ArticleThoracic cancer2023
Quantification of diffuse parenchymal lung disease in non-small cell lung cancer patients with definitive concurrent chemoradiation therapy for predicting radiation pneumonitis.
Article in Thoracic cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers, 1 of them a synthesis that pooled it.
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, 1 synthesis or guideline pooled it, 3 citations in OpenAlex.
- Predictive value of machine learning for radiation pneumonitis and checkpoint inhibitor pneumonitis in lung cancer patients: a systematic review and meta-analysis.Scientific reports · 2025Pooled it
- Quantification of diffuse parenchymal lung disease in non-small cell lung cancer patients with definitive concurrent chemoradiation therapy for predicting radiation pneumonitis.Thoracic cancer · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundWe sought to quantify diffuse parenchymal lung disease (DPLD) extent using quantitative computed tomography (CT) analysis and to investigate its association with radiation pneumonitis (RP) development in non-small cell lung cancer (NSCLC) patients receiving definitive concurrent chemoradiation therapy (CCRT).
methodsA total of 82 NSCLC patients undergoing definitive CCRT were included in this prospective cohort study. Pretreatment CT scans were analyzed using quantitative CT analysis software. Low-attenuation area (LAA) features based on lung density and texture features reflecting interstitial lung disease (ILD) were extracted from the whole lung. Clinical and dosimetric factors were also evaluated. RP development was assessed using the Common Terminology Criteria for Adverse Events version 5.0. Univariable and multivariable logistic regression analyses were performed to identify independent risk factors for grade ≥3 (≥GR3) RP.
resultsRP was identified in 68 patients (73.9%), with nine patients (10.9%) experiencing ≥GR3 RP. Univariable logistic regression analysis identified excess kurtosis and high-attenuation area (HAA)_volume (cc) as significantly associated with ≥GR3 RP. Multivariable logistic regression analysis showed that the combined use of imaging features and clinical factors (forced expiratory volume in 1 second [FEV1], forced vital capacity [FVC], and CHEMO regimen) demonstrated the best performance (area under the receiver operating characteristic curve = 0.924) in predicting ≥GR3 RP.
conclusionQuantified imaging features of DPLD obtained from pretreatment CT scans would predict the occurrence of RP in NSCLC patients undergoing definitive CCRT. Combining imaging features with clinical factors could improve the accuracy of the predictive model for severe RP.
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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.