ArticleJournal of clinical laboratory analysis2026
Integrating Clinical Features, Laboratory Biomarkers and Computed Tomography for the Discrimination of Non-Small Cell Lung Cancer and Benign Pulmonary Diseases: A Clinical Prediction Model.
Article in Journal of clinical laboratory analysis, 2026. 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
6 authors.
Funding
Abstract
objectiveTo develop and validate a clinical prediction model for differentiating non-small cell lung cancer (NSCLC) from benign pulmonary diseases (BPD).
methodsThe retrospective study included 226 participants in the training set (134 with NSCLC and 92 with BPD) and 98 participants in the validation set (62 with NSCLC and 36 with BPD). A logistic regression model was constructed using variables such as sex, hemoptysis, serum biomarkers (carcinoembryonic antigen [CEA] and total protein [TP]), and CT features (volume ratio of solid density region [VR_2A], volume of calcified density area [VR_3], total volume [TV], CT variance [CTV], and maximum surface area [MSA]). The model was validated on an independent cohort, and its performance was evaluated using area under the curve (AUC), accuracy, calibration curves, and decision curves. Additionally, a nomogram was developed for clinical application, and its acceptance and convenience among clinicians were assessed.
resultsThe prediction model achieved an AUC-ROC of 0.95 in the training set and 0.82 in the validation set. Calibration and decision curves demonstrated that the model had reliable diagnostic performance and good clinical application value.
conclusionThe integrated prediction model combining clinical features, laboratory biomarkers, and CT features shows promise for improving the accuracy of differentiating NSCLC from BPD. Further studies are warranted to explore its potential in clinical practice.
Indexed as
Identifiers
What Socratic 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 Socratic graph.