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-b

SUBMITTER: Zhao Q 

PROVIDER: S-EPMC12409024 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature