ArticleOncology letters2024
Predictive model for acute radiation esophagitis in esophageal carcinoma based on prognostic nutritional index and systemic inflammatory index and its application.
Article in Oncology letters, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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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.
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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.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Decision tree-based machine learning algorithm for prediction of acute radiation esophagitis.Biochemistry and biophysics reports · 2025Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Acute radiation esophagitis (ARE) is a common complication in patients with esophageal cancer undergoing radiotherapy. Therefore, it is important to construct an effective ARE risk-prediction model for clinical treatment. The present study performed a retrospective analysis of 225 patients with esophageal cancer who received radiotherapy at the First Affiliated Hospital of Anhui Medical University (Hefei, China) from January 2018 to December 2022. Univariate and logistic regression analyses were performed to screen patients with esophageal cancer after radiotherapy. The results revealed that 147 patients developed radiation esophagitis. Logistic regression analysis results demonstrated that the prognostic nutritional index [odds ratio (OR), 0.864; 95% confidence interval (CI), 0.809-0.924], neutrophil to lymphocyte ratio (OR, 1.795; 95% CI, 1.209-2.667) and platelet to lymphocyte ratio (OR, 1.011; 95% CI, 1.000-1.022) were independent predictors of ARE in patients receiving intensity-modulated conformal radiotherapy for esophagus cancer (P<0.05). A nomogram model for predicting the occurrence of ARE was established based on the three risk factors. The decision curve suggested a high net benefit value when the threshold probability was within 0.25-1.0. External verification confirmed the reproducibility and generalizability of the nomogram model. In general, the calibration curve of this model was close to the ideal curve and had excellent prediction accuracy. Therefore, it may be used as a new tool for early prediction of the ARE risk.
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