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Background <p>This study aimed to develop a dynamic nomogram in which clinical indicators are integrated with magnetic resonance imaging (MRI) radiomics to predict axillary lymph node metastasis (ALNM) in patients with breast cancer.</p> Methods <p>Our retrospective study included 221 pathologically confirmed patients with breast cancer. Radiomic features were extracted from dynamic contrast-enhanced MRI (DCE-MRI) and fat-suppressed T2-weighted imaging (FS-T2WI) datasets. After feature screening, a support vector machine (SVM) algorithm was employed to establish radiomic models and calculate radiomic scores. Clinical independent predictors were identified through univariate and multivariate logistic regression analyses. A nomogram was established on the basis of the radiomic scores obtained with the optimal SVM model and clinically independent predictors, and it was subsequently transformed into a dynamic nomogram. Model performance was evaluated by the receiver operating characteristic curve and area under the curve (AUC). Shapley additive explanation (SHAP) was applied to interpret the contribution of clinical predictors. Calibration and decision curves were employed to assess nomogram performance.</p> Results <p>The platelet-to-lymphocyte ratio (PLR), breast imaging reporting and data system (BI-RADS) classification, and ALN status on MRI were identified as independent predictors of ALNM (all <i>p</i> &lt; 0.05). SHAP analysis identified PLR as the top contributor. The clinical model developed with the three predictors achieved AUCs of 0.845 and 0.706 in the training and validation cohorts, respectively. Among the SVM models, the DCE-MRI and FS-T2WI fusion sequence model outperformed the single-sequence models, with AUCs of 0.868 and 0.875, respectively, in the training and validation cohorts. When clinical predictors were incorporated into the fusion sequence model, the nomogram achieved AUC values of 0.930 and 0.928 in the training and validation cohorts. Decision curve analysis demonstrated that the nomogram has significant clinical value.</p> Conclusions <p>A nomogram model integrating MRI-derived radiomic scores, BI-RADS classification, ALN status, and PLR demonstrated good performance in predicting ALNM in patients with breast cancer.</p>

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Dynamic nomogram for predicting axillary lymph node metastasis in breast cancer based on MRI analysis and the platelet-to-lymphocyte ratio (PLR)

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月26日

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  • Jie Wang,
  • Qinan Geng,
  • Manman Wang,
  • Mengmeng Yang,
  • Yilan Zhang

摘要

Background

This study aimed to develop a dynamic nomogram in which clinical indicators are integrated with magnetic resonance imaging (MRI) radiomics to predict axillary lymph node metastasis (ALNM) in patients with breast cancer.

Methods

Our retrospective study included 221 pathologically confirmed patients with breast cancer. Radiomic features were extracted from dynamic contrast-enhanced MRI (DCE-MRI) and fat-suppressed T2-weighted imaging (FS-T2WI) datasets. After feature screening, a support vector machine (SVM) algorithm was employed to establish radiomic models and calculate radiomic scores. Clinical independent predictors were identified through univariate and multivariate logistic regression analyses. A nomogram was established on the basis of the radiomic scores obtained with the optimal SVM model and clinically independent predictors, and it was subsequently transformed into a dynamic nomogram. Model performance was evaluated by the receiver operating characteristic curve and area under the curve (AUC). Shapley additive explanation (SHAP) was applied to interpret the contribution of clinical predictors. Calibration and decision curves were employed to assess nomogram performance.

Results

The platelet-to-lymphocyte ratio (PLR), breast imaging reporting and data system (BI-RADS) classification, and ALN status on MRI were identified as independent predictors of ALNM (all p < 0.05). SHAP analysis identified PLR as the top contributor. The clinical model developed with the three predictors achieved AUCs of 0.845 and 0.706 in the training and validation cohorts, respectively. Among the SVM models, the DCE-MRI and FS-T2WI fusion sequence model outperformed the single-sequence models, with AUCs of 0.868 and 0.875, respectively, in the training and validation cohorts. When clinical predictors were incorporated into the fusion sequence model, the nomogram achieved AUC values of 0.930 and 0.928 in the training and validation cohorts. Decision curve analysis demonstrated that the nomogram has significant clinical value.

Conclusions

A nomogram model integrating MRI-derived radiomic scores, BI-RADS classification, ALN status, and PLR demonstrated good performance in predicting ALNM in patients with breast cancer.