ArticleJournal of magnetic resonance imaging : JMRI2026
MRI-Based Radiomics Model for Classifying Axillary Lymph Node Burden and Disease-Free Survival in Patients With Early-Stage Breast Cancer.
Article in Journal of magnetic resonance imaging : JMRI, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- MRI Habitat Analysis for Preoperative Prediction of Perineural Invasion and Prognostic Stratification in Rectal Cancer.Journal of magnetic resonance imaging : JMRI · 2026Article
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Abstract
backgroundAxillary lymph node (ALN) burden is a key prognostic determinant in breast cancer and plays an important role in diagnosis and treatment planning. The noninvasive assessment of ALN burden might improve patient stratification and guide individualized treatment. PURPOSE: To explore the potential of MRI-based radiomics in preoperative classification of ALN burden in early-stage breast cancer and to assess survival differences between patients with high- and low-ALN burden. STUDY TYPE: Retrospective. POPULATION: Pathologically confirmed breast cancer patients (n = 343): training (n = 170), testing (n = 73) and internal validation (n = 50) from center 1; center 2 (n = 50) for external validation. FIELD STRENGTH/SEQUENCE: 3T, dynamic contrast-enhanced (DCE) sequence. ASSESSMENT: Four different machine learning classifiers were used to develop clinical, radiomics, and combined models for preoperative ALN burden assessment (66 high-burden cases). DCE-MRI radiomics features were extracted, and the optimal model was used to determine the Radscore. A clinical model was derived from clinicopathological variables, and integrated with the Radscore to form a combined model. Kaplan-Meier and Cox regression analyses were performed to compare disease-free survival (DFS) between high- and low-burden groups. STATISTICAL TESTS: Intraclass Correlation Coefficient (ICC), LASSO, logistic regression, Mann-Whitney U tests, Chi-squared tests, DeLong's test, Area Under the Curve (AUC), Decision Curve Analysis (DCA), calibration curves and Kaplan-Meier analysis, with p < 0.05 as significant.
resultsThe Random Forest-based combined model yielded AUCs of 0.881 (95% CI, 0.811-0.941) in the training set, 0.826 (0.716-0.917) in the testing set, 0.912 (0.811-0.985) in the internal validation set, and 0.881 (0.737-0.985) in the external validation set. When using the cut-off value determined from the training set, the overall accuracy was 0.759, 0.795, 0.840, and 0.860, respectively. Kaplan-Meier analysis revealed significant DFS differences between the model-classified high- and low-burden groups (p = 0.022, HR = 2.9). DATA
conclusionMRI-based radiomics models show promise for noninvasive evaluation of ALN burden and prognostic stratification of survival outcomes in breast cancer patients. LEVEL OF EVIDENCE: 3: TECHNICAL EFFICACY: Stage 2.
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