Observational studyScientific reports2023
Radiomics as a non-invasive adjunct to Chest CT in distinguishing benign and malignant lung nodules.
Observational study in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- An open-source CT texture phantom for radiomic imaging studies: Design, manufacture, and initial evaluation.Medical physics · 2026Article
- CT radiomics for survival risk stratification in resectable colorectal liver metastases: a multi-centre study.Scientific reports · 2026Article
- Improving risk stratification of pulmonary nodules: an integrated perinodular vascular and radiomic model for clinical decision support.BMC medical imaging · 2026Article
- Radiomics-derived classifier performance evaluation in lung nodule characterization compared with expert radiologists.Scientific reports · 2026Article
- Radiomics in glioblastoma recurrence: advances in prediction, localization, and differentiation from treatment-related effects.Journal of translational medicine · 2026Review
- Clinical study ofPeerJ · 2026Article
- Phantom-based evaluation of radiomics feature stability for low-dose CT lung cancer screening.Frontiers in endocrinology · 2026Article
- Radiomics-based explainable artificial intelligence to predict treatment response following lung stereotactic body radiation therapy.The British journal of radiology · 2025Article
- Radiomics-based classification of medication-related osteonecrosis of the jaw using panoramic radiographs.Oral radiology · 2025Article
- Radiomic phenotypes of oligometastatic non-small cell lung cancer.Translational lung cancer research · 2025Article
- Radiomics: Assessing Significance and Correlation with Ground-Truth Data in Precision Medicine in Lung Adenocarcinoma.Bioengineering (Basel, Switzerland) · 2025Article
- Integrating CT radiomics and clinical data with machine learning to predict fibrosis progression in coalworker pneumoconiosis.Frontiers in medicine · 2025Article
- Radiomics for differentiating adenocarcinoma and squamous cell carcinoma in non-small cell lung cancer beyond nodule morphology in chest CT.Scientific reports · 2024Article
- Radiomics analysis for distinctive identification of COVID-19 pulmonary nodules from other benign and malignant counterparts.Scientific reports · 2024Observational
- [A clinical-radiomics nomogram for differentiating focal organizing pneumonia and lung adenocarcinoma].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2024Article
- Machine Learning Radiomics Signature for Differentiating Lymphoma versus Benign Splenomegaly on CT.Diagnostics (Basel, Switzerland) · 2023Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In an observational study conducted from 2016 to 2021, we assessed the utility of radiomics in differentiating between benign and malignant lung nodules detected on computed tomography (CT) scans. Patients in whom a final diagnosis regarding the lung nodules was available according to histopathology and/or 2017 Fleischner Society guidelines were included. The radiomics workflow included lesion segmentation, region of interest (ROI) definition, pre-processing, and feature extraction. Employing random forest feature selection, we identified ten important radiomic features for distinguishing between benign and malignant nodules. Among the classifiers tested, the Decision Tree model demonstrated superior performance, achieving 79% accuracy, 75% sensitivity, 85% specificity, 82% precision, and 90% F1 score. The implementation of the XGBoost algorithm further enhanced these results, yielding 89% accuracy, 89% sensitivity, 89% precision, and an F1 score of 89%, alongside a specificity of 85%. Our findings highlight tumor texture as the primary predictor of malignancy, emphasizing the importance of texture-based features in computational oncology. Thus, our study establishes radiomics as a powerful, non-invasive adjunct to CT scans in the differentiation of lung nodules, with significant implications for clinical decision-making, especially for indeterminate nodules, and the enhancement of diagnostic and predictive accuracy in this clinical context.
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