ArticleBMC medicine2025
Development and external validation of a machine learning-based model to predict postoperative recurrence in patients with duodenal adenocarcinoma: a multicenter, retrospective cohort study.
Article in BMC medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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
10 citing papers in PubMed.
- Early prediction of bowel necrosis in incarcerated groin hernia using parsimonious machine learning models: a retrospective cohort study.Hernia : the journal of hernias and abdominal wall surgery · 2026Article
- Development and internal validation of a machine learning-based model to predict 1-year all-cause mortality in patients with gastrointestinal bleeding with type 2 diabetes.BMC medical informatics and decision making · 2026Article
- Machine learning-based predictive models and subtypes patterns in peripheral blood of schizophrenia based on a machine learning computational framework.Schizophrenia (Heidelberg, Germany) · 2026Article
- Resource-stratified machine learning framework for cognitive status classification and mild cognitive impairment to dementia progression prediction.Alzheimer's research & therapy · 2026Article
- Development and temporal validation of a machine learning-based model to predict postoperative recurrence and guide adjuvant radiotherapy in patients with soft tissue sarcoma: a retrospective cohort study.American journal of cancer research · 2026Article
- Machine learning in cancer imaging for enhanced precision in diagnosis and therapy.Discover computing · 2026Review
- Development and Validation of a Prognostic Model for Lung Cancer Based on Machine Learning and Immune Microenvironment Analysis.Journal of cellular and molecular medicine · 2025Article
- Article
- Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors.
Funding
Abstract
backgroundDuodenal adenocarcinoma (DA) has a high recurrence rate, making the prediction of recurrence after surgery critically important.
methodsOur objective is to develop a machine learning-based model to predict the postoperative recurrence of DA. We conducted a multicenter, retrospective cohort study in China. 1830 patients with DA who underwent radical surgery between 2012 and 2023 were included. Wrapper methods were used to select optimal predictors by ten machine learning learners. Subsequently, these ten learners were utilized for model development. The model's performance was validated using three separate cohorts, and assessed by the concordance index (C-index), time-dependent calibration curve, time-dependent receiver operating characteristic curves, and decision curve analysis.
resultsAfter selecting predictors, ten feature subsets were identified. And ten feature subsets were combined with the ten machine learning learners in a permutation, resulting in the development of 100 predictive models, and the Penalized Regression + Accelerated Oblique Random Survival Forest model (PAM) exhibited the best predictive performance. The C-index for PAM was 0.882 (95% CI 0.860-0.886) in the training cohort, 0.747 (95% CI 0.683-0.798) in the validation cohort 1, 0.736 (95% CI 0.649-0.792) in the validation cohort 2, and 0.734 (95% CI 0.674-0.791) in the validation cohort 3. A publicly accessible web tool was developed for the PAM.
conclusionsThe PAM has the potential to identify postoperative recurrence in DA patients. This can assist clinicians in assessing the severity of the disease, facilitating patient follow-up, and aiding in the formulation of adjuvant treatment strategies.
Indexed as
Identifiers
What Socratic 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 Socratic graph.