ArticleAmerican journal of cancer research2025
Development of a predictive model for recurrence in postoperative glottic laryngeal squamous cell carcinoma patients following adjuvant chemotherapy based on PNI, NLR, and PLR.
Article in American journal of cancer research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo identify key factors influencing postoperative recurrence in patients with glottic laryngeal squamous cell carcinoma (LSCC) and to develop a predictive model incorporating traditional clinicopathological features and novel inflammatory and immune indicators. This model aims to provide a theoretical foundation for individualized prediction of postoperative recurrence risk and support clinical decision-making.
methodsClinical and laboratory data were collected from 614 patients with glottic laryngeal cancer who underwent surgery between April 2010 and December 2021. The study included inflammatory and immune-related indicators (such as NLR, PLR, PNI, IL-6, IL-8), alongside traditional clinical features like age, T stage, lymph node metastasis, and degree of differentiation. Univariate and multivariate logistic regression, as well as Cox regression analyses, were performed to identify factors associated with recurrence. A Nomogram model was constructed based on Cox regression results. The model's predictive performance was evaluated using ROC curves, the concordance index (C-index), and calibration curves, with validation conducted in both training and validation cohorts.
resultsMultivariate analysis identified age, T stage, lymph node metastasis, degree of differentiation, IL-6, IL-8, PNI, and PLR as independent factors influencing postoperative recurrence in patients with glottic laryngeal cancer. The Nomogram model demonstrated excellent predictive performance in both the training and validation cohorts, with AUCs for 12-, 24-, and 36-month recurrence-free survival predictions of 0.887, 0.906, and 0.915 (training cohort) and 0.895, 0.906, and 0.907 (validation cohort), respectively. The model's concordance indices were 0.860 and 0.857 in the training and validation groups, respectively. Calibration curves revealed a high degree of agreement between predicted and actual outcomes.
conclusionThe Nomogram model developed in this study integrates multiple clinical and inflammatory-immune indicators, enabling accurate prediction of 12-, 24-, and 36-month recurrence-free survival rates in post-surgical patients with glottic laryngeal cancer. The model holds significant clinical value, with IL-6, IL-8, and PNI identified as crucial indicators for predicting recurrence risk, providing valuable insights for postoperative follow-up and individualized treatment strategies.
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.