• André Pfob, Babak J Mehrara, Jonas A Nelson, Edwin G Wilkins, Andrea L Pusic, and Chris Sidey-Gibbons.
• Patient-Reported Outcomes, Value & Experience (PROVE) Center, Harvard Medical School & Brigham and Women's Hospital, Boston, Massachusetts.
• Ann. Surg. 2023 Jan 1; 277 (1): e144e152e144-e152.

ObjectiveWe 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 Of Background DataSatisfaction 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 suboptimal treatment recommendations for individuals.MethodsWe trained, tested, and validated 3 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 before surgery and at 1-year follow-up. Data from 10 of the 11 sites were randomly split into training and test samples (2:1 ratio) to develop and test 3 algorithms (logistic regression with elastic net penalty, extreme gradient boosting tree, and neural network) which were further validated using the additional site's data.AUC to predict clinically-significant changes in satisfaction with breasts at 1-year follow-up using the validated BREAST-Q were the outcome measures.ResultsThe 3 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).ConclusionIndividual 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.Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc.

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