from MRI scans; and 4) PRM-Net, which integrated deep learning and radiomics by the fusion of multiscale deep features and engineered radiomic features.ResultsPRM-Net achieved the highest diagnostic accuracy for LVI prediction [area under the curve (AUC) = 0.854, 95% confidence interval (CI): 0.779-0.929], outperforming M-Net (AUC = 0.816, 95% CI: 0.732-0.901), radiomics classifiers (AUC = 0.771, 95% CI: 0.648-0.894), and traditional imaging hallmarks (AUC = 0.761, 95% CI: 0.655-0.866).ConclusionsThese findings highlight the potential of PRM-Net in the preoperative prediction of LVI in patients with IBC and underscore the value of combining advanced radiomics with deep learning in clinical oncology. This approach may facilitate the identification of optimal surgical strategies tailored to individual patient needs.