by André Pfob, Babak J. Mehrara, Jonas A. Nelson, Edwin G.
Wilkins, Andrea L. Pusic, Chris Sidey-Gibbons
The Breast: VOLUME 60, P111-122, DECEMBER
01, 2021
Background
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.
Methods
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.
Results
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.
Conclusion
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.
