ArticleEuropean journal of nuclear medicine and molecular imaging2021
Value of
Article in European journal of nuclear medicine and molecular imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
What it found
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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
30 citing papers in PubMed.
- Radiomics based on enhanced CT for differentiating between pulmonary tuberculosis and pulmonary adenocarcinoma presenting as solid nodules or masses.Journal of cancer research and clinical oncology · 2023Trial
- Article
- Differentiation of pulmonary tuberculosis from non-tuberculous solid lung lesions using radiomics and clinical-semantic features on contrast-enhanced CT.Frontiers in medicine · 2026Article
- Imaging and RNA sequencing-based radiogenomic analysis of occult lymph node metastasis and survival in lung adenocarcinoma staged non-metastatic.Communications medicine · 2025Article
- Development and validation of a diagnostic radiomics model for the differential diagnosis of coexistent tuberculosis and lung cancer versus isolated lung cancer.Journal of thoracic disease · 2025Article
- PET radiomics in lung cancer: advances and translational challenges.EJNMMI physics · 2024Review
- AI-Driven Thoracic X-ray Diagnostics: Transformative Transfer Learning for Clinical Validation in Pulmonary Radiography.Journal of personalized medicine · 2024Article
- CT-based pancreatic radiomics predicts secondary loss of response to infliximab in biologically naïve patients with Crohn's disease.Insights into imaging · 2024Article
- Machine learning-based radiomics to distinguish pulmonary nodules between lung adenocarcinoma and tuberculosis.Thoracic cancer · 2024Article
- An [European radiology · 2024Article
- Peri- and intra-nodular radiomic features based onFrontiers in medicine · 2024Article
- PET/CT radiomics and deep learning in the diagnosis of benign and malignant pulmonary nodules: progress and challenges.Frontiers in oncology · 2024Review
- A CT-based radiomics predictive nomogram to identify pulmonary tuberculosis from community-acquired pneumonia: a multicenter cohort study.Frontiers in cellular and infection microbiology · 2024Article
- Radiomic analysis will add differential diagnostic value of benign and malignant pulmonary nodules: a hybrid imaging study based on [Insights into imaging · 2023Article
- Radiomics analysis of lung CT for multidrug resistance prediction in active tuberculosis: a multicentre study.European radiology · 2023Article
- Predicting PD-L1 expression status in patients with non-small cell lung cancer using [EJNMMI research · 2023Article
- Machine learning and radiomics for the prediction of multidrug resistance in cavitary pulmonary tuberculosis: a multicentre study.European radiology · 2023Article
- Distinction of Lymphoma from Sarcoidosis onJournal of nuclear medicine : official publication, Society of Nuclear Medicine · 2022Article
- Diagnostic Performance of Machine Learning Models Based onMolecular imaging and radionuclide therapy · 2022Article
- Radiomics in Oncological PET Imaging: A Systematic Review-Part 1, Supradiaphragmatic Cancers.Diagnostics (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
purposeTo develop a predictive model by
methodsA total of 235
resultsIn the training cohort, 9 radiomic features were selected as optimal predictors to build the radiomic model. The AUC of the radiomic model was significantly higher than that of the clinical model in the training cohort (0.861 versus 0.686, p < 0.01), and this was similar in the validation cohort (0.889 versus 0.644, p < 0.01). The AUC of the radiomic model was slightly lower than that of the complex model in the training cohort (0.861 versus 0.884, p > 0.05) and validation cohort (0.889 versus 0.909, p > 0.05), but there was no significant difference.
conclusion
Indexed as
Identifiers
32588088What 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.