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Women undergoing cancer-related mastectomy and reconstruction are facing multiple treatment choices where post-surgical satisfaction with breasts is a key outcome. We developed and validated machine learning algorithms to predict patient-reported satisfaction with breasts at 2-year follow-up to better inform the decision-making process for women with breast cancer. We trained, tested, and validated three machine learning algorithms (logistic regression (LR) with elastic net penalty, Extreme Gradient Boosting (XGBoost) tree, and neural network) to predict clinically important differences in satisfaction with breasts at 2-year follow-up using the validated BREAST-Q. We used data from 1553 women undergoing cancer-related mastectomy and reconstruction who were followed-up for two years at eleven study sites in North America from 2011 to 2016. 10-fold cross-validation was used to train and test the algorithms on data from 10 of the 11 sites which were further validated using the additional site's data. Area-under-the-receiver-operating-characteristics-curve (AUC) was the primary outcome measure. Of 1553 women, 702 (45.2%) experienced an improved satisfaction with breasts and 422 (27.2%) a decreased satisfaction. In the validation set (n = 221), the algorithms showed equally high performance to predict improved or decreased satisfaction with breasts (all P > 0.05): For improved satisfaction AUCs were 0.86–0.87 and for decreased satisfaction AUCs were 0.84–0.85. Long-term, individual patient-reported outcomes for women undergoing mastectomy and breast reconstruction can be accurately predicted using machine learning algorithms. Our algorithms may be used to better inform clinical treatment decisions for these patients by providing accurate estimates of expected quality of life. • Optimal treatment for women undergoing mastectomy and reconstruction is unclear. • Can machine learning algorithms predict long-term patient-reported outcomes? • An international cohort of 1553 women was used for the algorithm development. • External validation showed good performance with AUCs of 0.84–0.87 • accurate estimates of expected quality of life may help inform treatment decisions. [ABSTRACT FROM AUTHOR]

This article discusses a study which identified predictors of patient satisfaction with breast appearance, including implant type, in women who underwent breast reconstruction surgery using implants. The study involved 672 women from North America who had completed postmastectomy, implant-based reconstruction. It concluded that patients who received silicone breast implants reported significantly higher satisfaction with the results of reconstruction than those who received silicone breast implants.