ArticleEJNMMI research2021
Predicting the nature of pleural effusion in patients with lung adenocarcinoma based on
Article in EJNMMI research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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
2 citing papers in PubMed.
- Lymph nodes rather than pleural metabolic activity inTranslational lung cancer research · 2024Article
- Differentiating malignant and benign pleural effusion in patients with lung cancer: anFrontiers in oncology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundThis study aims to establish a predictive model on the basis of
methodsLung adenocarcinoma patients with PE who underwent
resultsA total of 413 lung adenocarcinoma patients with PE were enrolled in this study, including 245 patients with MPE and 168 patients with benign PE (BPE). The patients were divided into training (289 patients) and test (124 patients) cohorts. CEA, SUVmax of tumor and attachment to the pleura, obstructive atelectasis or pneumonia, SUVmax of pleura, and SUVmax of PE were identified as independent significant factors of MPE and were used to construct a predictive model, which was graphically represented as a nomogram. This predictive model showed good discrimination with the area under the curve (AUC) of 0.970 (95% CI 0.954-0.986) and good calibration. Application of the nomogram in the test cohort still gave good discrimination with AUC of 0.979 (95% CI 0.961-0.998) and good calibration. Decision curve analysis demonstrated that this nomogram was clinically useful.
conclusionsOur predictive model based on
Indexed as
Identifiers
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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.