Towards patient-centered decision-making in breast cancer surgery: Machine learning to predict individual patient-reported outcomes at 1-year follow-up Journal Article
| Authors: | Pfob, A.; Mehrara, B. J.; Nelson, J. A.; Wilkins, E. G.; Pusic, A. L.; Sidey-Gibbons, C. |
| Article Title: | Towards patient-centered decision-making in breast cancer surgery: Machine learning to predict individual patient-reported outcomes at 1-year follow-up |
| Abstract: | 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. © 2023 LWW. All rights reserved. |
| Keywords: | adult; controlled study; cancer surgery; major clinical study; clinical feature; follow up; quality of life; breast cancer; mastectomy; breast reconstruction; cancer therapy; prediction; patient care; clinical decision making; personalized medicine; shared decision making; shared decision-making; elastic tissue; patient-reported outcome; machine learning; learning algorithm; plastic surgeon; breast cancer surgery; breast-q; human; female; article; individualized treatment |
| Journal Title: | Annals of Surgery |
| Volume: | 277 |
| Issue: | 1 |
| ISSN: | 0003-4932 |
| Publisher: | Lippincott Williams & Wilkins |
| Date Published: | 2023-01-01 |
| Start Page: | e144 |
| End Page: | e152 |
| Language: | English |
| DOI: | 10.1097/sla.0000000000004862 |
| PUBMED: | 33914464 |
| PROVIDER: | scopus |
| PMCID: | PMC9762704 |
| DOI/URL: | |
| Notes: | The MSK Cancer Center Support Grant (P30 CA008748) is acknowledged in the PDF -- Source: Scopus |
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