Development and Validation of an Ipsilateral Breast Tumor Recurrence Risk Estimation Tool Incorporating Real-World...

Area of Science:

• Oncology
• Surgical Oncology
• Biostatistics

Background:

• Ipsilateral breast tumor recurrence (IBTR) is a significant concern following breast-conserving surgery (BCS).
• Accurate risk prediction is crucial for personalized treatment strategies in breast cancer management.
• Evolving systemic therapies necessitate updated tools for estimating IBTR risk.

Purpose of the Study:

• To develop and validate predictive models for estimating the risk of ipsilateral breast tumor recurrence (IBTR).
• To provide a reliable tool for personalized surgical and adjuvant treatment decisions in breast cancer patients undergoing BCS.

Main Methods:

• A multicenter retrospective cohort study involving 8,938 women who underwent partial mastectomy for invasive breast cancer (2008-2017).
• Development of prediction models using Cox proportional hazards regression.
• Internal validation through bootstrap resampling, assessed with Harrell's C-index, Brier scores, and calibration plots.

Main Results:

• During a median follow-up of 9.0 years, 3.6% of patients experienced IBTR.
• An initial model achieved a C-index of 0.74; incorporating key clinical factors slightly reduced it to 0.65 but improved calibration.
• Hazard ratios for treatment effects were derived from meta-analyses to enhance model accuracy.

Conclusions:

• A novel, internally validated risk estimation model for IBTR has been developed using Cox regression and bootstrap methods.
• A web-based tool is now available to support individualized risk assessment and treatment planning for breast cancer patients.