ArticleChinese journal of cancer research = Chung-kuo yen cheng yen chiu2018
Radiomic analysis of pulmonary ground-glass opacity nodules for distinction of preinvasive lesions, invasive pulmonary adenocarcinoma and minimally invasive adenocarcinoma based on quantitative texture analysis of CT.
Article in Chinese journal of cancer research = Chung-kuo yen cheng yen chiu, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Epidemiology of Ground Glass Opacity Lung Adenocarcinoma: A Network-Based Cumulative Meta-Analysis.Frontiers in oncology · 2020Pooled it
- Development and validation of a preoperative CT-based radiomic nomogram to predict pathology invasiveness in patients with a solitary pulmonary nodule: a machine learning approach, multicenter, diagnostic study.European radiology · 2022Trial
- Precision Medicine in Lung Cancer Screening: A Paradigm Shift in Early Detection-Precision Screening for Lung Cancer.Diagnostics (Basel, Switzerland) · 2025Review
- CT based 3D radiomic and clinical airway examination model for evaluating mask ventilation in oral and maxillofacial surgery patients.Scientific reports · 2025Article
- Premalignant Progression in the Lung: Knowledge Gaps and Novel Opportunities for Interception of Non-Small Cell Lung Cancer. An Official American Thoracic Society Research Statement.American journal of respiratory and critical care medicine · 2024Review
- A Bi-LSTM and multihead attention-based model incorporating radiomics signatures and radiological features for differentiating the main subtypes of lung adenocarcinoma.Quantitative imaging in medicine and surgery · 2023Article
- Deep-Learning Model of ResNet Combined with CBAM for Malignant-Benign Pulmonary Nodules Classification on Computed Tomography Images.Medicina (Kaunas, Lithuania) · 2023Article
- Article
- An integrated nomogram combined semantic-radiomic features to predict invasive pulmonary adenocarcinomas in subjects with persistent subsolid nodules.Quantitative imaging in medicine and surgery · 2023Article
- Artificial-intelligence-based computed tomography histogram analysis predicting tumor invasiveness of lung adenocarcinomas manifesting as radiological part-solid nodules.Frontiers in oncology · 2023Article
- Predicting Tumor Mutation Burden andJournal of personalized medicine · 2022Article
- Clinical and radiomic factors for predicting invasiveness in pulmonary ground‑glass opacity.Experimental and therapeutic medicine · 2022Article
- Radiomics in Early Lung Cancer Diagnosis: From Diagnosis to Clinical Decision Support and Education.Diagnostics (Basel, Switzerland) · 2022Review
- Radiomics-based machine learning differentiates "ground-glass" opacities due to COVID-19 from acute non-COVID-19 lung disease.Scientific reports · 2021Article
- Can computed tomography differentiate adenocarcinoma in situ from minimally invasive adenocarcinoma?Thoracic cancer · 2021Article
- Do we need to see to believe?-radiomics for lung nodule classification and lung cancer risk stratification.Journal of thoracic disease · 2020Review
- Pulmonary adenocarcinoma appearing as ground-glass opacity nodules identified using non-enhanced and contrast-enhanced CT texture analysis: A retrospective analysis.Experimental and therapeutic medicine · 2020Article
- Differences in lung cancer characteristics and mortality rate between screened and non-screened cohorts.Scientific reports · 2019Article
- Invasiveness and surgical timing evaluation by clinical features of ground-glass opacity nodules in lung cancers.Thoracic cancer · 2019Article
- Radiomic signature: a non-invasive biomarker for discriminating invasive and non-invasive cases of lung adenocarcinoma.Cancer management and research · 2019Article
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6 authors.
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Abstract
objectiveTo identify the differences among preinvasive lesions, minimally invasive adenocarcinomas (MIAs) and invasive pulmonary adenocarcinomas (IPAs) based on radiomic feature analysis with computed tomography (CT).
methodsA total of 109 patients with ground-glass opacity lesions (GGOs) in the lungs determined by CT examinations were enrolled, all of whom had received a pathologic diagnosis. After the manual delineation and segmentation of the GGOs as regions of interest (ROIs), the patients were subdivided into three groups based on pathologic analyses: the preinvasive lesions (including atypical adenomatous hyperplasia and adenocarcinoma
resultsWe found that the voxel count feature (P<0.001) could be used as a predictor for distinguishing IPAs from preinvasive lesions. However, the surface area feature (P=0.040) and the extruded surface area feature (P=0.013) could be predictors of IPAs compared with MIAs. In addition, the correlation feature (P=0.046) could distinguish preinvasive lesions from MIAs better.
conclusionsPreinvasive lesions, MIAs and IPAs can be discriminated based on texture features within CT images, although the three diseases could all appear as GGOs on CT images. The diagnoses of these three diseases are very important for clinical surgery.
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