AI Risk Prediction Tools for Autologous Breast Reconstruction

Autologous breast reconstruction offers patients a durable and natural-appearing option after mastectomy. However, complication risks include flap loss, infection, and delayed wound healing. This study developed both traditional statistical and machine learning (ML) models to predict the risk of developing a 90-day postoperative complication after autologous reconstruction.

Patient data were retrospectively collected for patients who underwent abdominal-based autologous breast reconstruction at Memorial Sloan Kettering Cancer Center (January 2015-September 2024). Multivariable logistic regression models and supervised ML models were developed to predict infection, hematoma, seroma, delayed wound healing, and flap compromise.

2,128 patients (3,249 flap reconstructions) were included. Overall, 90-day complications occurred in 475 (22.3%) patients, including infection (10%), hematoma (6%), delayed healing (4.1%), seroma (3.9%), and flap compromise (2.8%). AUCs ranged from 0.60 to 0.66 (logistic regression) and 0.64 to 0.73 (ML). Higher BMI was associated with increased risk for seroma (OR: 1.1, 95% CI: 1.04-1.13) and neoadjuvant chemotherapy for infection (OR:1.72, 95% CI: 1.17-2.51). Key ML model predictors on SHAP analysis included age, BMI, and pre-reconstruction radiation.

Individualized risk prediction models for complications after autologous breast reconstruction were developed using traditional statistics and ML. These models may help personalize treatment; however, further research is needed to enhance model predictive performance and clinical utility.