ArticlePeerJ2022
Radiomics combined with clinical features in distinguishing non-calcifying tuberculosis granuloma and lung adenocarcinoma in small pulmonary nodules.
Article in PeerJ, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 11 citations in OpenAlex.
- 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
- Comparison of clinical-radiological and radiomics features for predicting pulmonary nodule malignancy in a multicenter study of mixed clinical and surveillance populations.European radiology · 2026Article
- Differentiating Lung Adenocarcinoma from Tuberculous Nodules in HIV/AIDS Patients Using Preoperative CT-Based Intratumoral and Peritumoral Radiomics Combined with Clinical Features.Journal of multidisciplinary healthcare · 2025Article
- The scoring system combined with radiomics and imaging features in predicting the malignant potential of incidental indeterminate small (<20 mm) solid pulmonary nodules.BMC medical imaging · 2024Article
- CT-based radiomics and clinical characteristics for predicting bone metastasis in lung adenocarcinoma patients.Translational lung cancer research · 2024Article
- Machine learning-based radiomics to distinguish pulmonary nodules between lung adenocarcinoma and tuberculosis.Thoracic cancer · 2024Article
- Coccidioidomycosis Granulomas Informed by Other Diseases: Advancements, Gaps, and Challenges.Journal of fungi (Basel, Switzerland) · 2023Review
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Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aim: To evaluate the performance of radiomics models with the combination of clinical features in distinguishing non-calcified tuberculosis granuloma (TBG) and lung adenocarcinoma (LAC) in small pulmonary nodules. Methodology: We conducted a retrospective analysis of 280 patients with pulmonary nodules confirmed by surgical biopsy from January 2017 to December 2020. Samples were divided into LAC group ( Results: Based on the model selection, clinical features gender, and age in the LAC group and TBG group showed a significant difference in both datasets ( Conclusion: With the capacity to detect differences between TBG and LAC based on their clinical features, radiomics models with a combined of clinical features may function as the potential non-invasive tool for distinguishing TBG and LAC in small pulmonary nodules.
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