Risk of Developing Breast Reconstruction Complications: A Machine-Learning Nomogram for Individualized Risk Estimation with and without Postmastectomy Radiation Therapy

Background: The purpose of this study was to create a nomogram using machine learning models predicting risk of breast reconstruction complications with or without postmastectomy radiation therapy. Methods: Between 1997 and 2017, 1617 breast cancer patients undergoing mastectomy and breast reconstruction were analyzed. Those with autologous, tissueexpander/implant, and single-stage direct-to-implant reconstruction were included. Postmastectomy radiation therapy was delivered either with three-dimensionalconformal photon or proton therapy. Complication endpoints were defined based on surgical reintervention operative notes as infection/necrosis requiring debridement. For implant-based patients, complications were defined as capsular contracture requiring capsulotomy and implant failure. For each complication endpoint, least absolute shrinkage and selection operator penalizedregression was used to select the subset of predictors associated with the smallest prediction error from 10-fold cross-validation. Nomograms were built using the least absolute shrinkage and selection operator-selected predictors,and internal validation using cross-validation was performed. Results: Median follow-up was 6.6 years. Among 1617 patients, 23 percent under went autologous reconstruction, 39 percent underwent direct-to-implant reconstruction,and 37 percent underwent tissue expander/implant reconstruction.Among 759 patients who received postmastectomy radiation therapy, 8.3 percent received proton-therapy to the chest wall and nodes and 43 percent received chest wall boost. Internal validation for each model showed an area under the receiver operating characteristic curve of 73 percent for infection, 75 percent for capsular contracture, 76 percent for absolute implant failure, and 68 percent for overall implant failure. Periareolar incisions and complete implant muscle coverage were found to be important predictors for infection and capsular contracture,respectively. In a multivariable analysis, we found that protons compared to no postmastectomy radiation therapy significantly increased capsular contracture risk (OR, 15.3; p < 0.001). This was higher than the effect of photons with electron boost versus no postmastectomy radiation therapy (OR, 2.5; p = 0.01). Conclusion: Using machine learning, these nomograms provided prediction of postmastectomy breast reconstruction complications with and without radiation therapy.

Automated summary

A nomogram using machine learning models was created to predict the risk of breast reconstruction complications with or without postmastectomy radiation therapy. The study found that periareolar incisions and complete implant muscle coverage were important predictors for infection and capsular contracture, respectively.