SynthesisThoracic cancer2022
Lung cancer risk prediction models based on pulmonary nodules: A systematic review.
Synthesis in Thoracic cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 6 of them syntheses that pooled it.
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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
43 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Innovative technologies and their clinical prospects for early lung cancer screening.Clinical and experimental medicine · 2025Pooled it
- A systematic review and meta-analysis of lung cancer risk prediction models.Acta oncologica (Stockholm, Sweden) · 2025Pooled it
- Predictive performance of risk prediction models for lung cancer incidence in Western and Asian countries: a systematic review and meta-analysis.Scientific reports · 2025Pooled it
- Progress and current trends in prediction models for the occurrence and prognosis of cancer and cancer-related complications: a bibliometric and visualization analysis.Frontiers in oncology · 2025Pooled it
- Pooled it
- Lung cancer risk prediction models based on pulmonary nodules: A systematic review.Thoracic cancer · 2022Pooled it
- Development and validation of machine learning diagnostic models integrating clinical, CT, and laboratory features to differentiate lung cancer from pulmonary tuberculosis in patients with solitary pulmonary nodules: a single-center retrospective study.Translational lung cancer research · 2026Article
- Emphysema or Bronchiectasis with Pulmonary Nodules: Impact on The Risk of Malignancy.Thoracic research and practice · 2026Article
- Clinical Effectiveness of miR-760 to Distinguish Benign and Malignant Pulmonary Nodules on the Basis of Low-Dose Spiral CT Imaging.Molecular imaging and biology · 2026Article
- Integrating Clinical Features, Laboratory Biomarkers and Computed Tomography for the Discrimination of Non-Small Cell Lung Cancer and Benign Pulmonary Diseases: A Clinical Prediction Model.Journal of clinical laboratory analysis · 2026Article
- Management of suspicious nodules in lung cancer screening - a narrative review of monitoring strategies based on nodule features and further needs.Frontiers in oncology · 2026Review
- Associations of derived inflammatory, lipid, and anthropometric indices with CT-detected pulmonary nodules: a hospital-based cross-sectional study with explainable machine learning.Frontiers in endocrinology · 2026Article
- Non-targeted metabolomics reveals diagnostic biomarker in the plasma of patients with lung cancer.Oncology letters · 2026Article
- Construction and validation of a prediction model for malignant pulmonary nodules based on imaging, demographic, and laboratory features.Frontiers in oncology · 2026Article
- Large-Scale T-cell Receptor Repertoire Profiling Unveils Tumor-Specific Signals for Diagnosing Indeterminate Pulmonary Nodules.Cancer research · 2025Article
- Artificial Intelligence and Machine Learning in Lung Cancer: Advances in Imaging, Detection, and Prognosis.Cancers · 2025Review
- The probability of lung cancer in patients with pulmonary nodules detected via low-dose computed tomography screening in China.BMC cancer · 2025Article
- A Clinical-Radiomics Nomogram for the Preoperative Prediction of Aggressive Micropapillary and a Solid Pattern in Lung Adenocarcinoma.Current oncology (Toronto, Ont.) · 2025Article
- Efficacy of three lung cancer prediction models in diagnosing benign and malignant pulmonary nodules.Translational cancer research · 2025Article
- Artificial Intelligence in Thoracic Surgery: A Review Bridging Innovation and Clinical Practice for the Next Generation of Surgical Care.Journal of clinical medicine · 2025Review
Corrections and comments
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Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundScreening with low-dose computed tomography (LDCT) is an efficient way to detect lung cancer at an earlier stage, but has a high false-positive rate. Several pulmonary nodules risk prediction models were developed to solve the problem. This systematic review aimed to compare the quality and accuracy of these models.
methodsThe keywords "lung cancer," "lung neoplasms," "lung tumor," "risk," "lung carcinoma" "risk," "predict," "assessment," and "nodule" were used to identify relevant articles published before February 2021. All studies with multivariate risk models developed and validated on human LDCT data were included. Informal publications or studies with incomplete procedures were excluded. Information was extracted from each publication and assessed.
resultsA total of 41 articles and 43 models were included. External validation was performed for 23.2% (10/43) models. Deep learning algorithms were applied in 62.8% (27/43) models; 60.0% (15/25) deep learning based researches compared their algorithms with traditional methods, and received better discrimination. Models based on Asian and Chinese populations were usually built on single-center or small sample retrospective studies, and the majority of the Asian models (12/15, 80.0%) were not validated using external datasets.
conclusionThe existing models showed good discrimination for identifying high-risk pulmonary nodules, but lacked external validation. Deep learning algorithms are increasingly being used with good performance. More researches are required to improve the quality of deep learning models, particularly for the Asian population.
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