Application of the Machine-Learning Model to Improve Prediction of Non-Sentinel Lymph Node Metastasis Status Among Breast Cancer Patients - Wu, Qian; Deng, Li; Jiang, Ying; Zhang, Hongwei (Frontiers in Surgery, 2022)
Background Performing axillary lymph node dissection (ALND) is the current standard option after a positive sentinel lymph node (SLN). However, whether 1-2 metastatic SLN requires ALND is debatable. The probability of metastasis in non-sentinel lymph nodes (NSLN) can be calculated using nomograms. In this study, we developed an individualized model using machine learning (ML) methods to select potential variables which influence NSLN metastasis. Materials and Methods Cohorts of early breast cancer patients who underwent SLN biopsy and ALND between 2012 and 2021 were created (training cohort, N 157 and validation cohort, N 58) for development of the nomogram. Three ML methods were trained in the training set to create a strong predictive model. Finally, the multiple iterations of the least absolute shrinkage and selection operator regression method was used to determine the variables associated with NSLN status. Results Four independent variables (positive SLN number, absence of lymph node hilum, lymphovascular invasion and total number of SLNs harvested) were combined to generate the nomogram. The area under the receiver operating characteristic curve (AUC) value of 0.759 was obtained in the entire set. The AUC values for the training set and the test set were 0.782 and 0.705 respectively. The hosmer-lemeshow test of the model fit accuracy was identified with P=0.759. Conclusion This study developed a nomogram that incorporates ultrasound-related variables using ML method and serves to clinically predict non-metastatic status of NSLN and help in selection of the appropriate treatment option.