Predicting Axillary Lymph Node Metastasis in Young Onset Breast Cancer: A Clinical-Radiomics Nomogram Based on DCE-MRI.

BACKGROUND

Young onset breast cancer, diagnosed in women under 50, is known for its aggressive nature and challenging prognosis. Precisely forecasting axillary lymph node metastasis (ALNM) is essential for customizing treatment plans and enhancing patient results.

OBJECTIVE

This research sought to create and verify a clinical-radiomics nomogram that combines radiomic features from Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) with standard clinical predictors to improve the accuracy of predicting ALNM in young breast cancer patients.

METHODS

We performed a retrospective analysis at one facility, involving the creation and validation of a nomogram in two stages.At first, a medical model was developed utilizing conventional indicators like tumor dimensions, molecular classifications, multifocal presence, and MRI-determined ALN status.A more detailed clinical-radiomics model was subsequently developed by integrating radiomic characteristics derived from DCE-MRI images.These models were created using logistic regression analyses on a training dataset, and their effectiveness was assessed by measuring the area under the receiver operating characteristic curve (AUC) in a separate validation dataset.

RESULTS

The clinical-radiomics nomogram surpassed the clinical-only model, recording an AUC of 0.892 in the training dataset and 0.877 in the validation dataset.Significant predictors included MRI-reported ALN status and select radiomic features, which markedly enhanced the model's predictive capacity.

CONCLUSION

Integrating radiomic features with clinical predictors in a nomogram significantly improves ALNM prediction in young onset breast cancer, providing a valuable tool for personalized treatment planning. This study underscores the potential of merging advanced imaging data with clinical insights to refine oncological predictive models. Future research should expand to multicentric studies and include genomic data to boost the nomogram's generalizability and precision.