ArticleAmerican journal of translational research2025
A clinical and CT-based model for differentiating high-grade from low-grade lung adenocarcinoma in patients with idiopathic pulmonary fibrosis.
Article in American journal of translational research, 2025. 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.
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo establish a clinical and CT-based diagnostic model to predict high-grade lung adenocarcinoma (LAC) in patients with idiopathic pulmonary fibrosis (IPF).
methodsA total of 289 LAC-IPF patients were enrolled retrospectively and were divided into training (n=171) and test sets (n=118). In each set, the patients were divided into a low-grade LAC group and high-grade LAC group according to pathologic findings. Clinical and high-resolution CT (HRCT) features were analyzed by binary logistic regression analysis to select independent predictors for high-grade LAC by building three models: the clinical model, the radiologic model, and the combined model integrating the independent clinical and radiologic factors. The discriminative performance of the three models was assessed using the receiver operating characteristic (ROC). The model with the best diagnostic performance was verified in the test set.
resultsThere was no significant difference between the training and test sets regarding clinical and radiologic factors (
conclusionsA clinical and CT-based model can be used as an effective tool to predict high-grade LAC in IPF patients.
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.