ArticleBioscience reports2022
Identification of pathological subtypes of early lung adenocarcinoma based on artificial intelligence parameters and CT signs.
Article in Bioscience reports, 2022. 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 infiltration risk prediction models by logistic regression for ground-glass pulmonary nodules: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- Artificial intelligence construction: a review of the bridge between CT imaging features of lung ground-glass nodules adenocarcinoma and carcinogenic driver genes.Journal of cancer research and clinical oncology · 2026Review
- Clinical characteristics of ground-glass opacities in young patients : a single-center retrospective study.Journal of cardiothoracic surgery · 2026Article
- Artificial intelligence-based density proportion analysis in predicting the invasiveness of neoplastic ground-glass nodules.Translational lung cancer research · 2026Article
- Imaging-based development and validation of artificial intelligence models for lung adenocarcinoma precursor lesions and early lung adenocarcinoma presenting as pulmonary nodules.Frontiers in artificial intelligence · 2026Article
- Application of an improved YOLOv5s-based deep learning model for automated detection of pulmonary adenocarcinomaFrontiers in medicine · 2026Article
- Exploring the risk factors for the growth of pure ground-glass nodules in a 3-year follow-up period.Quantitative imaging in medicine and surgery · 2025Article
- The Current Roadmap of Lung Cancer Biology, Genomics and Racial Disparity.International journal of molecular sciences · 2025Review
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- Differentiation of early-stage tumors from benign lesions manifesting as pure ground-glass nodule: a clinical prediction study based on AI-derived quantitative parameters.Frontiers in oncology · 2025Article
- Artificial intelligence-based CT histogram parameters differentiating bronchiolar adenoma and lung adenocarcinomas: A two-center study.PloS one · 2025Article
- Can radiomics in brain magnetic resonance imaging predict the mutational status of primary lung cancer based on brain metastasis?Polish journal of radiology · 2025Article
- Progressive changes in non-neoplastic ground-glass nodules on follow-up computed tomography (CT).Quantitative imaging in medicine and surgery · 2024Article
- Quantitative analysis of imaging characteristics in lung adenocarcinoma in situ using artificial intelligence.Thoracic cancer · 2024Article
- CT-based radiomics combined with clinical features for invasiveness prediction and pathological subtypes classification of subsolid pulmonary nodules.European journal of radiology open · 2024Article
- A Novel Scale for Diagnosis of Pulmonary Ground-Glass Nodules: A Multicenter and Ambispective Cohort Study.The clinical respiratory journal · 2024Article
- Artificial intelligence-measured nodule mass for determining the invasiveness of neoplastic ground glass nodules.Quantitative imaging in medicine and surgery · 2024Article
- [Value of CT Quantitative Parameters in Prediction of Pathological Types of Lung Ground Glass Nodules].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2024Article
- Research in the application of artificial intelligence to lung cancer diagnosis.Frontiers in medicine · 2024Review
- Development and validation of a nomogram based on CT texture analysis for discriminating minimally invasive adenocarcinoma from glandular precursor lesions in sub‑centimeter pulmonary ground glass nodules.Oncology letters · 2024Article
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5 authors.
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Abstract
objectiveTo explore the value of quantitative parameters of artificial intelligence (AI) and computed tomography (CT) signs in identifying pathological subtypes of lung adenocarcinoma appearing as ground-glass nodules (GGNs).
methodsCT images of 224 GGNs from 210 individuals were collected retrospectively and classified into atypical adenomatous hyperplasia (AAH)/adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) groups. AI was used to identify GGNs and to obtain quantitative parameters, and CT signs were recognized manually. The mixed predictive model based on logistic multivariate regression was built and evaluated.
resultsOf the 224 GGNs, 55, 93, and 76 were AAH/AIS, MIA, and IAC, respectively. In terms of AI parameters, from AAH/AIS to MIA, and IAC, there was a gradual increase in two-dimensional mean diameter, three-dimensional mean diameter, mean CT value, maximum CT value, and volume of GGNs (all P<0.0001). Except for the CT signs of the location, and the tumor-lung interface, there were significant differences among the three groups in the density, shape, vacuolar signs, air bronchogram, lobulation, spiculation, pleural indentation, and vascular convergence signs (all P<0.05). The areas under the curve (AUC) of predictive model 1 for identifying the AAH/AIS and MIA and model 2 for identifying MIA and IAC were 0.779 and 0.918, respectively, which were greater than the quantitative parameters independently (all P<0.05).
conclusionAI parameters are valuable for identifying subtypes of early lung adenocarcinoma and have improved diagnostic efficacy when combined with CT signs.
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