ArticleBMC cancer2025
A CT-based radiomics nomogram incorporating adipose tissue to differentiate invasive adenocarcinomas among part-solid pulmonary nodules.
Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- A radiomics-based nomogram for preoperatively predicting the invasiveness of nodular lung adenocarcinoma: a multicenter study.Translational lung cancer research · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
backgroundAccurately assessing the invasiveness of lung adenocarcinoma with part-solid pulmonary nodules is pivotal for the treatment. Recent studies have highlighted the crucial role of adipose in the growth and invasion of tumors. This study aimed to develop and validate a model by integrating radiomic features from intrathoracic adipose tissue (IAT) with clinical factors to classify lung adenocarcinoma invasiveness.
methods608 patients with lung adenocarcinomas were enrolled in three different centers. Radiomic features were extracted from nodules and IAT in computed tomography images. Multivariable logistic regression analysis was used to develop a nomogram model. The nomogram's performance was assessed utilizing accuracy, discrimination, and clinical benefits. The net reclassification index (NRI) and integrated discrimination improvement (IDI) were applied to evaluate changes in model performance after the addition of IAT features.
resultsThe nomogram, incorporating nodule and IAT signature with nodular diameter, was constructed in the training dataset. The nomogram demonstrated area under the receiver operating curve values of 0.927 (95% CI: 0.872,0.983), 0.945 (95% CI: 0.902,0.988), and 0.917 (95% CI: 0.871,0.962) in the internal validation and two external validation cohorts, respectively. The nomogram showed good calibration (Hosmer-Lemeshow test, P>0.05), and adding the IAT signatures did improve nomogram performance in all cohorts (all NRI and IDI >0, P<0.05). Decision curve and stratification analysis revealed that the nomogram was clinically useful and had potential generalization ability.
conclusionsThe nomogram, consisting of nodule signature, IAT signature, and clinical factors, could individualize the classification of invasive adenocarcinomas with part-solid pulmonary nodules.
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.