Development and Validation of an Ipsilateral Breast Tumor Recurrence Risk Estimation Tool Incorporating Real-World Data and Evidence From Meta-Analyses: A Retrospective Multicenter Cohort Study

Purpose Ipsilateral breast tumor recurrence (IBTR) remains a critical concern for patients undergoing breast-conserving surgery (BCS). Reliable risk estimation tools for IBTR risk can support personalized surgical and adjuvant treatment decisions, especially in the era of evolving systemic therapies. We aimed to develop and validate models to estimate IBTR risk.
Patients and Methods This multicenter retrospective cohort study included 8,938 women who underwent partial mastectomy for invasive breast cancer between 2008 and 2017. Prediction models were developed using Cox proportional hazards regression and validated via bootstrap resampling. Model performance was assessed using Harrell's C-index, Brier scores, calibration plots, and goodness-of-fit tests.
Results During a median follow-up of 9.0 years (IQR, 6.6-10.9), IBTR occurred in 320 patients (3.6%). The initial model, based on variables from Sanghani et al, achieved a Harrell's C-index of 0.74. Incorporating hormonal receptor status, human epidermal growth factor receptor 2 status, radiotherapy, and targeted therapy as predictors reduced the C-index to 0.65, despite their clinical relevance. Importantly, the inclusion of these factors improved calibration, demonstrating better alignment between predicted and observed IBTR probabilities. Although the hazard ratios (HRs) for radiotherapy aligned with the Early Breast Cancer Trialists’ Collaborative Group meta-analyses (MA), those for chemotherapy and endocrine therapy showed slight differences. Therefore, HRs from the MA were used to represent treatment effects in our model.
Conclusion We have developed and internally validated a new risk estimation model for IBTR using Cox regression and bootstrap methods. A Web-based risk estimation tool is now available to facilitate individualized risk assessment and treatment planning.