ArticleRadiology2024
AI for Multistructure Incidental Findings and Mortality Prediction at Chest CT in Lung Cancer Screening.
Article in Radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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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
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Comprehensive application of artificial intelligence in preserved ratio impaired spirometry: A systematic literature review.PloS one · 2026Pooled it
- Automated AI-Based Aortic Measurements From Attenuation Correction CT as an Adjunctive Cardiovascular Risk Biomarker: An International Multicenter Study.Circulation. Cardiovascular imaging · 2026Article
- Optimizing surgical decision-making for lung adenocarcinoma by preoperatively identifying pathological high-risk factors: retrospective development and prospective validation of a deep learning model.International journal of surgery (London, England) · 2026Article
- Applying artificial intelligence to ensure high quality and equitable lung cancer screening.Translational lung cancer research · 2026Review
- A multicenter study of automatic segmentation-based multimodal fusion integrating radiomics, deep learning, and clinical parameters for prostate cancer detection.Abdominal radiology (New York) · 2026Article
- Prognostication and clinical opportunities with AI for coronary artery calcium: a scoping review.BMJ digital health & AI · 2026Article
- Spatial associations between esophageal lesions and surrounding tissues in esophageal fistula: a CAM-guided radiomics study.Frontiers in oncology · 2026Article
- Artificial Intelligence and Machine Learning in Lung Cancer: Advances in Imaging, Detection, and Prognosis.Cancers · 2025Review
- Predictive modeling & mechanistic validation of synergistic pimodivir combinations for anti-influenza therapy via PB2cap affinity boost.NPJ digital medicine · 2025Article
- Pros and cons of reporting incidental findings in lung cancer screening.European radiology · 2025Review
- AI-Based screening for thoracic aortic aneurysms in routine breast MRI.Nature communications · 2025Article
- Development and validation of machine learning models based on molecular features for estimating the probability of multiple primary lung carcinoma versus intrapulmonary metastasis in patients presenting multiple non-small cell lung cancers.Translational lung cancer research · 2025Article
- The Role of Computed Tomography and Artificial Intelligence in Evaluating the Comorbidities of Chronic Obstructive Pulmonary Disease: A One-Stop CT Scanning for Lung Cancer Screening.International journal of chronic obstructive pulmonary disease · 2025Review
- Automated chest CT three-dimensional quantification of body composition: adipose tissue and paravertebral muscle.Scientific reports · 2024Article
- Artificial intelligence: a primer for pediatric radiologists.Pediatric radiology · 2024Review
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11 authors.
Funding
Abstract
Background Incidental extrapulmonary findings are commonly detected on chest CT scans and can be clinically important. Purpose To integrate artificial intelligence (AI)-based segmentation for multiple structures, coronary artery calcium (CAC), and epicardial adipose tissue with automated feature extraction methods and machine learning to detect extrapulmonary abnormalities and predict all-cause mortality (ACM) in a large multicenter cohort. Materials and Methods In this post hoc analysis, baseline chest CT scans in patients enrolled in the National Lung Screening Trial (NLST) from August 2002 to September 2007 were included from 33 participating sites. Per scan, 32 structures were segmented with a multistructure model. For each structure, 15 clinically interpretable radiomic features were quantified. Four general codes describing abnormalities reported by NLST radiologists were applied to identify extrapulmonary significant incidental findings on the CT scans. Death at 2-year and 10-year follow-up and the presence of extrapulmonary significant incidental findings were predicted with ensemble AI models, and individualized structure risk scores were evaluated. Area under the receiver operating characteristic curve (AUC) analysis was used to evaluate the performance of the models for prediction of ACM and extrapulmonary significant incidental findings. The Pearson χ
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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.