ArticleCommunications medicine2023
Deep learning for the detection of benign and malignant pulmonary nodules in non-screening chest CT scans.
Article in Communications medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed, 66 citations in OpenAlex.
- Graph-Aware and Sequence-Aware Multimodal Deep Learning Framework for Cancer Detection and Risk Analysis from Medical Imaging.Journal of imaging · 2026Article
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- Deep learning-based malignancy probability estimation of pulmonary nodules in PET/CT imaging.European radiology · 2026Article
- Artificial intelligence predictsVeterinary pathology · 2026Article
- Insertion Network for Image Sequence Correspondence Building.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- The intersection of artificial intelligence and lung nodule research: current applications and future prospects.International journal of surgery (London, England) · 2026Article
- Accuracy and reproducibility of tumor size measurement using a deep-learning-based CDSS in resected lung cancer.PloS one · 2026Article
- Emerging technologies in thoracic surgery: artificial intelligence, robotic-assisted surgery, and telesurgery.Frontiers in surgery · 2026Review
- Diagnostic performance of artificial intelligence models for pulmonary nodule classification: a multi-model evaluation.European radiology · 2026Article
- Incidental Pulmonary Nodules: What Should We Do in 2026?Respiration; international review of thoracic diseases · 2026Review
- Deep learning-based pulmonary nodule risk assessment outperforms established malignancy risk scores in lung cancer screening.Radiology advances · 2026Article
- Applications of artificial intelligence in non-small cell lung cancer: from precision diagnosis to personalized prognosis and therapy.Journal of translational medicine · 2025Review
- Artificial Intelligence and Machine Learning in Lung Cancer: Advances in Imaging, Detection, and Prognosis.Cancers · 2025Review
- Diagnostic accuracy of AI in chest radiography for pneumonia and lung cancer: A meta-analysis.European journal of radiology open · 2025Article
- Early Detection of Lung Cancer: A Review of Innovative Milestones and Techniques.Journal of clinical medicine · 2025Review
- Review
- Artificial intelligence for the detection of airway nodules in chest CT scans.European radiology · 2025Article
- Establishing an AI-based diagnostic framework for pulmonary nodules in computed tomography.BMC pulmonary medicine · 2025Article
- Mapping the research trends and hotspots on solid pulmonary nodules from 2000-2024: insights from bibliometric analysis.Journal of thoracic disease · 2025Article
- Machine Learning and Artificial Intelligence for Infectious Disease Surveillance, Diagnosis, and Prognosis.Viruses · 2025Review
Corrections and comments
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Authors and funding
12 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOutside a screening program, early-stage lung cancer is generally diagnosed after the detection of incidental nodules in clinically ordered chest CT scans. Despite the advances in artificial intelligence (AI) systems for lung cancer detection, clinical validation of these systems is lacking in a non-screening setting.
methodWe developed a deep learning-based AI system and assessed its performance for the detection of actionable benign nodules (requiring follow-up), small lung cancers, and pulmonary metastases in CT scans acquired in two Dutch hospitals (internal and external validation). A panel of five thoracic radiologists labeled all nodules, and two additional radiologists verified the nodule malignancy status and searched for any missed cancers using data from the national Netherlands Cancer Registry. The detection performance was evaluated by measuring the sensitivity at predefined false positive rates on a free receiver operating characteristic curve and was compared with the panel of radiologists.
resultsOn the external test set (100 scans from 100 patients), the sensitivity of the AI system for detecting benign nodules, primary lung cancers, and metastases is respectively 94.3% (82/87, 95% CI: 88.1-98.8%), 96.9% (31/32, 95% CI: 91.7-100%), and 92.0% (104/113, 95% CI: 88.5-95.5%) at a clinically acceptable operating point of 1 false positive per scan (FP/s). These sensitivities are comparable to or higher than the radiologists, albeit with a slightly higher FP/s (average difference of 0.6).
conclusionsThe AI system reliably detects benign and malignant pulmonary nodules in clinically indicated CT scans and can potentially assist radiologists in this setting.
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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.