In recent years, the results of the ACOSOG Z0011 and IBCSG 23-01 trials showed that neither the DFS nor the OS differed significantly between the SLNB-only and ALND groups among breast cancer patients with limited SLN involvement. Based on the results of these trials, the latest NCCN guidelines also recommended that ALND not be performed in patients with 1-2 involved SLNs who were planning to undergo breast-conserving surgery and subsequent radiotherapy . However, in most developing countries, such as China, the BCR is only approximately 20%, compared to 50%-80% in Western countries [6, 16, 17]. Even in some leading centres in China, the BCR is only 30% . Because of the low BCR in developing countries and the absence of evidence for ALND omission in Eastern populations, most clinicians in developing countries such as China still hold a conservative view and recommend ALND for patients with positive SLNs . In addition, the ALN status remains one of the most important prognostic factors. In our present study, only 266 (37.3%) of the patients with 1-2 SLN metastases in the training cohort were demonstrated to have NSLN metastasis after ALND, consistent with the results of previous studies [8, 19]. More than 60% of patients thus received unnecessary ALND. Therefore, it is of great importance to accurately predict NSLN metastasis either intraoperatively or preoperatively. Our study retrospectively analysed the clinicopathological data of 714 breast cancer patients with 1-2 positive SLNs in the training cohort to determine the factors associated with axillary involvement. We further developed a new mathematical prediction model based on this Chinese population to evaluate the risk of NSLN metastasis.
Previous studies have shown that LVI is a feature related to poor prognosis and that it promotes local recurrence and distant metastasis of tumours . Several recent studies have recognized LVI as an independent predictor of NSLN metastasis in patients with 1-2 positive SLNs [21, 22]. We arrived at the same conclusion. In our study, 76.0% and 35.8% of patients with LVI and without LVI, respectively, were found to have NSLN involvement, and this difference was significant. It remains controversial whether histologic grade is associated with NSLN metastasis, and the conclusions from Maimaitiaili A and Wang XY were inconsistent [23, 24]. Our univariate analysis showed that patients with higher histologic grades were more likely to have at least one positive ALN, and histologic grade remained an independent predictor of NSLN metastasis in the subsequent multivariate analysis (OR = 1.630; 95% CI: 1.061-2.505; P = 0.026).
Moreover, we divided all patients in the training cohort into the luminal A, luminal B, HER2 overexpression and triple negative subtypes according to St Gallen International Expert Consensus (2013 edition) . In these respective molecular subtype groups, 32.0%, 35.1%, 56.5% and 37.8% of the patients exhibited NSLN involvement. Compared to the triple negative type, the HER2 overexpression type was associated with a statistically higher risk of positive NSLNs, but the luminal A and luminal B subtypes were not. Whether NSLN metastasis is associated with the molecular subtype remains controversial. The results from a recent single-centre study of 291 patients demonstrated that patients with luminal B and HER2 overexpression breast cancer had a significantly higher possibility of having at least one positive NSLN than patients with luminal A breast cancer . However, in another retrospective study, investigators failed to identify molecular subtype as an independent predictor of NSLN metastasis. Patients with positive SLNs had the same risk of axillary involvement regardless of their molecular subtypes .
The number of positive SLNs, number of negative SLNs, the number of SLNs dissected and the SLN metastasis ratio were important predictors of NSLN metastasis in breast cancer patients with 1-2 positive SLNs. These factors rely heavily on assessment in intraoperative frozen sections. Thus, these values are unclear prior to surgery. Two publications considered the numbers of positive and negative SLNs as the independent risk factors included in their prediction models [26, 27]. The SLN metastasis ratio was incorporated into the model predictions in another clinical study . However, the value of the number of positive SLNs, number of negative SLNs, number of SLNs dissected and SLN metastasis ratio for predicting NSLN metastasis has not been fully clarified because of the collinearity among these factors. In our study, LASSO regression was used to construct the mathematical model, thus effectively solving the problem of collinearity among these factors.
The MSKCC nomogram, the first model to predict NSLN metastasis in patients with positive SLNs, performed well in the original population, with an AUC of 0.76 . The results from a previous study showed that the AUC of the MSKCC nomogram was less than 0.7, proving that the performance of the MSKCC nomogram was inferior to other models in other populations [29-31]. In addition, the MSKCC nomogram and other previous models were developed based on Western populations in developed countries, and thus hardly apply to the Eastern population. In the present retrospective analysis, we developed a new, LASSO algorithm-based intraoperative mathematical prediction model based on the clinical data of 714 patients in China for evaluating the risk of NSLN metastasis in Chinese breast cancer patients with 1-2 positive SLNs. The LASSO algorithm forces the sum of the absolute value of the regression coefficients to be less than a fixed value by reducing certain coefficients to zero, which helps to effectively construct a simpler model that includes only the most meaningful predictive factors. Ultimately, LASSO regression identified the 13 most powerful predictors: 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 coefficients of each predictor are shown in Table 4. The higher the absolute value of a regression coefficient, the greater is its influence on the model. In our prediction model, the most powerful predictor was the SLN metastasis ratio, with a coefficient of 0.7672377992. This factor was also included in the Cambridge model and another recent model [32, 33].
Previous evidences showed that the absolute agreement rate of histologic grade and LVI were only 75% and 69%, respectively, between the specimens obtained by core needle biopsy (CNB) and those obtained by surgical excision [34, 35]. The small volume of specimens from CNB and intratumoural heterogeneity are the possible reasons for the low concordance rate of histologic grade and LVI status between the preoperative CNB and postoperative pathology results. Considering that the aim of our study was to develop an intraoperative prediction model, histologic grade and LVI status, which are not entirely available via preoperative or intraoperative evaluation, were excluded from our prediction model, although they were identified as independent risk factors in the retrospective multivariate analysis.
Furthermore, we calculated the RS of each patient in the training cohort according to the model equation. The ROC curve for the prediction model was then generated and shown to have an AUC of 0.764 (95% CI: 0.729-0.798), comparable to that of the MSKCC nomogram . Thus, the predictive power of the model was acceptable. Finally, ROC curve analysis confirmed the cutoff value of the RS to be 1.87239924305. The sensitivity, specificity and total accuracy were 74.1%, 69.6% and 71.3%, respectively. However, the FNR was as high as 25.9%. When the cutoff value of RS was set as 1.059327995, the FNR was only 9.8%, less than the clinically acceptable rate of 10%. Therefore, we believe that ALND may be safely ignored when the RS of a patient, as calculated by the model equation, is less than the cutoff value of 1.059327995. We divided the patients in the training cohort into a low-RS group and a high-RS group according to the cutoff value. Significantly more patients had positive NSLNs in the high-RS group than in the low-RS group, which further confirmed the predictive power of our model.
To evaluate the clinical applicability of the prediction model, a subsequent independent cohort of 131 patients was used for validation. The model still showed impressive performance, with an AUC of 0.777 (95% CI: 0.692-0.862). The present prediction model can thus be considered an intraoperative clinical tool for clinicians to predict the risk of NSLN metastasis in Chinese breast cancer patients with 1-2 positive SLNs and make the decision regarding ALND.
To our knowledge, this study is the first one to apply the LASSO algorithm to develop a prediction model based on an Eastern population. As more than 13 factors were included in our model, it offers a more personalized assessment for breast cancer patients. However, there are a few limitations in our study. First, this was a retrospective study, and a prospective clinical trial is greatly needed. Second, our prediction model should be validated with population data from other centres.