ArticleBreast (Edinburgh, Scotland)2021
Machine learning to predict individual patient-reported outcomes at 2-year follow-up for women undergoing cancer-related mastectomy and breast reconstruction (INSPiRED-001).
Article in Breast (Edinburgh, Scotland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Exploring the role of health-related quality of life measures in predictive modelling for oncology: a systematic review.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2025Pooled it
- The usefulness of artificial intelligence in breast reconstruction: a systematic review.Breast cancer (Tokyo, Japan) · 2024Pooled it
- Artificial Intelligence-predicted Outcomes of Breast Reconstruction: Progress, Pitfalls, and Path Forward.Plastic and reconstructive surgery. Global open · 2026Article
- Time-Dynamic AI Models to Predict Quality of Life in Patients With Breast Cancer: Development and Validation Study Using the EORTC BALANCE Cohort.Journal of medical Internet research · 2026Article
- Applications of Artificial Intelligence (AI) in Breast Cancer Care Delivery and Education: A Scoping Review.International journal of environmental research and public health · 2026Article
- Machine Learning Model to Predict Postmastectomy Breast Reconstruction Complications.JAMA network open · 2026Article
- Artificial Intelligence in Breast Reconstruction: Enhancing Surgical Planning, Aesthetic Outcomes, and Patient-Centered Care.Journal of clinical medicine · 2025Review
- A narrative review of the use of PROMs and machine learning to impact value-based clinical decision-making.BMC medical informatics and decision making · 2025Review
- Using artificial intelligence to predict patient outcomes from patient-reported outcome measures: a scoping review.Health and quality of life outcomes · 2025Article
- Development of machine learning models to predict cancer-related fatigue in Dutch breast cancer survivors up to 15 years after diagnosis.Journal of cancer survivorship : research and practice · 2025Article
- An Oversampling-Enhanced Multi-Class Imbalanced Classification Framework for Patient Health Status Prediction Using Patient-Reported Outcomes.IEEE access : practical innovations, open solutions · 2025Article
- Using Machine Learning to Predict Unplanned Hospital Utilization and Chemotherapy Management From Patient-Reported Outcome Measures.JCO clinical cancer informatics · 2024Article
- A contemporary review of breast cancer risk factors and the role of artificial intelligence.Frontiers in oncology · 2024Review
- Predicting patient reported outcome measures: a scoping review for the artificial intelligence-guided patient preference predictor.Frontiers in artificial intelligence · 2024Article
- Machine-Learning Prediction of Capsular Contraction after Two-Stage Breast Reconstruction.JPRAS open · 2023Article
- Enhanced Surgical Decision-Making Tools in Breast Cancer: Predicting 2-Year Postoperative Physical, Sexual, and Psychosocial Well-Being following Mastectomy and Breast Reconstruction (INSPiRED 004).Annals of surgical oncology · 2023Article
- Machine learning models for 180-day mortality prediction of patients with advanced cancer using patient-reported symptom data.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2023Article
- The underused potential of breast conserving therapy after neoadjuvant system treatment - Causes and solutions.Breast (Edinburgh, Scotland) · 2023Review
- Predicting Patient-Reported Outcomes Following Surgery Using Machine Learning.The American surgeon · 2023Review
- Radiomic model based on magnetic resonance imaging for predicting pathological complete response after neoadjuvant chemotherapy in breast cancer patients.Frontiers in oncology · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundWomen 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.
methodsWe 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.
resultsOf 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.
conclusionLong-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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.