ArticleQuantitative imaging in medicine and surgery2024
Artificial intelligence-measured nodule mass for determining the invasiveness of neoplastic ground glass nodules.
Article in Quantitative imaging in medicine and surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Scientific evidence of commercial artificial intelligence products for pulmonary nodule assessment on CT scans: a systematic review.European radiology · 2026Article
- Narrative review: the research advances of artificial intelligence in the prediction of pulmonary nodule growth.Journal of thoracic disease · 2026Review
- Predicting invasiveness of subsolid nodules: a HRCT-based model for lung adenocarcinoma.American journal of translational research · 2026Article
- Correlation between CT growth patterns and invasiveness progression in neoplastic subcentimeter sub-solid nodules.Annals of medicine · 2025Article
- Intratumoral heterogeneity score enhances invasiveness prediction in pulmonary ground-glass nodules via stacking ensemble machine learning.Insights into imaging · 2025Article
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6 authors.
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Abstract
Background: The nodule mass is an important indicator for evaluating the invasiveness of neoplastic ground-glass nodules (GGNs); however, the efficacy of nodule mass acquired by artificial intelligence (AI) has not been validated. This study thus aimed to determine the efficacy of nodule mass measured by AI in predicting the invasiveness of neoplastic GGNs. Methods: From May 2019 to September 2023, a retrospective study was conducted on 755 consecutive patients comprising 788 pathologically confirmed neoplastic GGNs, among which 259 were adenocarcinoma in situ (AIS), 282 minimally invasive adenocarcinoma (MIA), and 247 invasive adenocarcinoma (IAC). Nodule mass was quantified using AI software, and other computed tomography (CT) features were concurrently evaluated. Clinical data and CT features were compared using the Kruskal-Wallis test or Pearson chi-square test. The predictive efficacy of mass and CT features for evaluating invasive lesions (ILs) (MIAs and IACs) and IACs was analyzed and compared via receiver operating characteristic (ROC) analysis and the Delong test. Results: ROC curve analysis revealed that the optimal cutoff value of mass for distinguishing ILs and AISs was 225.25 mg [area under the curve (AUC) 0.821; 95% confidence interval 0.792-0.847; sensitivity 64.27%; specificity 89.19%; P<0.001], and for differentiating IACs from AISs and MIAs, it was 390.4 mg (AUC 0.883; 95% confidence interval 0.858-0.904; sensitivity 80.57%; specificity 86.32%; P<0.001). The efficacy of nodule mass in distinguishing ILs and AISs was comparable to that of size (P=0.2162) and significantly superior to other CT features (each P value <0.001). Additionally, the ability of nodule mass to differentiate IACs from AISs and MIAs was significantly better than that of CT features (each P value <0.001). Conclusions: AI-based nodule mass analysis is an effective indicator for determining the invasiveness of neoplastic GGNs.
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