# Clinical–radiomics model for predicting internal mammary lymph node metastasis in operable breast cancer patients

## TL;DR

A new model combining clinical data and MRI radiomics improves prediction of internal mammary lymph node metastasis in breast cancer patients.

## Contribution

A novel clinical–radiomics model is proposed for predicting internal mammary lymph node metastasis in breast cancer.

## Key findings

- The clinical–radiomics model achieved an AUC of 0.964, outperforming standalone clinical and radiomics models.
- The model demonstrated strong calibration and stability with 90.23% average accuracy in cross-validation.
- The model's decision curve analysis confirmed its clinical utility and optimal predictive efficiency.

## Abstract

Although preoperative prediction of axillary lymph nodes status has been achieved using radiomics and combined models, there is a dearth of research on internal mammary lymph node (IMN) metastasis status prediction. We developed a predictive model by combining clinicopathological factors with preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics to accurately predict IMN metastasis in breast cancer.

Patients who had no evidence of IMN metastasis on preoperative images but underwent internal mammary sentinel lymph node biopsy (IM-SLNB) were included in this study. Preoperative DCE-MRI and clinicopathological data of 124 patients with breast cancer were obtained, to developed Clinical, radiomics, and clinical–radiomics models, separately. Decision curve analysis (DCA) was employed to assess the models’ clinical applicability.

The resulting area under the curves (AUCs) were 0.913, 0.831, 0.964 for the clinical model, the radiomics model, and the clinical–radiomics model, respectively. The Delong test revealed significant differences in the receiver operating characteristic (ROC) curves only between the clinical and clinical–radiomics models (all P<0.05). DCA substantiated the clinical–radiomics model’s optimal predictive efficiency, enhanced discriminatory ability, and maximum benefit. The AUC (95% confidence interval: 0.935-0.993) of the clinical–radiomics model is 0.964. Repeated k-fold cross validation showed that average accuracy and Standard deviation of clinical–radiomics model are 90.23% and 8.45%, respectively. And the calibration slope of clinical–radiomics model is 1.08(p=0.071).

Although the clinical model was effective in predicting IMN status, the addition of DCE-MRI radiomics significantly improved the predictive value of the clinical–radiomics model, which showed excellent discrimination, calibration, and stability. This suggests that the clinic-radiomics model has potential for preoperative assessment of IMN metastasis risk in breast cancer patients, but external validation is needed to confirm its clinical utility. IMN irradiation is recommended for early patients with high IMN metastasis risk, and overtreatment should be avoided for patients with low metastasis risk.