ArticleiScience2025
CT-based radiomics and cluster analysis for the prediction of local progression in stage I NSCLC patients treated with microwave ablation.
Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Article
- A Machine Learning Model Based on Multi-Phase Contrast-enhanced CT for the Preoperative Prediction of the Muscle-Invasive Status of Bladder Cancer.Current medical imaging · 2025Article
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
No grant is acknowledged in the PubMed record.
Abstract
To predict local progression after microwave ablation (MWA) in patients with stage I non-small cell lung cancer (NSCLC), we developed a CT-based radiomics model. Postoperative CT images were used. The intraclass correlation coefficients, two-sample t-test, least absolute shrinkage and selection operator (LASSO) regression, and Pearson correlation analysis were applied to select radiomics features and establish radiomics score. The Radiomics score was used to classify patients into new radiomics labels. The k-means cluster algorithm was employed to cluster patients into new cluster labels based on radiomics features. Logistic regression was used to build prediction models. The optimal model incorporating clinical risk factors, radiomics labels, and cluster labels achieved the best discrimination. This study proposes a radiomics model that accurately predicts local progression in patients with stage I NSCLC treated with MWA. This prediction tool may be helpful in determining MWA efficacy and individualized risk classification and treatment.
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.