ReviewJapanese journal of radiology2023
Artificial intelligence in lung cancer: current applications and perspectives.
Review in Japanese journal of radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 1 of them a synthesis that pooled it.
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Who cites it
60 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning in pulmonary nodule detection and segmentation: a systematic review.European radiology · 2025Pooled it
- Artificial Intelligence in the Training of Radiology Residents: a Multicenter Randomized Controlled Trial.Journal of cancer education : the official journal of the American Association for Cancer Education · 2025Trial
- Commercial AI for CT lung cancer screening: product capabilities, coverage of nodule management tasks and supporting evidence.European radiology · 2026Article
- Characterization of Patients with Non-Small Cell Lung Cancer Using Machine Learning Tools: Relationship with Prognostic Biomarkers and Overall Survival-A Pilot Study.Journal of clinical medicine · 2026Article
- Development and internal validation of a high-resolution computed tomography radiomics and three-dimensional deep learning diagnostic prediction model for preoperative differentiation of minimally invasive and invasive adenocarcinoma in subsolid nodules.Journal of thoracic disease · 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
- Revolutionizing lung cancer screening: the rise of artificial intelligence integrating circulating tumor markers.World journal of surgical oncology · 2026Review
- A new era of precision diagnosis and treatment for lung cancer: artificial intelligence-driven multimodal data integration and clinical applications.Cell death & disease · 2026Review
- Application of artificial intelligence in lung cancer diagnosis, therapy, and prognosis.BMC medical genomics · 2026Review
- Artificial intelligence as an independent reader of risk-dominant lung nodules: influence of CT reconstruction parameters.European radiology · 2026Article
- Review
- Application and prospect of artificial intelligence in diagnostic imaging of prostate cancer.NPJ digital medicine · 2026Review
- Artificial Intelligence in Lung Cancer: A Narrative Review of Recent Advances in Diagnosis, Biomarker Discovery, and Drug Development.Pharmaceutics · 2026Review
- LungGANDetectAI: a GAN-augmented and attention-guided deep learning framework for accurate and explainable lung cancer detection.Scientific reports · 2026Article
- Pediatric pulmonary nodules: current state of knowledge, AI applications, and future directions.Pediatric radiology · 2026Review
- Diagnostic performance of artificial intelligence models for pulmonary nodule classification: a multi-model evaluation.European radiology · 2026Article
- Comparative analysis of two-dimensional and three-dimensional machine learning models for predicting the degree of invasion in lung non-mucinous adenocarcinomas presenting as subsolid nodules.Frontiers in oncology · 2026Article
- Artificial Intelligence and Machine Learning in Lung Cancer: Advances in Imaging, Detection, and Prognosis.Cancers · 2025Review
- Deep learning-based bone removal in head and neck computed tomography angiography: a comparative study with conventional algorithms.Quantitative imaging in medicine and surgery · 2025Article
- Artificial Intelligence in Medicine.Thoracic research and practice · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) has been a very active research topic over the last years and thoracic imaging has particularly benefited from the development of AI and in particular deep learning. We have now entered a phase of adopting AI into clinical practice. The objective of this article was to review the current applications and perspectives of AI in thoracic oncology. For pulmonary nodule detection, computer-aided detection (CADe) tools have been commercially available since the early 2000s. The more recent rise of deep learning and the availability of large annotated lung nodule datasets have allowed the development of new CADe tools with fewer false-positive results per examination. Classical machine learning and deep-learning methods were also used for pulmonary nodule segmentation allowing nodule volumetry and pulmonary nodule characterization. For pulmonary nodule characterization, radiomics and deep-learning approaches were used. Data from the National Lung Cancer Screening Trial (NLST) allowed the development of several computer-aided diagnostic (CADx) tools for diagnosing lung cancer on chest computed tomography. Finally, AI has been used as a means to perform virtual biopsies and to predict response to treatment or survival. Thus, many detection, characterization and stratification tools have been proposed, some of which are commercially available.
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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.