Predicting Surgical Outcomes in Breast Reconstruction With Machine Learning: A Systematic Review

Clinical Snapshot

Key Findings

• P Value: P < 0.01 for BREAST-Q prediction superiority; P = 0.041 for class imbalance methods

• Effect Size: Median AUC 0.71 across all models

• Primary Outcome: Model discrimination measured by AUC-ROC

• Nnt Or Sensitivity: BREAST-Q prediction models showed β = 0.13 improvement over skin necrosis prediction

• Confidence Interval: IQR 0.16 for AUC values

Clinical Application

Requires institutional ML infrastructure and standardized data collection protocols Relevant to Australian plastic surgery practice; BREAST-Q is validated tool used in Australian centres; would need integration with existing clinical pathways and potentially BreastScreen Australia data Breast reconstruction patients in tertiary plastic surgery centres

Abstract

INTRODUCTION: The applications of artificial intelligence (AI) in plastic surgery have grown considerably in recent years. As large patient datasets become more accessible, surgeons are increasingly leveraging machine learning (ML), a subset of AI, to predict patient outcomes and guide surgical decision-making. This review evaluates the relative performance of ML prediction models in breast reconstruction. METHODS: A systematic review was conducted utilizing PubMed, Scopus, and EMBASE according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Studies using ML to predict patient outcomes in breast reconstruction were included. The type of ML model and the specific outcome measures were reported. Performance of the models was reported as area under the receiver operating characteristic curve and compared using descriptive statistics, multivariate linear regression, and random-effects meta-regression in RStudio. RESULTS: Our search yielded 1025 citations, of which 24 were assessed for eligibility. Fourteen studies met the inclusion criteria and were sought for data extraction. There were 19 ML models and 11,013 patients assessed across 92 testing conditions. Models were trained on varying patient demographics, comorbidities, and operative characteristics, whereas outcomes assessed included various surgical complications or patient satisfaction using BREAST-Q. The median area under the receiver operating characteristic curve of all models was 0.71 (interquartile range = 0.16). When adjusting for the number of patients, number of predictors, ML model category, and outcome of interest, models predicting BREAST-Q performed higher with skin necrosis used as the reference outcome (β = 0.13, P < 0.01). After adjusting for number of patients and predictors, models that employed strategies to mitigate class imbalance were associated with higher model discrimination (β = 0.038; 95% CI, 0.002-0.075; P = 0.041). CONCLUSION: Machine learning applications for risk prediction and surgical planning are growing rapidly. The models evaluated in this review demonstrated the ability to predict a variety of outcomes, with models predicting BREAST-Q, various surgical outcomes, and those reporting class imbalance methods leading to higher model discrimination. Notably, covariate adjustment and study heterogeneity may have impacted these associations. As ML models are increasingly integrated into plastic surgery practice, standardized reporting practices are essential to promote reproducibility and cross-study comparison.

References

1. 1.Rosenbloom, A., Gasbeck, T., Mamoun, L., Shah, N., Nanda, A., & Lee, G. (2026). Predicting surgical outcomes in breast reconstruction with machine learning: A systematic review. Annals of Plastic Surgery. https://doi.org/10.1097/SAP.0000000000004667