TY - JOUR
T1 - Towards Patient-centered Decision-making in Breast Cancer Surgery
T2 - Machine Learning to Predict Individual Patient-reported Outcomes at 1-year Follow-up
AU - Pfob, André
AU - Mehrara, Babak J.
AU - Nelson, Jonas A.
AU - Wilkins, Edwin G.
AU - Pusic, Andrea L.
AU - Sidey-Gibbons, Chris
N1 - Publisher Copyright:
© 2023 LWW. All rights reserved.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - 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 of 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 suboptimal treatment recommendations for individuals. Methods: We 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 sitea'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. Results: The 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). 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.
AB - 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 of 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 suboptimal treatment recommendations for individuals. Methods: We 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 sitea'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. Results: The 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). 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.
KW - breast cancer surgery
KW - breast reconstruction
KW - individualized treatment
KW - machine learning
KW - shared decision-making
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U2 - 10.1097/SLA.0000000000004862
DO - 10.1097/SLA.0000000000004862
M3 - Article
C2 - 33914464
AN - SCOPUS:85145278668
SN - 0003-4932
VL - 277
SP - E144-E152
JO - Annals of surgery
JF - Annals of surgery
IS - 1
ER -