Towards Patient-Centered Decision-Making in Breast Cancer Surgery: Machine Learning to Predict Individual Patient-Reported Outcomes at 1-Year Follow-up

Objective We developed, tested, and validated machine learning algorithms to predict individual patient-reported outcomes at 1-year follow-up to facilitate individualized, patient-centered decision-making for women with breast cancer. Summary background data Satisfaction with breasts is a key outcome for women undergoing cancer-related mastectomy and reconstruction. Current decision-making relies on group-level evidence which may lead to sub-optimal treatment recommendations for individuals. Methods We trained, tested, and validated three machine learning algorithms using data from 1921 women undergoing cancer-related mastectomy and reconstruction conducted at eleven study sites in North America from 2011 to 2016. Data from 1921 women undergoing cancer-related mastectomy and reconstruction were collected prior to surgery and at 1-year follow-up. Data from 10 of the 11 sites was randomly split into training and test samples (2:1 ratio) to develop and test three algorithms (logistic regression with elastic net penalty, Extreme Gradient Boosting tree, and neural network) which were further validated using the additional site's data.Accuracy and area-under-the-receiver-operating-characteristics-curve (AUC) to predict clinically-significant changes in satisfaction with breasts at 1-year follow-up using the validated BREAST-Q were the outcome measures. Results The three algorithms performed equally well when predicting both improved or decreased satisfaction with breasts in both testing and validation datasets: For the testing dataset median accuracy= 0.81 (range 0.73-0.83), median AUC= 0.84 (range 0.78-0.85). For the validation dataset median accuracy= 0.83 (range 0.81-0.84), median AUC= 0.86 (range 0.83-0.89). Conclusion Individual patient-reported outcomes can be accurately predicted using machine learning algorithms, which may facilitate individualized, patient-centered decision-making for women undergoing breast cancer treatment.