ArticleQuantitative imaging in medicine and surgery2023
Artificial intelligence system-based histogram analysis of computed tomography features to predict tumor invasiveness of ground-glass nodules.
Article in Quantitative imaging in medicine and surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Scientific evidence of commercial artificial intelligence products for pulmonary nodule assessment on CT scans: a systematic review.European radiology · 2026Article
- TabPFN-driven ternary classification of stage IA lung adenocarcinoma subtypes using AI-derived histogram features a retrospective multicenter cohort study.International journal of surgery (London, England) · 2026Article
- Artificial intelligence-based density proportion analysis in predicting the invasiveness of neoplastic ground-glass nodules.Translational lung cancer research · 2026Article
- Integrating CT-based radiomics and deep learning for invasive prediction of ground-glass nodules in lung adenocarcinoma: a multicohort study.Insights into imaging · 2025Article
- 3D computed tomography density of stage T1 lung cancer, which value is most stable for predicting its invasiveness?Quantitative imaging in medicine and surgery · 2025Article
- Clinical manifestations of SARS-CoV-2 Omicron infection is associated with the stage of liver cirrhosis.BMC infectious diseases · 2025Article
- Assessment of intratumor heterogeneity for preoperatively predicting the invasiveness of pulmonary adenocarcinomas manifesting as pure ground-glass nodules.Quantitative imaging in medicine and surgery · 2025Article
- Predicting EGFR gene mutation in lung adenocarcinoma using spectral CT combined with AI parameters: a diagnostic accuracy study.Frontiers in oncology · 2025Article
- 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
- Artificial intelligence-measured nodule mass for determining the invasiveness of neoplastic ground glass nodules.Quantitative imaging in medicine and surgery · 2024Article
- Inter-observer consistency on subsolid nodule follow-up recommendation based on National Comprehensive Cancer Network (NCCN) guidelines in low-dose computed tomography (LDCT) lung cancer screening.Quantitative imaging in medicine and surgery · 2024Article
- Imaging diagnostics of pulmonary ground-glass nodules: a narrative review with current status and future directions.Quantitative imaging in medicine and surgery · 2024Review
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Abstract
Background: The use of an artificial intelligence (AI)-based diagnostic system can significantly aid in analyzing the histogram of pulmonary nodules. The aim of our study was to evaluate the value of computed tomography (CT) histogram indicators analyzed by AI in predicting the tumor invasiveness of ground-glass nodules (GGNs) and to determine the added value of contrast-enhanced CT (CECT) compared with nonenhanced CT (NECT) in this prediction. Methods: This study enrolled patients with persistent GGNs who underwent preoperative NECT and CECT scanning. AI-based histogram analysis was performed for pathologically confirmed GGNs, which was followed by screening invasiveness-related factors via univariable analysis. Multivariable logistic models were developed based on candidate CT histogram indicators measured on either NECT or CECT. Receiver operating characteristic (ROC) curve and precision-recall (PR) curve were used to evaluate the models' performance. Results: A total of 116 patients comprising 121 GGNs were included and divided into the precancerous lesion and adenocarcinoma groups based on invasiveness. In the AI-based histogram analysis, the mean CT value [NECT: odds ratio (OR) =1.009; 95% confidence interval (CI): 1.004-1.013; P<0.001] and solid component volume (NECT: OR =1.005; 95% CI: 1.000-1.010; P=0.032) were associated with the adenocarcinoma and used for multivariable logistic modeling. The area under ROC curve (AUC) and PR curve (AUPR) were not significantly different between the NECT model (AUC =0.765, 95% CI: 0.679-0.837; AUPR =0.907, 95% CI: 0.825-0.953) and the optimal CECT model (delayed phase: AUC =0.772, 95% CI: 0.687-0.843; AUPR =0.895, 95% CI: 0.812-0.944). No significantly different metrics were observed between the NECT and CECT models (precision: 0.707 Conclusions: The AI diagnostic system can help in the diagnosis of GGNs. The system displayed decent performance in GGN detection and alert to malignancy. Mean CT value and solid component volume were independent predictors of tumor invasiveness. CECT provided no additional improvement in diagnostic performance as compared with NECT.
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