ArticleFrontiers in oncology2023
Artificial-intelligence-based computed tomography histogram analysis predicting tumor invasiveness of lung adenocarcinomas manifesting as radiological part-solid nodules.
Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A methodological framework for integrating generalisable deep learning and radiomics fusion model for early lung cancer detection across multi-centre imaging datasets.BMC medical informatics and decision making · 2026Pooled it
- Artificial intelligence-based density proportion analysis in predicting the invasiveness of neoplastic ground-glass nodules.Translational lung cancer research · 2026Article
- The Role of Artificial Intelligence in Modern Medicine: Clinical Applications, Economic Implications, and Ethical Considerations.Current pharmaceutical design · 2026Review
- Preoperative contrast-enhanced CT for predicting the macrotrabecular-massive subtype and its prognostic significance in hepatocellular carcinoma.Quantitative imaging in medicine and surgery · 2025Article
- Development and validation of a CT-based comprehensive nomogram for differentiating benign from malignant subcentimeter solid nodules.Journal of thoracic disease · 2025Article
- Low-dose ultrahigh-resolution PCCT enhances subsolid nodule characterization.La Radiologia medica · 2025Article
- Identification of the optimal threshold for predicting the infiltration degree of T1-stage lung adenocarcinoma using solid component volume and three-dimensional consolidation-to-tumor ratio in threshold segmentation.Quantitative imaging in medicine and surgery · 2025Article
- Identifying invasiveness to aid lung adenocarcinoma diagnosis using deep learning and pathomics.Scientific reports · 2025Article
- Predicting EGFR gene mutation in lung adenocarcinoma using spectral CT combined with AI parameters: a diagnostic accuracy study.Frontiers in oncology · 2025Article
- Artificial intelligence-based CT histogram parameters differentiating bronchiolar adenoma and lung adenocarcinomas: A two-center study.PloS one · 2025Article
- Artificial intelligence-measured nodule mass for determining the invasiveness of neoplastic ground glass nodules.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
- A spatio-temporal image analysis for growth of indeterminate pulmonary nodules detected by CT scan.Radiological physics and technology · 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
- A nomogram for predicting invasiveness of lung adenocarcinoma manifesting as pure ground-glass nodules: incorporating subjective CT signs and histogram parameters based on artificial intelligence.Journal of cancer research and clinical oncology · 2023Article
- Artificial intelligence system-based histogram analysis of computed tomography features to predict tumor invasiveness of ground-glass nodules.Quantitative imaging in medicine and surgery · 2023Article
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14 authors.
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Abstract
Background: Tumor invasiveness plays a key role in determining surgical strategy and patient prognosis in clinical practice. The study aimed to explore artificial-intelligence-based computed tomography (CT) histogram indicators significantly related to the invasion status of lung adenocarcinoma appearing as part-solid nodules (PSNs), and to construct radiomics models for prediction of tumor invasiveness. Methods: We identified surgically resected lung adenocarcinomas manifesting as PSNs in Peking University People's Hospital from January 2014 to October 2019. Tumors were categorized as adenocarcinoma Results: In total, 299 AIS/MIAs and 889 IACs were included. In the training cohort, multivariate logistic regression analysis demonstrated that age [odds ratio (OR), 1.020; 95% CI, 1.004-1.037; Conclusion: We developed an AI-based histogram model, which could reliably predict tumor invasiveness in lung adenocarcinoma manifesting as PSNs. This finding would provide promising value in guiding the precision management of PSNs in the daily practice.
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