Radiomic Nomogram for Predicting Axillary Lymph Node Metastasis in Patients with Breast Cancer

Rationale and ObjectivesThe detection of axillary lymph node metastasis (ALNM) in patients with breast cancer is a crucial determinant in the decision-making process for axillary surgery and potential therapies. The objective of this study was to develop and validate a radiomics nomogram that integrates radiomics features from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) with clinical factors to predict ALNM in patients with breast cancer.Materials and MethodsA total of 177 patients with breast cancer were randomly divided into a training set (n = 123) and a validation set (n = 54) using a 7:3 ratio. From the DCE-MRI images, 2818 radiomics features were extracted from the primary tumor and axillary lymph node (ALN). Subsequently, optimal features were selected through the least absolute shrinkage and selection operator algorithm to construct the Radscore. Clinical factors were identified using univariate logistic regression analysis and included in a multivariate logistic regression analysis. Using the Radscore and clinical factors, a radiomics nomogram was developed using the Support Vector Machine method. The predicting efficacy of our model was visually appraised utilizing a receiver operator characteristic (ROC) curve, while its clinical application and predictive accuracy were assessed through decision curve analysis (DCA) and calibration curves, respectively.ResultsThe results revealed Ki67, multifocality, and MRI-reported ALN status as independent risk factors for ALNM. The radiomics nomogram demonstrated good calibration and discrimination with areas under the ROC curve of 0.92 (95% confidence interval [CI], 0.88–0.97) in the training set and 0.90 (95% CI, 0.72–0.90) in the validation set. DCA revealed the clinical usefulness of the radiomics nomogram.ConclusionThe DCE-MRI-based radiomics nomogram is a reliable tool for assessing ALNM in patients with breast cancer. The detection of axillary lymph node metastasis (ALNM) in patients with breast cancer is a crucial determinant in the decision-making process for axillary surgery and potential therapies. The objective of this study was to develop and validate a radiomics nomogram that integrates radiomics features from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) with clinical factors to predict ALNM in patients with breast cancer. A total of 177 patients with breast cancer were randomly divided into a training set (n = 123) and a validation set (n = 54) using a 7:3 ratio. From the DCE-MRI images, 2818 radiomics features were extracted from the primary tumor and axillary lymph node (ALN). Subsequently, optimal features were selected through the least absolute shrinkage and selection operator algorithm to construct the Radscore. Clinical factors were identified using univariate logistic regression analysis and included in a multivariate logistic regression analysis. Using the Radscore and clinical factors, a radiomics nomogram was developed using the Support Vector Machine method. The predicting efficacy of our model was visually appraised utilizing a receiver operator characteristic (ROC) curve, while its clinical application and predictive accuracy were assessed through decision curve analysis (DCA) and calibration curves, respectively. The results revealed Ki67, multifocality, and MRI-reported ALN status as independent risk factors for ALNM. The radiomics nomogram demonstrated good calibration and discrimination with areas under the ROC curve of 0.92 (95% confidence interval [CI], 0.88–0.97) in the training set and 0.90 (95% CI, 0.72–0.90) in the validation set. DCA revealed the clinical usefulness of the radiomics nomogram. The DCE-MRI-based radiomics nomogram is a reliable tool for assessing ALNM in patients with breast cancer.