ArticleFrontiers in oncology2021
Clinical and CT Radiomics Nomogram for Preoperative Differentiation of Pulmonary Adenocarcinoma From Tuberculoma in Solitary Solid Nodule.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 3 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 3 syntheses or guidelines pooled it, 38 citations in OpenAlex.
- Incidence and risk factors for malignancy in patients with incidental solitary pulmonary nodules: a systematic review and meta-analysis.Annals of medicine · 2026Pooled it
- Pooled it
- Risk factors for malignant solid pulmonary nodules: a meta-analysis.BMC cancer · 2025Pooled it
- 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
- Radiomics nomogram for preoperative differentiation of pulmonary mucinous adenocarcinoma from tuberculoma in solitary pulmonary solid nodules.BMC cancer · 2023Trial
- Spectral CT-based radiomics signature for distinguishing malignant pulmonary nodules from benign.BMC cancer · 2023Trial
- Development and validation of machine learning diagnostic models integrating clinical, CT, and laboratory features to differentiate lung cancer from pulmonary tuberculosis in patients with solitary pulmonary nodules: a single-center retrospective study.Translational lung cancer research · 2026Article
- Differential diagnosis of benign lesions and lung adenocarcinoma presenting as lung-RADS 2022 category 4B solid nodules based on multiscale CT radiomics.BMC cancer · 2026Article
- Combined anatomical MRI differentiates pulmonary invasive adenocarcinoma from tuberculoma in noncalcified nodule: a retrospective comparison of CT with MRI.Journal of thoracic disease · 2026Article
- Development of a nomogram-based model incorporating radiomic features from follow-up longitudinal lung CT images to distinguish invasive adenocarcinoma from benign lesions: a retrospective study.BMC pulmonary medicine · 2024Article
- AI-Driven Thoracic X-ray Diagnostics: Transformative Transfer Learning for Clinical Validation in Pulmonary Radiography.Journal of personalized medicine · 2024Article
- Radiomics analysis of contrast-enhanced computerized tomography for differentiation of gastric schwannomas from gastric gastrointestinal stromal tumors.Journal of cancer research and clinical oncology · 2024Article
- Machine learning-based radiomics to distinguish pulmonary nodules between lung adenocarcinoma and tuberculosis.Thoracic cancer · 2024Article
- Development of a combined radiomics and CT feature-based model for differentiating malignant from benign subcentimeter solid pulmonary nodules.European radiology experimental · 2024Article
- Article
- Diagnostic performance of a deep learning-based method in differentiating malignant from benign subcentimeter (≤10 mm) solid pulmonary nodules.Journal of thoracic disease · 2023Article
- Immunocyte count combined with CT features for distinguishing pulmonary tuberculoma from malignancy among non-calcified solitary pulmonary solid nodules.Journal of thoracic disease · 2023Article
- Article
- A CT-based nomogram for predicting the risk of adenocarcinomas in patients with subsolid nodule according to the 2021 WHO classification.Cancer imaging : the official publication of the International Cancer Imaging Society · 2022Article
- Differential diagnosis of pulmonary nodular mucinous adenocarcinoma and tuberculoma with dynamic CT: a retrospective study.Journal of thoracic disease · 2022Article
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Authors and funding
7 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimTo investigate clinical and computed tomography (CT) radiomics nomogram for preoperative differentiation of lung adenocarcinoma (LAC) from lung tuberculoma (LTB) in patients with pulmonary solitary solid nodule (PSSN). MATERIALS AND
methodsA total of 313 patients were recruited in this retrospective study, including 96 pathologically confirmed LAC and 217 clinically confirmed LTB. Patients were assigned at random to training set (n = 220) and validation set (n = 93) according to 7:3 ratio. A total of 2,589 radiomics features were extracted from each three-dimensional (3D) lung nodule on thin-slice CT images and radiomics signatures were built using the least absolute shrinkage and selection operator (LASSO) logistic regression. The predictive nomogram was established based on radiomics and clinical features. Decision curve analysis was performed with training and validation sets to assess the clinical usefulness of the prediction model.
resultsA total of six clinical features were selected as independent predictors, including spiculated sign, vacuole, minimum diameter of nodule, mediastinal lymphadenectasis, sex, and age. The radiomics nomogram of lung nodules, consisting of 15 selected radiomics parameters and six clinical features showed good prediction in the training set [area under the curve (AUC), 1.00; 95% confidence interval (CI), 0.99-1.00] and validation set (AUC, 0.99; 95% CI, 0.98-1.00). The nomogram model that combined radiomics and clinical features was better than both single models (p < 0.05). Decision curve analysis showed that radiomics features were beneficial to clinical settings.
conclusionThe radiomics nomogram, derived from unenhanced thin-slice chest CT images, showed favorable prediction efficacy for differentiating LAC from LTB in patients with PSSN.
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