Work

The Impact of Post Mastectomy Radiotherapy on T1-2N0-1 Male Breast Cancer and establishment of an artificial neural network predicting model: Population-Based Study

Year: 2025

Type: article

Abstract: Purpose: The objective of this study was to analyze the impact of post-mastectomy radiotherapy (PMRT) in male breast cancer (MBC) patients and develop an artificial neural network (ANN) model to identify a potential PMRT benefit population. Materials and Methods: Data from a total of 2,247 MBC patients with T1-2N0-1M0 who underwent total mastectomy between 1998 and 2016 were enrolled from the SEER database. Propensity score matching was used to reduce covariate imbalances. Cox regression analysis was conducted to compare overall survival (OS) between the PMRT and no-PMRT groups. The hypothesis was that patients who had undergone PMRT and lived longer than the median OS of the no-PMRT group could benefit from PMRT. An ANN model was then developed to predict PMRT benefit population. Results: Multivariate Cox regression analysis demonstrated better OS in the PMRT group compared to the no-PMRT group of matched patients. This survival benefit was particularly significant in patients with grade III or T2N0 and T2N1 disease, while no significant difference was observed in patients with grade I/II or T1N0 and T1N1 disease. An ANN model was established to predict PMRT benefit population based on patients with T2N0/T2N1. The optimal cut-off value for the model predicted probability was 0.51. Survival curves indicated that a score of 0.51 could accurately distinguish potential PMRT benefit population. Conclusion: For MBC patients with T2N0, T2N1, and grade III, PMRT would improve survival. The ANN model would be used to identify patients who are likely to benefit from PMRT and aid in clinical decision-making.

Source: Cancer Research and Treatment

DOI: 10.4143/crt.2025.518