Development and validation of a nomogram for predicting internal mammary sentinel node metastasis in breast cancer patients.

Objective: Internal mammary nodes are important in breast cancer prognosis, but their diagnosis is often missed in clinical practice, leading to inaccurate staging and treatment. We developed a validated nomogram to predict the presence of internal mammary sentinel nodes (IMSN) metastasis.

Methods: A total of 864 sequential IMSN biopsy procedures from a prospective studies database of 1505 cases were used for model development and validation. Multivariable logistic regression was performed on 519 sequential IMSN biopsy procedures from multi-center data between August 2018 and July 2022 to predict the presence of IMSN metastasis. A nomogram was developed based on the logistic regression model and subsequently applied to 345 sequential IMSN biopsy procedures from single-center data between November 2011 and July 2018. The model's discrimination was assessed using the area under the receiver operating characteristic curve.

Results: The overall frequency of IMSN metastasis was 17.0% in our study. A predictive model for IMSN metastasis was constructed using tumor size, tumor location, lymphovascular invasion, the number of positive axillary nodes (P < 0.05 for all variables in multivariate analysis), and histological grade (P < 0.05 only in univariate analysis). The nomogram was accurate, with a concordance index of 0.84 in the bootstrapping analysis and an area under the receiver operating characteristic curve of 0.80 in the validation population.

Conclusion: Our nomogram provides an accurate and validated multivariable predictive model for estimating the individual likelihood of having IMSN metastasis. This may be useful for personalized treatment decisions regarding internal mammary radiotherapy in breast cancer patients.