ArticleAmerican journal of respiratory and critical care medicine2015
Noninvasive Computed Tomography-based Risk Stratification of Lung Adenocarcinomas in the National Lung Screening Trial.
Article in American journal of respiratory and critical care medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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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
38 citing papers in PubMed, 59 citations in OpenAlex.
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- Review
- A Comprehensive Review of Radiomics in Pulmonary Nodule Management: Clinical Applications and Standardization Dilemmas.Current medical imaging · 2026Review
- Long-Term Prognosis of Resected Stage 1 Lung Adenocarcinoma With Radiomics, Regionality, and Histopathologic Correlation.CHEST pulmonary · 2025Article
- Global Cancer Imaging Access: Addressing Barriers and Harnessing Innovations.Radiology. Imaging cancer · 2025Review
- Pulmonary adenocarcinoma of low malignant potential defines indolent NSCLC associated with overdiagnosis in the national lung screening trial.Cancer biomarkers : section A of Disease markers · 2025Article
- A computed tomography-based score indicative of lung cancer aggression (SILA) predicts lung adenocarcinomas with low malignant potential or vascular invasion.Cancer biomarkers : section A of Disease markers · 2025Article
- Assessing the transportability of radiomic models for lung cancer diagnosis: commercialTranslational lung cancer research · 2024Article
- Long-Term Survival and CANARY-Based Artificial Intelligence for Multifocal Lung Adenocarcinoma.Mayo Clinic proceedings. Digital health · 2024Article
- Assessment of interobserver concordance in radiomic tools for lung nodule classification, with a focus on BRODERS and SILA.Scientific reports · 2023Article
- Novel tools for early diagnosis and precision treatment based on artificial intelligence.Chinese medical journal pulmonary and critical care medicine · 2023Review
- Integrated Multi-omics Analysis of Early Lung Adenocarcinoma Links Tumor Biological Features with Predicted Indolence or Aggressiveness.Cancer research communications · 2023Article
- Integration of artificial intelligence in lung cancer: Rise of the machine.Cell reports. Medicine · 2023Review
- HLA-DR cancer cells expression correlates with T cell infiltration and is enriched in lung adenocarcinoma with indolent behavior.Scientific reports · 2021Article
- Article
- YBX1 Enhances Metastasis and Stemness by Transcriptionally Regulating MUC1 in Lung Adenocarcinoma.Frontiers in oncology · 2021Article
- Do we need to see to believe?-radiomics for lung nodule classification and lung cancer risk stratification.Journal of thoracic disease · 2020Review
- Ventilation Heterogeneity and Its Association with Nodule Formation Among Participants in the National Lung Screening Trial-A Preliminary Investigation.Academic radiology · 2020Article
- Genomic Underpinnings of Tumor Behavior inAmerican journal of respiratory and critical care medicine · 2020Article
- Artificial intelligence in cancer imaging: Clinical challenges and applications.CA: a cancer journal for clinicians · 2019Review
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11 authors at 6 institutions in 3 countries.
Funding
Abstract
rationaleScreening for lung cancer using low-dose computed tomography (CT) reduces lung cancer mortality. However, in addition to a high rate of benign nodules, lung cancer screening detects a large number of indolent cancers that generally belong to the adenocarcinoma spectrum. Individualized management of screen-detected adenocarcinomas would be facilitated by noninvasive risk stratification.
objectivesTo validate that Computer-Aided Nodule Assessment and Risk Yield (CANARY), a novel image analysis software, successfully risk stratifies screen-detected lung adenocarcinomas based on clinical disease outcomes.
methodsWe identified retrospective 294 eligible patients diagnosed with lung adenocarcinoma spectrum lesions in the low-dose CT arm of the National Lung Screening Trial. The last low-dose CT scan before the diagnosis of lung adenocarcinoma was analyzed using CANARY blinded to clinical data. Based on their parametric CANARY signatures, all the lung adenocarcinoma nodules were risk stratified into three groups. CANARY risk groups were compared using survival analysis for progression-free survival. MEASUREMENTS AND MAIN
resultsA total of 294 patients were included in the analysis. Kaplan-Meier analysis of all the 294 adenocarcinoma nodules stratified into the Good, Intermediate, and Poor CANARY risk groups yielded distinct progression-free survival curves (P < 0.0001). This observation was confirmed in the unadjusted and adjusted (age, sex, race, and smoking status) progression-free survival analysis of all stage I cases.
conclusionsCANARY allows the noninvasive risk stratification of lung adenocarcinomas into three groups with distinct post-treatment progression-free survival. Our results suggest that CANARY could ultimately facilitate individualized management of incidentally or screen-detected lung adenocarcinomas.
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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.