Integrating Peritumoral and Intratumoral Radiomics with Deep Learning for Preoperative Prediction of Lymphovascular Invasion in Invasive Breast Cancer Using DCE-MRI.

Abstract

BackgroundLymphovascular invasion (LVI) is a critical factor in breast cancer (BC) prognosis and treatment planning, yet preoperative non-invasive assessment remains challenging. This research proposes the design and validation of a comprehensive artificial intelligence (AI) system that combines intratumoral and peritumoral radiomic analysis, deep learning (DL)-derived features, and clinical risk indicators extracted from dynamic contrast-enhanced MRI (DCE-MRI), with the goal of predicting LVI status in patients with BC.MethodsThis multi-institutional retrospective study included 496 IBC patients (training cohort: n = 344; validation cohort: n = 152). DCE-MRI scans were acquired preoperatively, and intratumoral/peritumoral (0-1, 1-3, 3-5 mm) radiomics features were extracted. A ResNet-50-based DL model was applied to 2.5D tumor slices, and clinical risk factors were identified via logistic regression. The least absolute shrinkage and selection operator (LASSO) method was employed to identify the most relevant features. The ensemble model was created by combining the Intra- Peri Fusion model with the clinically independent risk factors. Model performance was evaluated by sensitivity, specificity, AUC, and decision curve analysis (DCA).ResultsLVI was present in 33.8% and 32.7% of the training and validation cohorts. The SVM (Support Vector Machine) Intra-Peri Fusion model reached AUCs of 0.921 and 0.906, showing enhanced discriminative performance over single-region approaches. The ensemble model, derived from integrating a fusion model with clinical risk factors, demonstrated superior performance with AUCs of 0.951 (training) and 0.929 (validation) and high net benefit in DCA. Calibration curves confirmed excellent agreement between predicted and observed outcomes.ConclusionThe AI-driven ensemble model combining radiomics, DL, and clinical features enables accurate preoperative prediction of LVI in IBC, which holds potential for optimizing surgical planning and adjuvant therapy strategies.