How to accurately preoperative screen nipple-sparing mastectomy candidate-a nomogram for predicting nipple-areola complex involvement risk in breast cancer patients.

Background: Nipple-sparing mastectomy (NSM) offers superior cosmetic outcomes and has been gaining wide acceptance. It has always been difficult to objectively quantify the risk of nipple-areola complex involvement (NACi). The goal was to develop a prediction model for clinical application.

Methods: Patients who had a total mastectomy (TM) between January 2016 and January 2020 at a single institute formed the development cohort (n = 578) and those who had NSM + immediate breast reconstruction (IBR) between January 2020 and January 2021 formed the validation cohort (n = 112). The prediction model was developed using univariate and multivariate logistic regression studies. Based on NACi risk variables identified in the development cohort, a nomogram was created and evaluated in the validation cohort. Meanwhile, stratified analysis was performed based on the model's risk levels and was combined with intraoperative frozen pathology (IFP) to optimize the model.

Results: Tumor central location, clinical tumor size (CTS) > 4.0 cm, tumor-nipple distance (TND) ≤ 1.0 cm, clinical nodal status positive (cN +), and KI-67 ≥ 20% were revealed to be good predictive indicators for NACi. A nomogram based on these major clinicopathologic variables was employed to quantify preoperative NACi risk. The accuracy was verified internally and externally. The diagnostic accuracy of IFP was 92.9%, sensitivity was 64.3%, and specificity was 96.9% in the validation group. Stratified analysis was then performed based on model risk. The diagnostic accuracy rates of IFP and NACiPM in low-risk, intermediate-risk, and high-risk respectively were 96.0%, 93.3%, 83.9%, 61.3%, 66.7%, and 83.3%.

Conclusion: We created a visual nomogram to predict NACi risk in breast cancer patients. The NACiPM can be used to distinguish the low, intermediate, and high risk of NAC before surgery. Combined with IFP, we can develop a decision-making system for the implementation of NSM.