SynthesisThoracic cancer2018
Can CT imaging features of ground-glass opacity predict invasiveness? A meta-analysis.
Synthesis in Thoracic cancer, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Development and validation of a risk prediction model for invasiveness of pure ground-glass nodules based on a systematic review and meta-analysis.BMC medical imaging · 2024Pooled it
- Artificial intelligence-based deep learning algorithms for ground-glass opacity nodule detection: A review.Narra J · 2025Review
- An artificial intelligence algorithm for the detection of pulmonary ground-glass nodules on spectral detector CT: performance on virtual monochromatic images.BMC medical imaging · 2024Article
- Deep learning-based characterization of pathological subtypes in lung invasive adenocarcinoma utilizingBMC cancer · 2024Article
- Article
- Bubble-like lucency in pulmonary ground glass nodules on computed tomography: a specific pattern of air-containing space for diagnosing neoplastic lesions.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Predicting the Invasiveness of Pulmonary Adenocarcinomas in Pure Ground-Glass Nodules Using the Nodule Diameter: A Systematic Review, Meta-Analysis, and Validation in an Independent Cohort.Diagnostics (Basel, Switzerland) · 2024Review
- CT morphological features and histogram parameters to predict micropapillary or solid components in stage IA lung adenocarcinoma.Frontiers in oncology · 2024Article
- The value of preoperative positron emission tomography/computed tomography in differentiating the invasive degree of hypometabolic lung adenocarcinoma.BMC medical imaging · 2023Article
- Spectral Dual-Layer Computed Tomography Can Predict the Invasiveness of Ground-Glass Nodules: A Diagnostic Model Combined with Thymidine Kinase-1.Journal of clinical medicine · 2023Article
- [Advances in the Study of Invasive Non-mucinous Adenocarcinoma with Different Pathological Subtypes].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2023Review
- Clinical and radiomic factors for predicting invasiveness in pulmonary ground‑glass opacity.Experimental and therapeutic medicine · 2022Article
- Identification of pathological subtypes of early lung adenocarcinoma based on artificial intelligence parameters and CT signs.Bioscience reports · 2022Article
- Application of dual-stream 3D convolutional neural network based onEJNMMI physics · 2021Article
- Investigating the association between ground-glass nodules glucose metabolism and the invasive growth pattern of early lung adenocarcinoma.Quantitative imaging in medicine and surgery · 2021Article
- Establishment and verification of a prediction model based on clinical characteristics and positron emission tomography/computed tomography (PET/CT) parameters for distinguishing malignant from benign ground-glass nodules.Quantitative imaging in medicine and surgery · 2021Article
- Invasive Prediction of Ground Glass Nodule Based on Clinical Characteristics and Radiomics Feature.Frontiers in genetics · 2021Article
- Maximum Standardized Uptake Value ofFrontiers in oncology · 2021Article
- Value ofEJNMMI research · 2020Article
- Feasibility of electromagnetic navigation bronchoscopy-guided lung resection for pulmonary ground-glass opacity nodules.Journal of thoracic disease · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA meta-analysis was conducted to investigate the diagnostic performance of computed tomography (CT) imaging features of ground-glass opacity (GGO) to predict invasiveness.
methodsTwo reviewers independently searched PubMed, Medline, Web of Science, Cochrane Embase and CNKI for relevant studies. CT imaging signs of bubble lucency, speculation, lobulated margin, and pleural indentation were used as diagnostic references to discriminate pre-invasive and invasive disease. The sensitivity, specificity, diagnostic odds ratio (DOR), summary receiver operating characteristic (SROC) curves, and the area under the SROC curve (AUC) were calculated to evaluate diagnostic efficiency.
resultsTwelve studies were finally included. Diagnostic performance ranged from 0.41 to 0.52 for sensitivity and 0.56 to 0.63 for specificity. The diagnostic positive and negative likelihood ratios ranged from 1.03 to 2.13 and 0.52 to 1.05, respectively. The DORs of the GGO CT features for discriminating invasive disease ranged from 1.02 to 4.00. The area under the ROC curve was also low, with a range of 0.60 to 0.67 for discriminating pre-invasive and invasive disease.
conclusionThe diagnostic value of a single CT imaging sign of GGO, such as bubble lucency, speculation, lobulated margin, or pleural indentation is limited for discriminating pre-invasive and invasive disease because of low sensitivity, specificity, and AUC.
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