ArticleBMC medical imaging2025
CT based quantification of intratumoral and peritumoral heterogeneity for diagnosing lymphovascular invasion for early stage non-small cell lung cancer.
Article in BMC medical imaging, 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.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo establish a model integrates clinical, traditional radiologic, intratumoral and peritumoral radiomics (ITR and PTR), and intratumoral and peritumoral heterogeneity (ITH and PTH) features to diagnose lymphovascular invasion (LVI) status for early stage non small cell lung cancer (NSCLC). MATERIALS AND
methodsClinical data and chest CT imaging data of NSCLC patients who underwent surgical resection of the lungs from January 2019 to May 2021 were collected. Surgical pathology were the diagnostic gold standard to clarify the LVI status. ITR and PTR features and ITH and PTH features from the total tumor volume and peritumoral tumor volume were extracted. Then clinical, traditional radiologic, ITR and PTR, ITH and PTH models were established to diagnose LVI status. Finally, a column chart diagnostic model was constructed and the diagnostic efficacy was evaluated.
results366 NSCLC patients were enrolled in this retrospective study from 2 institutions, in which Institution 1 served as the basis for training (n = 154) and internal validation (n = 154) sets, while Institution 2 served as the external validation set (n = 58). In the three cohorts of PTR_(0–3, -3–3 and 0–6), the PTR_0–6 model has better predictive performance, with area under the curve (AUC) of 0.882 and 0.824 for the training and validation groups, respectively. Gender, Vascular Convergence Sign, and N stage were significantly related to LVI status, Finally, the combined model integrated ITH, PTR_0–6, and PTH_0–6 models, N stage and Vascular Convergence Sign has the highest diagnostic accuracy. The AUCs for training set, internal validation set, and external validation set were 0.963, 0.882, and 0.743, respectively.
conclusionsA comprehensive diagnostic model based on clinical features, traditional radiological features, radiomic features, and heterogeneity features of NSCLC were established to diagnose LVI for early stage NSCLC, which has the highest diagnostic efficiency and can help to guide treatment decisions.
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.