ArticleGland surgery2026
Interpretable machine learning for survival prediction and risk stratification in elderly patients with breast cancer after breast-conserving surgery.
Article in Gland surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The necessity of adjuvant radiotherapy following breast-conserving surgery (BCS) in elderly patients with early-stage breast cancer remains controversial. Existing studies focus predominantly on population-level benefits without identifying specific prognostic subgroups with different baseline survival probabilities. We aimed to develop interpretable machine learning models to predict survival and establish precise prognostic risk stratification that could inform individualised treatment discussions. Methods: Using the Surveillance, Epidemiology, and End Results database (2016-2022), we included patients aged ≥70 years with T1-2N0M0, oestrogen receptor-positive, human epidermal growth factor receptor 2 (HER2) negative breast cancer who underwent BCS. We developed six machine learning survival models incorporating age, tumour grade, T stage, progesterone receptor status, race, histology, and chemotherapy. Model performance was evaluated using time-dependent area under the curve (AUC) and concordance index. The optimal model was interpreted using SHapley Additive exPlanations (SHAP) framework. Patients were stratified into three risk groups, with survival differences assessed using Kaplan-Meier analysis. Results: A total of 39,872 patients were included (training set: 31,897; test set: 7,975). The eXtreme Gradient Boosting (XGBoost) model demonstrated optimal performance with 1-, 3-, and 5-year AUCs of 0.714, 0.692, and 0.711, respectively. SHAP analysis identified age as the most important predictor, followed by tumour grade and T stage. Risk stratification successfully delineated three distinct prognostic groups: low-risk (37% of patients, 5-year overall survival 88-90%), intermediate-risk (33% of patients, 5-year overall survival 82-84%), and high-risk (30% of patients, 5-year overall survival 65-67%) (log-rank P<0.001). Notably, the low-risk group's survival rate was comparable to radiotherapy-treated patients in previous studies (88.6%). Conclusions: We successfully established a prognostic risk stratification system identifying three distinct survival groups (low-risk, intermediate-risk, and high-risk). The low-risk group's 5-year survival matched radiotherapy-treated patients in a previous study (Yang
Indexed as
Identifiers
What OpenQuestion holds
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.