ArticleTranslational cancer research2023
CT-based three-dimensional invasiveness analysis of adenocarcinoma presenting as pure ground-glass nodules.
Article in Translational cancer research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 9 citations in OpenAlex.
- Artificial intelligence-based density proportion analysis in predicting the invasiveness of neoplastic ground-glass nodules.Translational lung cancer research · 2026Article
- Quantitative CT analysis for predicting the PD-L1 expression in lung adenocarcinoma.Japanese journal of radiology · 2026Article
- Article
- Nomogram integrating clinical-radiological and radiomics features for differentiating invasive from non-invasive pulmonary adenocarcinomas presenting as ground-glass nodules.American journal of cancer research · 2025Article
- Quantitative analysis of imaging characteristics in lung adenocarcinoma in situ using artificial intelligence.Thoracic cancer · 2024Article
- The value of predicting the invasiveness and degree of infiltration of pulmonary ground-glass nodules based on computed tomography features and enhanced quantitative analysis.Quantitative imaging in medicine and surgery · 2024Article
- Lung Cancer Staging: Imaging and Potential Pitfalls.Diagnostics (Basel, Switzerland) · 2023Review
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Authors and funding
7 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: We invest computed tomography (CT) image differences between non-invasive adenocarcinomas (NIAs) and invasive adenocarcinomas (IAs) presenting as pure ground glass nodules (GGNs). Methods: From 2013 to 2019, 48 pure GGNs were surgically resected in 45 patients. Of these, 40 were pathologically diagnosed as non-small cell lung cancers (NSCLCs). We assessed them using the Synapse Vincent (Fujifilm Co., Ltd., Tokyo, Japan) three-dimensional (3D) analysis system; we drew histograms of the CT densities. We calculated the maximum, minimum, means, and standard deviations of the densities. The proportions of GGNs of high CT density were compared between the two groups. The diagnostic performance was investigated via receiver operating curve (ROC) analysis. Results: Of the 40 pure GGNs, 20 were NIAs (4 adenocarcinomas Conclusions: CT density reflected the invasiveness of pure GGNs. A CT volume proportion density >-300 Hounsfield units may significantly predict histological invasiveness.
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