ArticleEuropean radiology2025
Predicting lymphovascular invasion in stage IA lung adenocarcinoma: a CT-based classification and regression tree model.
Article in European radiology, 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
8 authors.
Funding
Abstract
backgroundLymphovascular invasion (LVI) is a significant histopathological marker associated with poor prognosis in patients. However, there is a notable lack of reliable, non-invasive preoperative tools to predict LVI accurately. PURPOSE: To develop and validate a computed tomography (CT)-based classification and regression tree (CART) model for the preoperative prediction of LVI in patients with clinical stage IA lung adenocarcinoma (LUAD). MATERIALS AND
methodsThis multicenter cohort study recruited patients who underwent resection and had a preoperative CT examination. An internal cohort (n = 525) is included to construct the LVI classification and regression tree model (LVI-CART). An external cohort (n = 115) and a public cohort (n = 57) are then used to fully validate the predictive performance of the LVI-CART. Kaplan-Meier survival analysis and univariable Cox regression analyses were conducted to investigate the relationship between predicted LVI status and survival.
resultsThe LVI-CART model includes two features, diameter and nodule type, and shows acceptable performance in predicting pathological LVI, with area under the curve values of 0.719, 0.756, and 0.835 in the internal validation set, external validation set and test set, respectively. A predicted LVI positive relative to the median value in the outcomes cohort was found to be independently associated with 1-, 3-year RFS and 1-, 3-, 5-year OS (all p-values < 0.05).
conclusionsThe LVI-CART model could be used to preoperatively predict LVI and identify patients with poor prognosis in clinical IA LUAD. The model is like to be simple and easily applicable to risk stratification. KEY POINTS: Question Lymphovascular invasion is a critical histopathological indicator of poor prognosis, necessitating reliable non-invasive preoperative predictive tools. Findings The classification and regression tree model for predicting lymphovascular invasion (LVI-CART) model demonstrates adequate predictive ability for pathological LVI, portending poor recurrence-free and overall survival. Clinical relevance The LVI-CART model provides clinicians with an easy-to-use method for preoperative identification of patients with clinical stage IA lung adenocarcinoma who are LVI-positive. It also provides a framework for a comprehensive assessment of patient survival risk.
Indexed as
Identifiers
40251442What 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.