ArticleJPRAS open2023
Machine-Learning Prediction of Capsular Contraction after Two-Stage Breast Reconstruction.
Article in JPRAS open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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
11 citing papers in PubMed, 10 citations in OpenAlex.
- Association is Not Prediction: Reframing Claims-Based Machine Learning for Capsular Contracture.Aesthetic plastic surgery · 2026Article
- Artificial Intelligence-predicted Outcomes of Breast Reconstruction: Progress, Pitfalls, and Path Forward.Plastic and reconstructive surgery. Global open · 2026Article
- Does Patient History Influence Capsular Contracture? An Exploratory Analysis with Machine Learning.Aesthetic plastic surgery · 2026Article
- Artificial Intelligence in Breast Reconstruction: A Scoping Review of Pre-, Intra-, and Postoperative Applications.Plastic and reconstructive surgery. Global open · 2026Article
- Machine Learning Model to Predict Postmastectomy Breast Reconstruction Complications.JAMA network open · 2026Article
- Risk Factors and Incidence of Reoperation in Direct-to-Implant Breast Reconstruction Surgery Among Patients with Breast Cancer.Annals of surgical oncology · 2026Article
- Using Machine Learning as a Seroma Risk Assessment Tool in Prepectoral Breast Reconstruction.Plastic and reconstructive surgery. Global open · 2026Article
- Artificial Intelligence in Breast Reconstruction: Enhancing Surgical Planning, Aesthetic Outcomes, and Patient-Centered Care.Journal of clinical medicine · 2025Review
- Predicting Breast Reconstruction Readmission, Reoperation, and Prolonged Length of Stay: A Machine Learning Approach.Journal of surgical oncology · 2025Article
- Artificial Intelligence in Breast Reconstruction: A Narrative Review.Medicina (Kaunas, Lithuania) · 2025Review
- Implementing Machine Learning Models for Prediction of Gender-Affirming Mastectomy Complications: Estimating Performance and Accuracy.Aesthetic surgery journal. Open forum · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Two-stage breast reconstruction is a common technique used to restore preoperative appearance in patients undergoing mastectomy. However, capsular contracture may develop and lead to implant failure and significant morbidity. The objective of this study is to build a machine-learning model that can determine the risk of developing contracture formation after two-stage breast reconstruction. Methods: A total of 209 women (406 samples) were included in the study cohort. Patient characteristics that were readily accessible at the preoperative visit and details pertaining to the surgical approach were used as input data for the machine-learning model. Supervised learning models were assessed using 5-fold cross validation. A neural network model is also evaluated using a 0.8/0.1/0.1 train/validate/test split. Results: Among the subjects, 144 (35.47%) developed capsular contracture. Older age, smaller nipple-inframammary fold distance, retropectoral implant placement, synthetic mesh usage, and postoperative radiation increased the odds of capsular contracture ( Conclusion: To our knowledge, this is the first study that uses a neural network to predict the development of capsular contraction after two-stage implant-based reconstruction. At the preoperative visit, surgeons may counsel high-risk patients on the potential need for further revisions or guide them toward autologous reconstruction.
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Registered trials
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.