External validation and modification of nomogram for predicting positive resection margins before breast conserving surgery

A positive resection margin after breast conserving surgery (BCS) is the most important risk factor for tumor recurrence. In 2012, Seoul National University Hospital (SNUH) breast surgery team developed a nomogram for predicting positive resection margins before BCS to provide individual surgical plans that could reduce local recurrence without increasing re-excision rates. The purpose of this study was to validate this nomogram using an external cohort and to test if addition of surgeon-related factor could improve its use as a predictive model.

A total of 419 patients with breast cancer who underwent BCS from January to December 2018 were retrospectively reviewed. Using the SNUH BCS nomogram, risk score for positive resection margins was calculated for 343 patients. The predictive accuracy of the nomogram was assessed, and multivariable logistic regression analyses were performed to evaluate the nomogram’s predictive variables.

The positive resection margin rate of the current external validation cohort was 13.5% (46 out of 343), compared to 14.6% (151 out of 1034) of the original study. The discrimination power of the SNUH BCS nomogram as measure by area under the receiver operating characteristics curve (AUC) was 0.656 [95% confidence interval (CI) 0.576–0.735]. This result is lower than expected value of 0.823 [95% CI 0.785–0.862], the AUC of the original study. Multivariable logistic regression analysis showed that, among the five nomogram variables, presence of tumor size discrepancy greater than 0.5 cm between MRI and ultrasonography (OR 2.445, p = 0.019) and presence of ductal carcinoma in situ on needle biopsy (OR 2.066, p = 0.048) were significantly associated with positive resection margins. Finally, the nomogram score was re-calculated by adding each surgeon’s resection margin positive rate as odds ratio and the AUC was increased to 0.733.

Validation of the SNUH BCS nomogram was not successful in the current study as much as its original publication. However, we could improve its predictive power by including surgeon-related factor. Before applying a published nomogram as a preoperative predictive model, we suggest each institution to validate the model and adjust it with surgeon-related factor. Addition of new factors to currently available nomograms holds promise for improving its applicability for breast cancer patients at the actual clinical level.