ArticleFrontiers in oncology2020
Radiomic-Based Quantitative CT Analysis of Pure Ground-Glass Nodules to Predict the Invasiveness of Lung Adenocarcinoma.
Erratum issuedOpen access · goldAbstract read
In one paragraph
Article in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 38 papers.
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38citing papers in PubMed
3.8field-weighted citation impact, top 5% of its field
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
38 citing papers in PubMed, 45 citations in OpenAlex.
- Computed tomography radiomics-based distinction of invasive adenocarcinoma from minimally invasive adenocarcinoma manifesting as pure ground-glass nodules with bubble-like signs.General thoracic and cardiovascular surgery · 2022Trial
- A computerized tomography-based radiomic model for assessing the invasiveness of lung adenocarcinoma manifesting as ground-glass opacity nodules.Respiratory research · 2022Trial
- Development and validation of a radiomics-habitat model for preoperatively predicting poorly differentiated stage IA lung adenocarcinoma.Journal of thoracic disease · 2026Article
- Radiomic model with SHAP-based interpretability for predicting invasiveness of pure ground-glass nodules: a retrospective study based on high-resolution computed tomography (HRCT) volumetric datasets.Journal of cardiothoracic surgery · 2026Article
- CT-based interpretable delta-radiomics model for risk stratification of pulmonary ground-glass nodules: a multicentre study.Insights into imaging · 2026Article
- Advancing lung adenocarcinoma diagnosis using peri-nodular features: a CT radiomics study for determining invasiveness in sub-centimeter pure ground glass nodules.Journal of thoracic disease · 2026Article
- 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
- A radiomics nomogram integrated with radiological features for preoperative prediction of lung nodule invasiveness: comparison with Lung-RADS.Translational lung cancer research · 2026Article
- Quantifying Early-Stage Lung Adenocarcinoma Progression with a Radiomic Trajectory.NPJ digital medicine · 2025Article
- An integrated strategy based on radiomics and quantum machine learning: diagnosis and clinical interpretation of pulmonary ground-glass nodules.BMC medical imaging · 2025Article
- Precision Medicine in Lung Cancer Screening: A Paradigm Shift in Early Detection-Precision Screening for Lung Cancer.Diagnostics (Basel, Switzerland) · 2025Review
- The influence of image selection and segmentation on the extraction of lung cancer imaging radiomics features using 3D-Slicer software.BMC cancer · 2025Article
- MRI radiomics based on machine learning in high-grade gliomas as a promising tool for prediction of CD44 expression and overall survival.Scientific reports · 2025Article
- A nomogram model integrating radiomics and clinical variables to predict napsin a expression in lung adenocarcinoma patients.Frontiers in oncology · 2025Article
- Computed tomography-derived radiomics models for distinguishing difficult-to-diagnose inflammatory and malignant pulmonary nodules.Biomedical engineering and computational biology · 2025Article
- 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
- Development of predictive models for assessing the progression and invasiveness of satellite lesions in patients with multiple pulmonary ground glass nodules.Journal of thoracic disease · 2023Article
- Multi-classification model incorporating radiomics and clinic-radiological features for predicting invasiveness and differentiation of pulmonary adenocarcinoma nodules.Biomedical engineering online · 2023Article
- Radiomics nomogram for the prediction of Ki-67 index in advanced non-small cell lung cancer based on dual-phase enhanced computed tomography.Journal of cancer research and clinical oncology · 2023Article
- An ensemble deep learning model for risk stratification of invasive lung adenocarcinoma using thin-slice CT.NPJ digital medicine · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
10 authors at 5 institutions in 1 country.
Fangyi XuDepartment of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Wenchao ZhuDepartment of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Yao ShenDepartment of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Jian WangDepartment of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, China.
Rui XuDUT-RU International School of Information Science & Engineering, Dalian University of Technology, Dalian, China.
Chooah QuteshDepartment of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Lijiang SongDepartment of Cardiothoracic Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Yi GanDepartment of Pathology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Cailing PuDepartment of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Hongjie HuDepartment of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Zhejiang University · CNSir Run Run Shaw Hospital · CNCenter for High Pressure Science & Technology Advanced Research · CNNingbo University · CNTongde Hospital of Zhejiang Province · CN
Funding
No grant is acknowledged in the PubMed record.
Abstract
PubMed holds no abstract for this paper.
Indexed as
adenocarcinomacomputed tomographylung cancermachine learningradiomics
Identifiers
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Read underepoch 390
Registered trials
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.