ArticleThorax2024
Radiomics analysis to predict pulmonary nodule malignancy using machine learning approaches.
Article in Thorax, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers, 2 of them syntheses that pooled it.
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Who cites it
69 citing papers in PubMed, 2 syntheses or guidelines pooled it, 69 citations in OpenAlex.
- Risk prediction for lung cancer screening: a systematic review and meta-regression.European respiratory review : an official journal of the European Respiratory Society · 2026Pooled it
- Radiomics for predicting microsatellite instability-high status in colorectal cancer: a systematic review and meta-analysis.World journal of surgical oncology · 2026Pooled it
- Moran's I-driven habitat radiomics: A biologically plausible and temporally robust approach for risk stratification of lung adenocarcinoma invasiveness.European journal of radiology open · 2026Article
- Differentiating benign from malignant pulmonary nodules in the context of bronchiectasis: a retrospective study.Annals of medicine · 2026Article
- Pulmonary nodules: classification, biomarkers, signaling pathways, diseases, early diagnosis and management.Signal transduction and targeted therapy · 2026Review
- Integrating Deep Learning of Low-Dose CT Imaging With Clinical Data for Lung Cancer Risk Prediction.Chest · 2026Article
- Review
- Explainable machine learning model for early prediction of ICU death in chronic heart failure with pulmonary infection.BMC medical informatics and decision making · 2026Article
- Reply to the Letter to the Editor: Lung cancer screening in Asian populations: from radiological convergence to mechanistic heterogeneity.European radiology · 2026Article
- Automatic segmentation-based radiomics and deep learning combined with clinical parameters for precise differentiation of lipid-poor adrenal adenomas and metastases.Abdominal radiology (New York) · 2026Article
- Body composition radiomics integrated with machine learning to predict prognosis in advanced NSCLC treated with first-line immunochemotherapy and concurrent radiotherapy.BMC pulmonary medicine · 2026Article
- Hybrid Deep Learning-Machine Learning Fusion of Clinical, Radiomic and Deep Learning Features for Preoperative Differentiation of Solitary Pulmonary Mucinous Adenocarcinoma.Diagnostics (Basel, Switzerland) · 2026Article
- When a Pulmonary Nodule Mimics Malignancy: Primary Granular Cell Tumor of the Lung.Diagnostics (Basel, Switzerland) · 2026Article
- Radiomics-based nomogram using digital radiography for early diagnosis of knee osteoarthritis.La Radiologia medica · 2026Article
- Development of a CT-based integrated model combining clinical features, radiomics, and deep learning to predict STAS in subsolid nodules of stage I lung cancer: a retrospective cohort study.Journal of thoracic disease · 2026Article
- Risk stratification of PD-L1 expression in non-small cell lung cancer: a predictive model for prognosis.Journal of thoracic disease · 2026Article
- Grayscale ultrasound radiomics for characterizing subpleural pulmonary lesions: a multicenter prospective study.Insights into imaging · 2026Article
- A new era of precision diagnosis and treatment for lung cancer: artificial intelligence-driven multimodal data integration and clinical applications.Cell death & disease · 2026Review
- Radiomics-derived classifier performance evaluation in lung nodule characterization compared with expert radiologists.Scientific reports · 2026Article
- Integrated nomogram for predicting intracranial progression-free survival with EGFR-TKI combined with cranial radiotherapy: a multicenter study.European radiology · 2026Article
9 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
15 authors at 6 institutions in 2 countries.
Funding
Abstract
backgroundLow-dose CT screening can reduce lung cancer-related mortality. However, most screen-detected pulmonary abnormalities do not develop into cancer and it often remains challenging to identify malignant nodules, particularly among indeterminate nodules. We aimed to develop and assess prediction models based on radiological features to discriminate between benign and malignant pulmonary lesions detected on a baseline screen.
methodsUsing four international lung cancer screening studies, we extracted 2060 radiomic features for each of 16 797 nodules (513 malignant) among 6865 participants. After filtering out low-quality radiomic features, 642 radiomic and 9 epidemiological features remained for model development. We used cross-validation and grid search to assess three machine learning (ML) models (eXtreme Gradient Boosted Trees, random forest, least absolute shrinkage and selection operator (LASSO)) for their ability to accurately predict risk of malignancy for pulmonary nodules. We report model performance based on the area under the curve (AUC) and calibration metrics in the held-out test set.
resultsThe LASSO model yielded the best predictive performance in cross-validation and was fit in the full training set based on optimised hyperparameters. Our radiomics model had a test-set AUC of 0.93 (95% CI 0.90 to 0.96) and outperformed the established Pan-Canadian Early Detection of Lung Cancer model (AUC 0.87, 95% CI 0.85 to 0.89) for nodule assessment. Our model performed well among both solid (AUC 0.93, 95% CI 0.89 to 0.97) and subsolid nodules (AUC 0.91, 95% CI 0.85 to 0.95).
conclusionsWe developed highly accurate ML models based on radiomic and epidemiological features from four international lung cancer screening studies that may be suitable for assessing indeterminate screen-detected pulmonary nodules for risk of malignancy.
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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.