ArticleAnnals of internal medicine2020
Deep Learning Using Chest Radiographs to Identify High-Risk Smokers for Lung Cancer Screening Computed Tomography: Development and Validation of a Prediction Model.
Article in Annals of internal medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06910956 (Deep Learning Using Routine Chest X-Rays and Electronic Medical Record Data to Identify High Risk Patients for Lung Cancer Screening CT), which is not on this map. Cited by 67 papers, 3 of them syntheses that pooled it.
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.
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.
Deep Learning Using Routine Chest X-Rays and Electronic Medical Record Data to Identify High Risk Patients for Lung Cancer Screening CT
Who cites it
67 citing papers in PubMed, 3 syntheses or guidelines pooled it, 123 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
- 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
- Recent advances in artificial intelligence across interventional pulmonology: a narrative review.Journal of thoracic disease · 2026Review
- Applying artificial intelligence to ensure high quality and equitable lung cancer screening.Translational lung cancer research · 2026Review
- [Expert Consensus on Precision Management of Pulmonary Nodules (2026 Version)].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2026Article
- Current trends and future directions of artificial intelligence in lung cancer diagnosis.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- An intelligent diagnostic model for pulmonary nodules utilizing chest radiographic imagery and its application in community-based lung cancer screening.British journal of cancer · 2025Article
- Progress and challenges of artificial intelligence in lung cancer clinical translation.NPJ precision oncology · 2025Review
- Deep Learning Approaches for Automated Prediction of Treatment Response in Non-Small-Cell Lung Cancer Patients Based on CT and PET Imaging.Tomography (Ann Arbor, Mich.) · 2025Review
- Enhancing lung cancer detection through integrated deep learning and transformer models.Scientific reports · 2025Article
- Hallmarks of artificial intelligence contributions to precision oncology.Nature cancer · 2025Review
- Externally validated and clinically useful machine learning algorithms to support patient-related decision-making in oncology: a scoping review.BMC medical research methodology · 2025Article
- Multimodal integration using a machine learning approach facilitates risk stratification in HR+/HER2- breast cancer.Cell reports. Medicine · 2025Article
- Artificial Intelligence in Cancer Diagnosis: A Game-changer in Healthcare.Current pharmaceutical biotechnology · 2025Review
- Optimized deep learning approach for lung cancer detection using flying fox optimization and bidirectional generative adversarial networks.PeerJ. Computer science · 2025Article
- Role of AI in empowering and redefining the oncology care landscape: perspective from a developing nation.Frontiers in digital health · 2025Review
- Unveiling the Influence of AI on Advancements in Respiratory Care: Narrative Review.Interactive journal of medical research · 2024Review
- Emerging trends and hotspots in lung cancer-prediction models research.Annals of medicine and surgery (2012) · 2024Review
- Predicting in-hospital mortality among patients admitted with a diagnosis of heart failure: a machine learning approach.ESC heart failure · 2024Review
7 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundLung cancer screening with chest computed tomography (CT) reduces lung cancer death. Centers for Medicare & Medicaid Services (CMS) eligibility criteria for lung cancer screening with CT require detailed smoking information and miss many incident lung cancers. An automated deep-learning approach based on chest radiograph images may identify more smokers at high risk for lung cancer who could benefit from screening with CT.
objectiveTo develop and validate a convolutional neural network (CXR-LC) that predicts long-term incident lung cancer using data commonly available in the electronic medical record (EMR) (chest radiograph, age, sex, and whether currently smoking).
designRisk prediction study.
settingU.S. lung cancer screening trials.
participantsThe CXR-LC model was developed in the PLCO (Prostate, Lung, Colorectal, and Ovarian) Cancer Screening Trial ( MEASUREMENTS: Up to 12-year lung cancer incidence predicted by CXR-LC.
resultsThe CXR-LC model had better discrimination (area under the receiver-operating characteristic curve [AUC]) for incident lung cancer than CMS eligibility (PLCO AUC, 0.755 vs. 0.634; LIMITATION: Validation in lung cancer screening trials and not a clinical setting.
conclusionThe CXR-LC model identified smokers at high risk for incident lung cancer, beyond CMS eligibility and using information commonly available in the EMR. PRIMARY FUNDING SOURCE: None.
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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.