Assessing the diagnostic value of hemodynamic distinctions between axillary lymph nodes and adjacent vessels in breast cancer axillary lymph node metastasis via breast magnetic resonance imaging.

BACKGROUND

Axillary lymph node metastasis (ALNM) is pivotal for breast cancer treatment and prognosis. Invasive tests may carry complications, while non-invasive methods like physical examination have poor accuracy. Existing AI-based models rely mostly on tumor-centric features. However, metastatic lymph nodes show neoangiogenesis and altered hemodynamics, leading to time-intensity curve (TIC) profiles similar to those of adjacent vessels. This study aimed to quantify these hemodynamic disparities from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and enhance ALNM prediction accuracy.

METHODS

A retrospective study included 186 patients (92 ALNM+, 94 ALNM-). Axillary vessels and lymph nodes were semi-automatically segmented via Hessian matrix algorithms. Four TIC-derived features [lymph-node TIC area (LNTICA), the difference in TIC area between the vessel ROI and lymph-node ROI (DIFF), non-negative area difference (NON-NEG DIFF), ratio of area difference to lymph node TIC area (RATIO TO LYMPH)] were extracted. ResNet50 extracted image features, and a Stacking framework integrated image, clinical, and TIC features, using support vector machine (SVM) and nomogram as classifiers. Statistical tests (F-test, t-test, and Kolmogorov-Smirnov test) validated feature discriminability.

RESULTS

All TIC features differed significantly between groups (P<0.001 for F-test, P<0.001 for t-test/Kolmogorov-Smirnov test for NON-NEG DIFF and RATIO TO LYMPH). RATIO TO LYMPH (mean: 0.11 vs. 0.36) showed optimal discriminability. Integrating TIC features improved area under the receiver operating characteristic curve (AUC): SVM (0.876→0.914) and nomogram (0.902→0.941) in the test set. SHapley Additive exPlanations (SHAP) analysis confirmed RATIO TO LYMPH as one of the top predictive features.

CONCLUSIONS

Lymph node-vessel hemodynamic disparities are robust ALNM biomarkers. Integrating these TIC-derived features with clinical and image data significantly enhances prediction accuracy, providing a non-invasive tool for clinical decision-making.