ArticleCancers2021
A Novel Nodule Edge Sharpness Radiomic Biomarker Improves Performance of Lung-RADS for Distinguishing Adenocarcinomas from Granulomas on Non-Contrast CT Scans.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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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.
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Who cites it
6 citing papers in PubMed.
- Initial experience of deep learning reconstruction algorithm in lung kernel: clinical usefulness for lung nodules at ultra-low-dose protocol.European radiology · 2026Article
- Incorporating adipose tissue into a CT-based deep learning nomogram to differentiate granulomas from lung adenocarcinomas.iScience · 2024Article
- A CT-based radiomics integrated model for discriminating pulmonary cryptococcosis granuloma from lung adenocarcinoma-a diagnostic test.Translational lung cancer research · 2023Article
- Radiomics-Based Quality Control System for Automatic Cardiac Segmentation: A Feasibility Study.Bioengineering (Basel, Switzerland) · 2023Article
- Feature selection methods and predictive models in CT lung cancer radiomics.Journal of applied clinical medical physics · 2023Review
- Ten quick tips for computational analysis of medical images.PLoS computational biology · 2023Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
The aim of this study is to evaluate whether NIS radiomics can distinguish lung adenocarcinomas from granulomas on non-contrast CT scans, and also to improve the performance of Lung-RADS by reclassifying benign nodules that were initially assessed as suspicious. The screening or standard diagnostic non-contrast CT scans of 362 patients was divided into training (S
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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.