ArticleTranslational cancer research2022
A simple-to-use nomogram for predicting the risk of radiation pneumonitis in patients with thoracic segment esophageal squamous cell carcinoma.
Article in Translational cancer research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. 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, 0 citations in OpenAlex.
No citing paper in PubMed yet.
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 at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Radiation pneumonitis (RP) is one of the most severe complications of radiotherapy (RT) or concurrent chemoradiotherapy for thoracic segment esophageal squamous cell carcinoma (TSESCC) with delayed diagnose by conventional computed tomography (CT). The study aimed to develop a nomogram to predict the risk of RP early. Methods: Data was collected from 174 patients with clinicopathologically confirmed TSESCC from October 2013 to June 2020. Procalcitonin (PCT), C-reactive protein (CRP), and interleukin-6 (IL-6) levels in serum were dynamically monitored during radiotherapy. Lasso analysis was used for feature screening before multivariate logistic regression analysis to reduce the multicollinearity of variables. A nomogram combined with biological factors and clinical signs for individualized risk assessment and precise prediction of RP was developed and assed the performance with respect to its calibration, discrimination. Results: Of the 174 patients, 30 patients developed RP (grade ≥2) while 144 patients did not. After variable screening by Lasso analysis and logistics multivariate regression analysis, the predictor variables that were finally retained in the nomogram prediction model included IL-6, CRP, and radiotherapy techniques. The model displayed good discrimination with an area under the curve (AUC) of 0.898 (95% CI: 0.849-0.947), with the sensitivity and specificity of 0.967 and of 0.736, respectively. This model also shows good calibration and clinical practical value. In addition, the study provided a web-based version of the dynamic nomogram to facilitate clinical application. Conclusions: The study provides a nomogram model containing IL-6, CRP, RT techniques, which could be conveniently used for individualized prediction of RP in patients with TSESCC during radiotherapy or concurrent chemoradiotherapy.
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.