Abstract
This study aimed to develop an intraoperative prediction model to evaluate the risk of non-sentinel lymph node (NSLN) metastasis in Chinese breast cancer patients with 1–2 positive sentinel lymph nodes (SLNs). The clinicopathologic data of 714 patients with 1–2 positive SLNs were investigated. Univariate and multivariate analyses were performed to identify the risk factors of NSLN metastasis. A new mathematical prediction model was developed based on LASSO and validated in an independent cohort of 131 patients. The area under the receiver operating characteristic curve (AUC) was used to quantify performance of the model. Patients with NSLN metastasis accounted for 37.3% (266/714) and 34.3% (45/131) of the training and validation cohorts, respectively. A LASSO regression-based prediction model was developed and included the 13 most powerful factors (age group, clinical tumour stage, histologic type, number of positive SLNs, number of negative SLNs, number of SLNs dissected, SLN metastasis ratio, ER status, PR status, HER2 status, Ki67 staining percentage, molecular subtype and P53 status). The AUCs of training and validation cohorts were 0.764 (95% CI 0.729–0.798) and 0.777 (95% CI 0.692–0.862), respectively. We presented a new prediction model with excellent clinical applicability and diagnostic performance for use by clinicians as an intraoperative clinical tool to predict risk of NSLN metastasis in Chinese breast cancer patients with 1–2 positive SLNs and make the final decisions regarding axillary lymph node dissection.
Highlights
This study aimed to develop an intraoperative prediction model to evaluate the risk of non-sentinel lymph node (NSLN) metastasis in Chinese breast cancer patients with 1–2 positive sentinel lymph nodes (SLNs)
Clinical evidence has shown that only 40% of patients with positive SLNs have further axillary involvement, highlighting the importance of a prediction model to evaluate the risk of non-sentinel lymph node (NSLN) metastasis in breast cancer patients with 1–2 positive S LNs8
The results showed that the area under the ROC curve (AUC) in macrometastasis and micrometastasis/ITC groups were 0.680 and 0.469 with the Memorial Sloan Kettering Cancer Center (MSKCC) nomogram and 0.676 and 0.574 with the Stanford nomogram, respectively, suggesting that these nomograms could not reliably predict the metastasis of non-sentinel lymph node in an Eastern population[13]
Summary
This study aimed to develop an intraoperative prediction model to evaluate the risk of non-sentinel lymph node (NSLN) metastasis in Chinese breast cancer patients with 1–2 positive sentinel lymph nodes (SLNs). Abbreviations ALN Axillary lymph node SLNB Sentinel lymph node biopsy ALND Axillary lymph node dissection SLNs Sentinel lymph nodes ACOSOG Z0011 American College of Surgeons Oncology Group Z0011 IBCSG 23-01 International Breast Cancer Study Group 23-01 LRR Locoregional recurrence DFS Disease-free survival OS Overall survival LVI Lymphovascular invasion HER2 Human epidermal growth factor receptor-2 BCR Rate of breast-conserving surgery NSLN Non-sentinel lymph node. Clinical evidence has shown that only 40% of patients with positive SLNs have further axillary involvement, highlighting the importance of a prediction model to evaluate the risk of non-sentinel lymph node (NSLN) metastasis in breast cancer patients with 1–2 positive S LNs8. We sought to develop a new intraoperative mathematical prediction model based on a Chinese population to evaluate the risk of NSLN metastasis in Chinese breast cancer patients with 1–2 positive SLNs. Least absolute shrinkage and selection operator (LASSO) regression was used to construct the model
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