ArticleEuropean radiology experimental2024
Development of a combined radiomics and CT feature-based model for differentiating malignant from benign subcentimeter solid pulmonary nodules.
Article in European radiology experimental, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 4 of them syntheses that pooled it.
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Who cites it
29 citing papers in PubMed, 4 syntheses or guidelines pooled it, 17 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
- A methodological framework for integrating generalisable deep learning and radiomics fusion model for early lung cancer detection across multi-centre imaging datasets.BMC medical informatics and decision making · 2026Pooled it
- Risk factors for malignant solid pulmonary nodules: a meta-analysis.BMC cancer · 2025Pooled it
- Knowledge mapping analysis of ground glass nodules: a bibliometric analysis from 2013 to 2023.Frontiers in oncology · 2024Pooled it
- Noninvasive differentiation of benign and malignant solid pulmonary nodules using multiparameter dual-layer spectral CT radiomics.Insights into imaging · 2026Article
- Development and internal validation of a high-resolution computed tomography radiomics and three-dimensional deep learning diagnostic prediction model for preoperative differentiation of minimally invasive and invasive adenocarcinoma in subsolid nodules.Journal of thoracic disease · 2026Article
- Artificial intelligence-assisted early screening of lung cancer and accurate diagnosis of pulmonary nodules: research progress and clinical prospects from radiomics to multi-omics integration: a narrative review.Journal of thoracic disease · 2026Review
- Exploring the key clinical and computed tomography features for distinguishing high-grade lung cancers from morphologically similar benign tumors: a two-center case-control study.BMC medical imaging · 2026Article
- Radiomics-derived classifier performance evaluation in lung nodule characterization compared with expert radiologists.Scientific reports · 2026Article
- Development and validation of a habitat-based computed tomography radiomics model for differentiating isolated lung cancer, isolated tuberculoma, and coexistence of tuberculosis with lung cancer: a dual-center retrospective study.Translational lung cancer research · 2026Article
- Tumor morphology on CT radiomics is largely driven by the local anatomical environment, not the primary tumor type.European radiology experimental · 2026Article
- CT-based radiomics and intratumoral heterogeneity for predicting benign and malignant lesions in solid pulmonary nodules.Journal of thoracic disease · 2026Article
- Impact of CT acquisition settings on the stability of radiomic features and the performance of pulmonary nodule classification models.Insights into imaging · 2026Article
- Intranodular and perinodular radiomics features based on non-contrast CT to distinguish pulmonary cryptococcosis from lung adenocarcinoma: a two-center study.Frontiers in oncology · 2026Article
- Predictive Factors and Nomogram for Malignant Pulmonary Nodules (≤ 1 cm).Canadian respiratory journal · 2026Article
- Development and validation of a CT-based comprehensive nomogram for differentiating benign from malignant subcentimeter solid nodules.Journal of thoracic disease · 2025Article
- Influencing factors and prediction of growth heterogeneity in solid nodule non-small cell lung cancer based on artificial intelligence: a prospective study.Translational lung cancer research · 2025Article
- Computed tomography radiomics of intratumoral and peritumoral microenvironments for identifying the invasiveness of subcentimeter lung adenocarcinomas.BMC medical imaging · 2025Article
- Reproducibility of methodological radiomics score (METRICS): an intra- and inter-rater reliability study endorsed by EuSoMII.European radiology · 2025Article
- Development and validation of growth prediction models for multiple pulmonary ground-glass nodules based on CT features, radiomics, and deep learning.Translational lung cancer research · 2025Article
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Authors and funding
10 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundWe aimed to develop a combined model based on radiomics and computed tomography (CT) imaging features for use in differential diagnosis of benign and malignant subcentimeter (≤ 10 mm) solid pulmonary nodules (SSPNs).
methodsA total of 324 patients with SSPNs were analyzed retrospectively between May 2016 and June 2022. Malignant nodules (n = 158) were confirmed by pathology, and benign nodules (n = 166) were confirmed by follow-up or pathology. SSPNs were divided into training (n = 226) and testing (n = 98) cohorts. A total of 2107 radiomics features were extracted from contrast-enhanced CT. The clinical and CT characteristics retained after univariate and multivariable logistic regression analyses were used to develop the clinical model. The combined model was established by associating radiomics features with CT imaging features using logistic regression. The performance of each model was evaluated using the area under the receiver-operating characteristic curve (AUC).
resultsSix CT imaging features were independent predictors of SSPNs, and four radiomics features were selected after a dimensionality reduction. The combined model constructed by the logistic regression method had the best performance in differentiating malignant from benign SSPNs, with an AUC of 0.942 (95% confidence interval 0.918-0.966) in the training group and an AUC of 0.930 (0.902-0.957) in the testing group. The decision curve analysis showed that the combined model had clinical application value.
conclusionsThe combined model incorporating radiomics and CT imaging features had excellent discriminative ability and can potentially aid radiologists in diagnosing malignant from benign SSPNs. RELEVANCE STATEMENT: The model combined radiomics features and clinical features achieved good efficiency in predicting malignant from benign SSPNs, having the potential to assist in early diagnosis of lung cancer and improving follow-up strategies in clinical work. KEY POINTS: • We developed a pulmonary nodule diagnostic model including radiomics and CT features. • The model yielded the best performance in differentiating malignant from benign nodules. • The combined model had clinical application value and excellent discriminative ability. • The model can assist radiologists in diagnosing malignant from benign pulmonary nodules.
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