Ultrasound-based radiomics model for predicting axillary lymph node metastasis of breast cancer

This study aims to explore the impact of different ROI delineation strategies on the axillary lymph nodes metastasis (ALNM) prediction model by analyzing two-dimensional ultrasound images of lymph nodes. In addition, we integrated clinical and pathological information to construct a comprehensive model, and based on this model, developed a nomogram for individualized assessment of the probability of ALNM.

A total of 146 axillary lymph nodes were randomly divided into a training set and a testing set at a ratio of 8:2. Clinical and pathological features were selected using univariate and multivariate logistic regression analyses, followed by the construction of a clinical prediction model. Radiomic features were extracted from both the internal and surrounding regions of the two-dimensional ultrasound images of the axillary lymph nodes. The least absolute shrinkage and selection operator (LASSO) algorithm was then used to select and retain the optimal features, followed by the construction of a radiomic prediction model. A combined prediction model was developed by integrating the clinical and radiomic models, and a nomogram was created for the combined prediction model.

The clinical status of axillary lymph nodes was an independent predictor for metastasis. The clinical prediction model based on the status of axillary lymph nodes achieved an AUC of 0.728 in the testing set. The radiomic prediction model based on the LASSO logistic regression algorithm with a 1 mm extended region had the highest AUC of 0.856 in the testing set. The combined prediction model integrating the clinical and optimal radiomic models achieved an AUC of 0.841 in the testing set, with a sensitivity of 77.4% and an accuracy of 79.5%. This combined model outperformed the individual clinical and radiomic models and was more effective in predicting axillary lymph node metastasis.

This study developed a predictive model for ALNM based on ultrasound images of ALNs and their peripheral extended regions. The results demonstrated that the combined model incorporating both the lymph node and a 1-mm peripheral extension yielded the best predictive performance. Furthermore, a comprehensive integrated model was established by incorporating clinical and pathological characteristics, which effectively enhanced the prediction of ALNM.