ArticleTranslational oncology2026
Integration of inflammatory and nutritional biomarkers with machine learning enhances prediction of progesterone response in fertility-preserving endometrial carcinoma management.
Article in Translational oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. 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
- Erratum issued
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEndometrial carcinoma (EC) and atypical endometrial hyperplasia (AEH) increasingly affect young women, posing challenges for fertility preservation. The inflammatory and nutritional status have been shown to significantly influence disease outcomes, especially in cancer. However, no studies have systematically investigated the predictive value of inflammation and nutrition scores for complete response (CR) in EC.
methodsThis retrospective study included 329 EC/AEH patients treated at Peking University People's Hospital from January 2012 to December 2025. We developed a multimodal nomogram integrating 12 inflammatory (NLR, SIRI, PLR, et al.) and 5 nutritional biomarkers (mGNRI, PNI, NRI, ALI, CONUT) via LASSO regression and machine learning. Model validation employed leave-one-out cross-validation (LOOCV), with performance assessed by AUC, calibration curves, and decision curve analysis (DCA).
resultsThe combined inflammatory-nutritional score achieved superior predictive accuracy, with AUC values of 0.846 (training cohort) and 0.871 (validation cohort). Besides, our nomogram which was constructed by four clinical variables (BMI, menstrual history, metabolic syndrome, and histological type), inflammatory score and nutritional score exhibited excellent predictive potential, with AUC values of 0.915 (training cohort) and 0.933 (validation cohort), significantly outperforming clinical models. Risk stratification revealed significantly lower CR rates in high-risk patients (log-rank P < 0.001), with decision curve analysis demonstrating a 35% reduction in unnecessary interventions.
conclusionsIntegrating systemic inflammation and nutritional biomarkers enhances CR prediction in EC/AEH, enabling personalized risk stratification to guide fertility-sparing strategies. This tool addresses a critical clinical gap, though future multicenter studies are warranted to validate generalizability and explore mechanistic pathways.
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.