ArticleEuropean radiology2020
Radiomics for lung adenocarcinoma manifesting as pure ground-glass nodules: invasive prediction.
Article in European radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 99 papers, 1 of them a synthesis that pooled it.
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Who cites it
99 citing papers in PubMed, 1 synthesis or guideline pooled it, 141 citations in OpenAlex.
- Diagnostic Accuracy of Deep Learning and Radiomics in Lung Cancer Staging: A Systematic Review and Meta-Analysis.Frontiers in public health · 2022Pooled it
- Trial
- Computed tomography radiomics-based distinction of invasive adenocarcinoma from minimally invasive adenocarcinoma manifesting as pure ground-glass nodules with bubble-like signs.General thoracic and cardiovascular surgery · 2022Trial
- A computerized tomography-based radiomic model for assessing the invasiveness of lung adenocarcinoma manifesting as ground-glass opacity nodules.Respiratory research · 2022Trial
- Spectral CT-based intratumoral and peritumoral radiomics for predicting invasiveness of ground-glass nodules in lung adenocarcinoma.La Radiologia medica · 2026Article
- Radiomic model with SHAP-based interpretability for predicting invasiveness of pure ground-glass nodules: a retrospective study based on high-resolution computed tomography (HRCT) volumetric datasets.Journal of cardiothoracic surgery · 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
- A radiomics nomogram integrated with radiological features for preoperative prediction of lung nodule invasiveness: comparison with Lung-RADS.Translational lung cancer research · 2026Article
- Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications.Cancers · 2026Review
- Preoperative CT-based radiomics to predict the 5-year growth of residual nodules after resection of dominant lung tumors in patients with multiple lung subsolid nodules.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- CT Radiomics-based machine learning approach for the invasiveness of pulmonary ground-glass nodules prediction.European journal of radiology open · 2025Article
- Hybrid Approach to Classifying Histological Subtypes of Non-small Cell Lung Cancer (NSCLC): Combining Radiomics and Deep Learning Features from CT Images.Journal of imaging informatics in medicine · 2025Article
- Preoperative CT-based artificial intelligence-derived quantitative parameters and imaging features for predicting the invasiveness of histologically confirmed subcentimeter adenocarcinomatous nodules: a two-center study.Quantitative imaging in medicine and surgery · 2025Article
- Contrast-enhanced computed tomography-based intratumoral and peritumoral radiomics for identifying malignancy in pulmonary ground-glass nodules.Journal of thoracic disease · 2025Article
- Radiomics-based explainable artificial intelligence to predict treatment response following lung stereotactic body radiation therapy.The British journal of radiology · 2025Article
- Predicting pathological subtypes of pure ground-glass nodules using Swin Transformer deep learning model.Insights into imaging · 2025Article
- Lung Nodule Malignancy Classification Integrating Deep and Radiomic Features in a Three-Way Attention-Based Fusion Module.Journal of imaging · 2025Article
- Functional remodeling of tissue-resident macrophages leads to distinct immune microenvironment evolution and clinical manifestations in lung adenocarcinoma.Clinical and experimental medicine · 2025Article
- A multiomics dataset of paired CT image and plasma cell-free DNA end motif for patients with pulmonary nodules.Scientific data · 2025Article
- Construction and validation of a risk stratification model based on Lung-RADSInsights into imaging · 2025Article
39 more citing papers are in PubMed but not listed here.
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Authors and funding
11 authors at 4 institutions in 1 country.
Funding
Abstract
objectivesTo investigate the value of radiomics based on CT imaging in predicting invasive adenocarcinoma manifesting as pure ground-glass nodules (pGGNs).
methodsThis study enrolled 395 pGGNs with histopathology-confirmed benign nodules or adenocarcinoma. A total of 396 radiomic features were extracted from each labeled nodule. A Rad-score was constructed with the least absolute shrinkage and selection operator (LASSO) in the training set. Multivariate logistic regression analysis was conducted to establish the radiographic model and the combined radiographic-radiomics model. The predictive performance was validated by receiver operating characteristic (ROC) curve. Based on the multivariate logistic regression analysis, an individual prediction nomogram was developed and the clinical utility was assessed.
resultsFive radiomic features and four radiographic features were selected for predicting the invasive lesions. The combined radiographic-radiomics model (AUC 0.77; 95% CI, 0.69-0.86) performed better than the radiographic model (AUC 0.71; 95% CI, 0.62-0.81) and Rad-score (AUC 0.72; 95% CI, 0.63-0.81) in the validation set. The clinical utility of the individualized prediction nomogram developed using the Rad-score, margin, spiculation, and size was confirmed in the validation set. The decision curve analysis (DCA) indicated that using a model with Rad-score to predict the invasive lesion would be more beneficial than that without Rad-score and the clinical model.
conclusionsThe proposed radiomics-based nomogram that incorporated the Rad-score, margin, spiculation, and size may be utilized as a noninvasive biomarker for the assessment of invasive prediction in patients with pGGNs. KEY POINTS: • CT-based radiomics analysis helps invasive prediction manifested as pGGNs. • The combined radiographic-radiomics model may be utilized as a noninvasive biomarker for predicting invasive lesion for pGGNs. • Radiomics-based individual nomogram may serve as a vital decision support tool to identify invasive pGGNs, obviating further workup and blind follow-up.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.