Serum Interleukin Levels Predict Occurrence of Acute Radiation Pneumonitis and Overall Survival in Thoracic Tumours
Bibliographic record
Abstract
BACKGROUND: Radiation-induced lung injury (RILI) is a significant adverse effect of thoracic radiotherapy, potentially impacting patient prognosis. The risk factors for acute radiation pneumonitis (RP) have not been fully clarified. The present study evaluated the predictive value of serum interleukins (ILs) in the occurrence of RP and overall survival in patients with thoracic cancers. METHODS: This single-centre retrospective observational study enrolled 435 thoracic cancer patients who underwent chest radiation therapy. Serum levels of IL-1β, IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12p70, IL-17, TNF-α, IFN-γ, IFN-α were measured by cytometric bead array before radiotherapy. The relationship between clinical characteristics, serum IL levels and the occurrence of RP were analyzed. Cox regression and Kaplan-Meier methods were also performed to investigate the prognostic role of serum IL levels in these patients. RESULTS: The incidence of RP in these patients was 17.01%. Elevated serum levels of IL-1β, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12p70, TNF-α, IFN-α were all associated with the occurrence of RP. High levels of IL-1β, IL-4, and IL-12p70 were correlated with more severe pneumonitis. Univariate and multivariate logistic regression analysis identified serum IL-6 level as an independent prognostic factor in patients receiving thoracic radiotherapy. CONCLUSIONS: Serum interleukin levels are linked to the development of acute RP in patients receiving thoracic radiotherapy. Serum IL-6 could serve as a valuable biomarker in identifying patients at high risk for RP, potentially guiding individualized therapeutic strategies and improving patient management in radiotherapy. Future research should focus on validating IL-6's role in larger cohorts and exploring its integration into clinical practice for the early prediction of RILI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".