ArticleFrontiers in oncology2025
A nomogram model integrating radiomics and clinical variables to predict napsin a expression in lung adenocarcinoma patients.
Article in Frontiers in oncology, 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Lung adenocarcinoma, a major subtype of non-small cell lung cancer, requires non-invasive diagnostic tools to improve early detection and differentiate primary from metastatic tumors. Napsin A, a key marker for primary lung adenocarcinoma, is traditionally assessed via invasive biopsy, limiting its utility in reflecting tumor heterogeneity. Radiomics, which extracts quantitative features from medical images, offers potential for non-invasive prediction of molecular markers like Napsin A. Objectives: To develop and validate a nomogram integrating radiomic features and clinical variables for non-invasive prediction of Napsin A expression in lung adenocarcinoma. Methods: This retrospective study enrolled 308 lung adenocarcinoma patients (training cohort: n = 246; validation cohort: n = 62), with contrast-enhanced CT images were used to extract 1,734 radiomic features, which underwent dimensionality reduction via t-tests, Pearson correlation, minimum redundancy maximum relevance (mRMR), and LASSO regression, retaining 27 final features; significant clinical variables (gender, smoking history, pulmonary cavity, spiculation sign, pleural indentation sign) were selected by logistic regression. A nomogram integrating radiomic and clinical predictors was developed and evaluated using ROC curves (AUC for Napsin A prediction), calibration curves (Hosmer-Lemeshow test), and decision curve analysis (DCA) for clinical utility. Results: The integrated nomogram model outperformed standalone radiomic and clinical models in predicting Napsin A expression, achieving AUC values of 0.844 (95% CI: 0.790-0.898) in the training cohort (n = 246) and 0.845 (95% CI: 0.724-0.967) in the validation cohort (n = 62), with balanced accuracy of 82.1% and 80.6%, respectively. Calibration curves showed strong agreement between predicted and observed outcomes (Hosmer-Lemeshow P > 0.05), and decision curve analysis confirmed its superior clinical utility across diverse threshold probabilities. Conclusion: The integrated nomogram offers a reliable non-invasive method for predicting Napsin A expression in lung adenocarcinoma, supporting personalized treatment decisions and reducing reliance on invasive biopsies.
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.