Nomogram based on radiomics analysis of primary breast cancer ultrasound images: prediction of axillary lymph node tumor burden in patients.

Objectives: To establish a prediction model for evaluating the axillary lymph node (ALN) status of patients with T1/T2 invasive breast cancer based on radiomics analysis of US images of primary breast lesions.
Methods: Between August 2016 and November 2018, a total of 343 patients with histologically proven malignant breast tumors were included in this study and randomly assigned to the training and validation groups at a ratio of 7:3. ALN tumor burden was defined as low (< 3 metastatic ALNs) or high (≥ 3 metastatic ALNs). Radiomics features were obtained using the PyRadiomics package, and the radiomics score was established by least absolute shrinkage and selection operator regression. A nomogram combining the breast cancer US radiomics score with patient age and lesion size was generated based on the multivariate logistic regression results.
Results: In the training and validation cohorts, 29.1% (69/237) and 32.08% (34/106) of patients were pathologically diagnosed with more than 2 metastatic ALNs, respectively. The radiomics score consisted of 16 US features, and patient age and lesion diameter identified by US were included to construct the model. The AUC of the model was 0.846 (95% CI, 0.790-0.902) for the training cohort and 0.733 (95% CI, 0.613-0.852) for the validation cohort. The calibration curves showed good agreement between the predictions and observations.
Conclusions: Our novel nomogram demonstrates high accuracy in predicting ALN tumor burden in breast cancer patients. We also suggest further development of PyRadiomics to improve US radiomics.
Key Points: • A nomogram based on US was developed to predict ALN tumor burden (low, < 3 metastatic ALNs; high, ≥ 3 metastatic ALNs). • The nomogram could assist clinicians in evaluating treatment strategies for T1/T2 invasive breast cancer.