Trial reportRespiratory research2022
A computerized tomography-based radiomic model for assessing the invasiveness of lung adenocarcinoma manifesting as ground-glass opacity nodules.
Trial report in Respiratory research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Preoperative predicting invasiveness of lung adenocarcinoma manifesting as ground-glass nodules based on multimodal images of dual-layer spectral detector CT radiomics models.Journal of cancer research and clinical oncology · 2023Trial
- Moran's I-driven habitat radiomics: A biologically plausible and temporally robust approach for risk stratification of lung adenocarcinoma invasiveness.European journal of radiology open · 2026Article
- Improving the preoperative diagnosis of invasive lung adenocarcinoma by combining multiplanar volume rendering (MPVR)-based solid component measurement with pathological analysis.Quantitative imaging in medicine and surgery · 2026Article
- Assessment of risk factors in persistent subsolid pulmonary nodules based on CT dynamic follow-up.BMC medical imaging · 2026Article
- Advancing lung adenocarcinoma diagnosis using peri-nodular features: a CT radiomics study for determining invasiveness in sub-centimeter pure ground glass nodules.Journal of thoracic disease · 2026Article
- Computed tomography radiomics of intratumoral and peritumoral microenvironments for identifying the invasiveness of subcentimeter lung adenocarcinomas.BMC medical imaging · 2025Article
- Peritumoral features for assessing invasiveness of lung adenocarcinoma manifesting as ground-glass nodules.Scientific reports · 2025Article
- New Perspectives on Lung Cancer Screening and Artificial Intelligence.Life (Basel, Switzerland) · 2025Review
- Spectral dual-layer detector CT-based radiomics-deep learning for predicting pathological aggressiveness of stage I lung adenocarcinoma: discrimination of precursor glandular lesions and invasive adenocarcinomas.Translational lung cancer research · 2025Article
- Predicting malignant risk of ground-glass nodules using convolutional neural networks based on dual-time-pointCancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- CT-based habitat radiomics for preoperative differentiation of adenocarcinoma in situ/minimally invasive adenocarcinoma from invasive adenocarcinoma manifesting as ground-glass nodules: a multicenter study.Frontiers in oncology · 2025Article
- Prediction and analysis of the tumor invasiveness of pulmonary ground-glass nodules based on metabolomics.Clinical and experimental medicine · 2024Article
- A nomogram for predicting invasiveness of lung adenocarcinoma manifesting as ground-glass nodules based on follow-up CT imaging.Translational lung cancer research · 2024Article
- Predicting the invasiveness of ground-glass opacity predominant lung adenocarcinoma with clinical stage Ia: a CT-based semantic and radiomics analysis.Journal of thoracic disease · 2024Article
- Diagnostic value of an interpretable machine learning model based on clinical ultrasound features for follicular thyroid carcinoma.Quantitative imaging in medicine and surgery · 2024Article
- Image quality and detection efficacy of zero echo time magnetic resonance imaging on Lung-RADS 2 pulmonary ground-glass nodules in comparison to thin-slice fat-saturated T2-weighted imaging.Journal of thoracic disease · 2024Article
- CT-based radiomic consensus clustering association with tumor biological behavior in clinical stage IA adenocarcinoma: a retrospective study.Translational lung cancer research · 2024Article
- Imaging diagnostics of pulmonary ground-glass nodules: a narrative review with current status and future directions.Quantitative imaging in medicine and surgery · 2024Review
- Histopathologic fate of resected pulmonary pure ground glass nodule: a systematic review and meta-analysis.Journal of thoracic disease · 2024Article
- The value of intratumoral and peritumoral radiomics features in differentiating early-stage lung invasive adenocarcinoma (≤3 cm) subtypes.Translational cancer research · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
backgroundClinically differentiating preinvasive lesions (atypical adenomatous hyperplasia, AAH and adenocarcinoma in situ, AIS) from invasive lesions (minimally invasive adenocarcinomas, MIA and invasive adenocarcinoma, IA) manifesting as ground-glass opacity nodules (GGOs) is difficult due to overlap of morphological features. Hence, the current study was performed to explore the diagnostic efficiency of radiomics in assessing the invasiveness of lung adenocarcinoma manifesting as GGOs.
methodsA total of 1018 GGOs pathologically confirmed as lung adenocarcinoma were enrolled in this retrospective study and were randomly divided into a training set (n = 712) and validation set (n = 306). The nodules were delineated manually and 2446 intra-nodular and peri-nodular radiomic features were extracted. Univariate analysis and least absolute shrinkage and selection operator (LASSO) were used for feature selection. Clinical and semantic computerized tomography (CT) feature model, radiomic model and a combined nomogram were constructed and compared. Decision curve analysis (DCA) was used to evaluate the clinical value of the established nomogram.
results16 radiomic features were selected and used for model construction. The radiomic model exhibited significantly better performance (AUC = 0.828) comparing to the clinical-semantic model (AUC = 0.746). Further analysis revealed that peri-nodular radiomic features were useful in differentiating between preinvasive and invasive lung adenocarcinomas appearing as GGOs with an AUC of 0.808. A nomogram based on lobulation sign and radiomic features showed the best performance (AUC = 0.835), and was found to have potential clinical value in assessing nodule invasiveness.
conclusionsRadiomic model based on both intra-nodular and peri-nodular features showed good performance in differentiating between preinvasive lung adenocarcinoma lesions and invasive ones appearing as GGOs, and a nomogram based on clinical, semantic and radiomic features could provide clinicians with added information in nodule management and preoperative evaluation.
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