ArticleJournal of the National Cancer Institute2019
Identification of Candidates for Longer Lung Cancer Screening Intervals Following a Negative Low-Dose Computed Tomography Result.
Article in Journal of the National Cancer Institute, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
36 citing papers in PubMed, 3 syntheses or guidelines pooled it, 56 citations in OpenAlex.
- [China National Lung Cancer Screening Guideline with Low-dose Computed Tomography (2023 Version)].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2023Guideline
- Integrated imaging and molecular analysis to decipher tumor microenvironment in the era of immunotherapy.Seminars in cancer biology · 2022Pooled it
- Lung Cancer Screening with Low-Dose CT: a Meta-Analysis.Journal of general internal medicine · 2020Pooled it
- Melatonin enhances radiofrequency-induced NK antitumor immunity, causing cancer metabolism reprogramming and inhibition of multiple pulmonary tumor development.Signal transduction and targeted therapy · 2021Trial
- Ten-year results of the Multicentric Italian Lung Detection trial demonstrate the safety and efficacy of biennial lung cancer screening.European journal of cancer (Oxford, England : 1990) · 2019Trial
- Pulmonary nodules: classification, biomarkers, signaling pathways, diseases, early diagnosis and management.Signal transduction and targeted therapy · 2026Review
- Over- and underscreening for lung cancer.Translational lung cancer research · 2026Review
- Personalised screening intensity based on existing lung cancer risk and spirometry.ERJ open research · 2026Article
- Structured narrative review on lung cancer screening: current evidence, clinical practice implications and implementation insights from a multidisciplinary task force and patient representatives.European respiratory review : an official journal of the European Respiratory Society · 2025Review
- External Testing of a Deep Learning Model for Lung Cancer Risk from Low-Dose Chest CT.Radiology · 2025Article
- The probability of lung cancer in patients with pulmonary nodules detected via low-dose computed tomography screening in China.BMC cancer · 2025Article
- A generalized health index: automated thoracic CT-derived biomarkers predict life expectancy.The British journal of radiology · 2025Article
- Current and Future Perspectives on Computed Tomography Screening for Lung Cancer: A Roadmap From 2023 to 2027 From the International Association for the Study of Lung Cancer.Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer · 2024Review
- Serum-based biomarkers associated with lung cancer risk and cause-specific mortality in the German randomized Lung Cancer Screening Intervention (LUSI) trial.Translational lung cancer research · 2023Article
- Review
- Recalibration of a Deep Learning Model for Low-Dose Computed Tomographic Images to Inform Lung Cancer Screening Intervals.JAMA network open · 2023Article
- Risk Model-Based Lung Cancer Screening : A Cost-Effectiveness Analysis.Annals of internal medicine · 2023Article
- Evaluation of benefits and harms of adaptive screening schedules for lung cancer: A microsimulation study.Journal of medical screening · 2022Article
- Multistate models for the natural history of cancer progression.British journal of cancer · 2022Article
- Lung function impairment in the German Lung Cancer Screening Intervention Study (LUSI): prevalence, symptoms, and associations with lung cancer risk, tumor histology and all-cause mortality.Translational lung cancer research · 2022Article
Corrections and comments
- Commented on by
- Commented on by
Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
Lengthening the annual low-dose computed tomography (CT) screening interval for individuals at lowest risk of lung cancer could reduce harms and improve efficiency. We analyzed 23 328 participants in the National Lung Screening Trial who had a negative CT screen (no ≥4-mm nodules) to develop an individualized model for lung cancer risk after a negative CT. The Lung Cancer Risk Assessment Tool + CT (LCRAT+CT) updates "prescreening risk" (calculated using traditional risk factors) with selected CT features. At the next annual screen following a negative CT, risk of cancer detection was reduced among the 70% of participants with neither CT-detected emphysema nor consolidation (median risk = 0.2%, interquartile range [IQR] = 0.1%-0.3%). However, risk increased for the 30% with CT emphysema (median risk = 0.5%, IQR = 0.3%-0.8%) and the 0.6% with consolidation (median = 1.6%, IQR = 1.0%-2.5%). As one example, a threshold of next-screen risk lower than 0.3% would lengthen the interval for 57.8% of screen-negatives, thus averting 49.8% of next-screen false-positives among screen-negatives but delaying diagnosis for 23.9% of cancers. Our results support that many, but not all, screen-negatives might reasonably lengthen their CT screening interval.
Indexed as
Identifiers
What OpenQuestion holds
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.