ArticleAnnals of medicine2026
Clinical and nutritional-inflammatory biomarkers-based nomogram predicts survival in recurrent or metastatic cervical cancer treated with immune checkpoint inhibitors.
Article in Annals of medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Low-dose radiotherapy combined with immune checkpoint inhibitors in advanced malignancies: a real-world exploratory study.Translational cancer research · 2026Article
- Development and validation of prognostic nomograms for patients with cervical cancer and liver metastasis: a SEER-based study.Translational cancer research · 2026Article
- Unexplained multiorgan fat embolism syndrome in a 10-year-old child with LAMA2-related congenital muscular dystrophy.Forensic science, medicine, and pathology · 2026Article
- A Synthetic Lethality-Informed Multi-Omic Framework for Identifying a Five-Gene Diagnostic Signature in Chronic Obstructive Pulmonary Disease.Current issues in molecular biology · 2026Article
- Inflammatory biomarker-based prognostic model for immunotherapy outcomes in patients with recurrent or metastatic cervical cancer.Frontiers in oncology · 2026Article
- Causal AI for cancer immunotherapy: a narrative framework review of target trial emulation, treatment-effect learning and clinical translation.Frontiers in immunology · 2026Review
- Development and external validation of a LASSO-based parsimonious nomogram for predicting BPPV recurrence: a multi-center retrospective cohort study.Frontiers in neurologyArticle
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundImmunotherapy offers potential benefits for recurrent or metastatic cervical cancer (R/M CC), yet personalized predictive tools are essential for optimizing treatment. This study aims to develop a nomogram integrating nutritional-inflammatory biomarkers and clinical features to predict survival in R/M CC patients undergoing immune checkpoint inhibitor (ICI) therapy. PATIENTS AND
methodsWe retrospectively analyzed 98 R/M CC patients treated with ICIs. Overall survival (OS) was the primary endpoint. Demographic characteristics and peripheral blood biomarkers before and after ICI treatment were collected. Univariate analysis screened potential variables, followed by LASSO regression to select key biomarkers and compute the Risk-Score. Prediction models combining clinical features and the Risk-Score were evaluated using ROC curves and decision curve analysis (DCA). The optimal nomogram model was developed and validated with ROC curves, calibration plots, and DCA.
resultsThree models were established: (i) a clinical model based on age and squamous cell carcinoma antigen (SCC-Ag), (ii) a Risk-Score model, and (iii) a combined model integrating age, SCC-Ag, and Risk-Score. The combined model showed superior predictive performance. A nomogram incorporating age, stage, SCC-Ag, and Risk-Score predicted 6-month, 1-year, and 2-year survival probability, with respective AUC values of 0.892, 0.868, and 0.846. Calibration curves and DCA affirmed high predictive accuracy and clinical utility.
conclusionThe nomogram integrating nutritional-inflammatory biomarkers and clinical parameters may serve as a promising tool for predicting survival in R/M CC patients undergoing ICI therapyand may help guide individualized treatment strategies following further validation.
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