Evidence map›Paper›PMID 42483845›Full record

ArticleJournal of surgical oncology2026

AI Risk Prediction Tools for Autologous Breast Reconstruction.

Jonlin Chen, Ariel Gabay, Abbas M Hassan, Francis D Graziano, Lillian A Boe, Arielle N Roberts, Ronnie L Shammas, Carrie S Stern, Robert J Allen, Babak J Mehrara and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of surgical oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Jonlin ChenPlastic and Reconstructive Surgery Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
Ariel GabayPlastic and Reconstructive Surgery Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York, USA.ORCID https://orcid.org/0009-0004-4965-9831
Abbas M HassanDivision of Plastic Surgery, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Francis D GrazianoPlastic and Reconstructive Surgery Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
Lillian A BoeDepartment of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
Arielle N RobertsPlastic and Reconstructive Surgery Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
Ronnie L ShammasPlastic and Reconstructive Surgery Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
Carrie S SternPlastic and Reconstructive Surgery Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
Robert J AllenPlastic and Reconstructive Surgery Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
Babak J MehraraPlastic and Reconstructive Surgery Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
Jonas A NelsonPlastic and Reconstructive Surgery Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAutologous breast reconstruction offers patients a durable and natural-appearing option after mastectomy. However, complication risks include flap loss, infection, and delayed wound healing. This study developed both traditional statistical and machine learning (ML) models to predict the risk of developing a 90-day postoperative complication after autologous reconstruction.

methodsPatient data were retrospectively collected for patients who underwent abdominal-based autologous breast reconstruction at Memorial Sloan Kettering Cancer Center (January 2015-September 2024). Multivariable logistic regression models and supervised ML models were developed to predict infection, hematoma, seroma, delayed wound healing, and flap compromise.

results2,128 patients (3,249 flap reconstructions) were included. Overall, 90-day complications occurred in 475 (22.3%) patients, including infection (10%), hematoma (6%), delayed healing (4.1%), seroma (3.9%), and flap compromise (2.8%). AUCs ranged from 0.60 to 0.66 (logistic regression) and 0.64 to 0.73 (ML). Higher BMI was associated with increased risk for seroma (OR: 1.1, 95% CI: 1.04-1.13) and neoadjuvant chemotherapy for infection (OR:1.72, 95% CI: 1.17-2.51). Key ML model predictors on SHAP analysis included age, BMI, and pre-reconstruction radiation.

conclusionIndividualized risk prediction models for complications after autologous breast reconstruction were developed using traditional statistics and ML. These models may help personalize treatment; however, further research is needed to enhance model predictive performance and clinical utility.

Indexed as

Artificial IntelligenceBreast NeoplasmsMachine LearningMammaplastyPostoperative ComplicationsAdultAgedFemaleHumansMastectomyMiddle AgedPrediction AlgorithmsPredictive Learning ModelsRetrospective StudiesRisk AssessmentRisk Factorsautologous reconstructionbreast reconstructioncomplications predictionsmachine learningrisk tool

Identifiers

PMID42483845
PMCPMC13575592

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.