ReviewRadiology2023
Expanding Role of Advanced Image Analysis in CT-detected Indeterminate Pulmonary Nodules and Early Lung Cancer Characterization.
Review in Radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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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
21 citing papers in PubMed, 31 citations in OpenAlex.
- A radiomics-based nomogram for preoperatively predicting the invasiveness of nodular lung adenocarcinoma: a multicenter study.Translational lung cancer research · 2026Article
- Artificial Intelligence in Lung Cancer: A Narrative Review of Recent Advances in Diagnosis, Biomarker Discovery, and Drug Development.Pharmaceutics · 2026Review
- Integrating central-slice anatomy and MRF-mapped radiomics dynamics for lung adenocarcinoma subtyping.Quantitative imaging in medicine and surgery · 2026Article
- Topologically distinct 2D and 3D intratumoral heterogeneity scores for preoperatively predicting invasiveness in stage I lung adenocarcinoma: A multicenter study.PLOS digital health · 2026Article
- YOLOv8-BCD: a real-time deep learning framework for pulmonary nodule detection in computed tomography imaging.Quantitative imaging in medicine and surgery · 2025Article
- AI-based large-scale screening of gastric cancer from noncontrast CT imaging.Nature medicine · 2025Observational
- Bronchiectasis and airspace enlargement surrounding the lung nodule in dual-energy CT pulmonary angiography: comparison between iodine map and monochromatic image.Radiological physics and technology · 2025Article
- Biomarkers for the diagnosis of indeterminate pulmonary nodules: are we there yet?Journal of thoracic disease · 2025Review
- Multiple Indeterminate pulmonary nodules (IPNs) as independent prognostic indicators in pediatric osteosarcoma: A ten-year retrospective study.Journal of bone oncology · 2025Article
- Assessments of lung nodules by an artificial intelligence chatbot using longitudinal CT images.Cell reports. Medicine · 2025Article
- RNA analysis of patients with benign and malignant pulmonary nodules.Oncology letters · 2025Article
- Spectral dual-layer detector CT-based radiomics-deep learning for predicting pathological aggressiveness of stage I lung adenocarcinoma: discrimination of precursor glandular lesions and invasive adenocarcinomas.Translational lung cancer research · 2025Article
- Fine-Tuned Large Language Model for Extracting Patients on Pretreatment for Lung Cancer from a Picture Archiving and Communication System Based on Radiological Reports.Journal of imaging informatics in medicine · 2025Article
- Imaging features and clinical markers for predicting postoperative recurrence in early-stage lung cancer.American journal of cancer research · 2025Article
- Discriminating Central Lung Cancer Tumors from Atelectasis using Radiomics Analysis on Contrast-free CTCurrent medical imaging · 2025Article
- Diagnostic value of combined CT artificial intelligence (AI) system and lung cancer biomarkers in pulmonary nodule evaluation.AIMS public health · 2025Article
- Ultra-low-dose chest computed tomography with model-based iterative reconstruction in the analysis of solid pulmonary nodules: A prospective study.World journal of radiology · 2024Article
- Data-driven risk stratification and precision management of pulmonary nodules detected on chest computed tomography.Nature medicine · 2024Article
- Leveraging Computed Tomography Imaging to Detect Chronic Obstructive Pulmonary Disease and Concomitant Chronic Diseases.American journal of respiratory and critical care medicine · 2024Article
- Prevalence and management of pulmonary nodules: a systematic review and meta-analysis.Journal of thoracic disease · 2024Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
The implementation of low-dose chest CT for lung screening presents a crucial opportunity to advance lung cancer care through early detection and interception. In addition, millions of pulmonary nodules are incidentally detected annually in the United States, increasing the opportunity for early lung cancer diagnosis. Yet, realization of the full potential of these opportunities is dependent on the ability to accurately analyze image data for purposes of nodule classification and early lung cancer characterization. This review presents an overview of traditional image analysis approaches in chest CT using semantic characterization as well as more recent advances in the technology and application of machine learning models using CT-derived radiomic features and deep learning architectures to characterize lung nodules and early cancers. Methodological challenges currently faced in translating these decision aids to clinical practice, as well as the technical obstacles of heterogeneous imaging parameters, optimal feature selection, choice of model, and the need for well-annotated image data sets for the purposes of training and validation, will be reviewed, with a view toward the ultimate incorporation of these potentially powerful decision aids into routine clinical practice.
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Registered trials
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.