ArticleJournal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer2016
Predicting Malignant Nodules from Screening CT Scans.
Article in Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer, 2016. 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 157 papers, 4 of them syntheses that pooled it.
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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
157 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Quality and accuracy of radiomics models in predicting KRAS status in lung cancer: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
- Predictive value of radiomic features extracted from primary lung adenocarcinoma in forecasting thoracic lymph node metastasis: a systematic review and meta-analysis.BMC pulmonary medicine · 2024Pooled it
- Lung cancer risk prediction models based on pulmonary nodules: A systematic review.Thoracic cancer · 2022Pooled it
- The application of radiomics in laryngeal cancer.The British journal of radiology · 2021Pooled it
- Trial
- A predictive nomogram for two-year growth of CT-indeterminate small pulmonary nodules.European radiology · 2022Trial
- Noninvasive Method for Predicting the Expression of Ki67 and Prognosis in Non-Small-Cell Lung Cancer Patients: Radiomics.Journal of healthcare engineering · 2022Trial
- Improving prognostic performance in resectable pancreatic ductal adenocarcinoma using radiomics and deep learning features fusion in CT images.Scientific reports · 2021Trial
- Revealing Tumor Habitats from Texture Heterogeneity Analysis for Classification of Lung Cancer Malignancy and Aggressiveness.Scientific reports · 2019Trial
- Trial
- Radiomics machine learning models for lung cancer early diagnosis in heterogenous multicentre chest CT data: LIBRA study results.European radiology experimental · 2026Article
- Article
- CT-Derived Radiomic Features for the Non-Invasive Differentiation of Mediastinal Lymphadenopathy in Lung Cancer and Sarcoidosis.Biomedicines · 2026Article
- Classifiers Combined with DenseNet Models for Lung Cancer Computed Tomography Image Classification: A Comparative Analysis.Current medical imaging · 2025Article
- Preoperative Prediction of Macrotrabecular-Massive Hepatocellular Carcinoma Using Machine Learning-Based Ultrasomics.Journal of hepatocellular carcinoma · 2025Article
- A meta-learning-based robust federated learning for diagnosing lung adenocarcinoma and tuberculosis granulomas.Frontiers in oncology · 2025Article
- From images to clinical insights: an educational review on radiomics in lung diseases.Breathe (Sheffield, England) · 2025Review
- Liquid Biopsy for Medical Imaging Analysis in Cancer Diagnosis.Current pharmaceutical design · 2025Review
- Annotated test-retest dataset of lung cancer CT scan images reconstructed at multiple imaging parameters.Scientific data · 2024Article
- Enhancing the differential diagnosis of small pulmonary nodules: a comprehensive model integrating plasma methylation, protein biomarkers, and LDCT imaging features.Journal of translational medicine · 2024Observational
97 more citing papers are in PubMed but not listed here.
Corrections and comments
- Commented on by
- Erratum issuedErratum.2018
Authors and funding
14 authors.
Funding
Abstract
objectivesThe aim of this study was to determine whether quantitative analyses ("radiomics") of low-dose computed tomography lung cancer screening images at baseline can predict subsequent emergence of cancer.
methodsPublic data from the National Lung Screening Trial (ACRIN 6684) were assembled into two cohorts of 104 and 92 patients with screen-detected lung cancer and then matched with cohorts of 208 and 196 screening subjects with benign pulmonary nodules. Image features were extracted from each nodule and used to predict the subsequent emergence of cancer.
resultsThe best models used 23 stable features in a random forests classifier and could predict nodules that would become cancerous 1 and 2 years hence with accuracies of 80% (area under the curve 0.83) and 79% (area under the curve 0.75), respectively. Radiomics outperformed the Lung Imaging Reporting and Data System and volume-only approaches. The performance of the McWilliams risk assessment model was commensurate.
conclusionsThe radiomics of lung cancer screening computed tomography scans at baseline can be used to assess risk for development of cancer.
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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.