ArticleRadiology2019
Perinodular Radiomic Features to Assess Nodule Microenvironment: Does It Help to Distinguish Malignant versus Benign Lung Nodules?
Article in Radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- A computerized tomography-based radiomic model for assessing the invasiveness of lung adenocarcinoma manifesting as ground-glass opacity nodules.Respiratory research · 2022Trial
- Development and validation of a preoperative CT-based radiomic nomogram to predict pathology invasiveness in patients with a solitary pulmonary nodule: a machine learning approach, multicenter, diagnostic study.European radiology · 2022Trial
- Non-enhanced CT deep learning model for differentiating lung adenocarcinoma from tuberculoma: a multicenter diagnostic study.European radiology · 2025Article
- Radiomics Features from Different Prostatic Zones onCancers · 2025Article
- CT radiomics from intratumor and peritumor regions for predicting poorly differentiated invasive nonmucinous pulmonary adenocarcinoma.Scientific reports · 2025Article
- Nomogram using intratumoral and peritumoral radiomics for the preoperative prediction of visceral pleural invasion in clinical stage IA lung adenocarcinoma.Journal of cardiothoracic surgery · 2024Article
- Predicting the pathological invasiveness in patients with a solitary pulmonary nodule via Shapley additive explanations interpretation of a tree-based machine learning radiomics model: a multicenter study.Quantitative imaging in medicine and surgery · 2023Article
- MRI advances in the imaging diagnosis of tuberculous meningitis: opportunities and innovations.Frontiers in microbiology · 2023Review
- Emerging applications of radiomics in rectal cancer: State of the art and future perspectives.World journal of gastroenterology · 2021Review
- Quantitative Assessment of Chest CT Patterns in COVID-19 and Bacterial Pneumonia Patients: a Deep Learning Perspective.Journal of Korean medical science · 2021Article
- Developing and validating a chronic obstructive pulmonary disease quick screening questionnaire using statistical learning models.Chronic respiratory diseaseArticle
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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Identifiers
30561277What 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.