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

Area of Science:

• Oncology
• Radiology
• Medical Imaging

Background:

• Young onset breast cancer (diagnosed under 50) presents aggressive characteristics and a challenging prognosis.
• Accurate prediction of axillary lymph node metastasis (ALNM) is crucial for tailoring treatment strategies and improving outcomes in these patients.

Purpose of the Study:

• To develop and validate a clinical-radiomics nomogram integrating radiomic features from Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) with clinical predictors.
• To enhance the prediction accuracy of ALNM in young breast cancer patients.

Main Methods:

• A retrospective analysis was conducted, developing and validating a nomogram in two stages.
• A clinical model used standard predictors; a clinical-radiomics model incorporated DCE-MRI derived radiomic features.
• Logistic regression was used for model development on a training set, with performance evaluated using Area Under the Curve (AUC) on a validation set.

Main Results:

• The clinical-radiomics nomogram demonstrated superior performance compared to the clinical-only model.
• The nomogram achieved an AUC of 0.892 in the training set and 0.877 in the validation set.
• MRI-reported ALN status and specific radiomic features were significant predictors, substantially improving predictive capability.

Conclusions:

• Integrating radiomic features with clinical predictors significantly enhances ALNM prediction in young-onset breast cancer.
• The developed nomogram serves as a valuable tool for personalized treatment planning in this patient group.
• Future multicentric studies incorporating genomic data are recommended to improve generalizability and precision.