Machine Learning to Predict Implant-Based Breast Reconstruction Failure: A Bootstrap-Validated Elastic Net Model

Implant-based reconstruction failure remains a significant complication following breast reconstruction, with substantial implications for patient outcomes. Reliable preoperative risk prediction tools are lacking.

A retrospective single-center cohort study was conducted, including 381 IBR procedures performed from 2006 to 2023. Reconstruction failure was defined as explantation without immediate replacement within one year. Elastic net regression was used to develop a multivariable model based on age, BMI, smoking status, radiation therapy, chemotherapy, use of expander, and implant plane. Internal bootstrap validation was performed following the 2024 TRIPOD + AI guidelines. Model performance was assessed using AUC, calibration curves, and decision curve analysis. A real-time online calculator was deployed for clinical use.

The mean follow-up was 38.2 ± 27.2 months. The observed IBR failure rate was 5.5% (n = 21). The final model included key predictors such as postoperative radiotherapy, expander use, and an interaction term between age and BMI. Discrimination was strong with an optimism-corrected AUC of 0.856 (95% CI 0.76-0.935). Calibration analysis demonstrated optimal agreement between predicted and observed outcomes, and decision curve analysis confirmed the model's net clinical benefit over default strategies. The model is accessible via an online tool ( https://urls.fr/BIWCeO ).

This study presents a robust, internally validated predictive model for implant loss following IBR. The online calculator suggests real-time, individualized risk estimation, supporting preoperative counseling and surgical decision-making in the future. External validation and clinical implementation studies are warranted to confirm its broader applicability.

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