Personalized recurrence risk prediction in early-stage breast cancer through an integrative mathematical model based on MammaPrint®, radiotherapy, phenotype, and clinicopathological factors

The predictive model fitted using Firth's penalized logistic regression demonstrated an adequate discriminative ability (AUC = 0.755). MMP was the variable with the greatest weight, followed by RT. These variables allow for a more accurate prediction of recurrence risk than traditional clinicopathological factors, supporting their value in the personalization of treatment. This study reports statistically significant differences when comparing the group without radiotherapy (BED = 0 Gy) to the low-dose group (BED < 60 Gy), with a p-value of 0.0475.