SynthesisJAMA2021
Screening for Lung Cancer With Low-Dose Computed Tomography: Updated Evidence Report and Systematic Review for the US Preventive Services Task Force.
Synthesis in JAMA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07675889 (Intraoperative Longitude-Latitude-Depth Three-Dimensional Localization Versus Preoperative CT-Guided Percutaneous Lung Puncture Localization for the Treatment of 0.8-2 cm Peripheral Pulmonary Nodules), which is not on this map. Cited by 367 papers, 7 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.
Intraoperative Longitude-Latitude-Depth Three-Dimensional Localization Versus Preoperative CT-Guided Percutaneous Lung Puncture Localization for the Treatment of 0.8-2 cm Peripheral Pulmonary Nodules: A Multicenter, Randomized, Open-Label, Non-Inferiority Clinical Trial
Who cites it
367 citing papers in PubMed, 7 syntheses or guidelines pooled it, 573 citations in OpenAlex.
- Prediction of oncogene mutation status in non-small cell lung cancer: a systematic review and meta-analysis with a special focus on artificial intelligence-based methods.European radiology · 2026Pooled it
- Effectiveness and implementation challenges of mobile low-dose computed tomography units for early lung cancer detection: a systematic review.Frontiers in public health · 2026Pooled it
- Large Language Models in Lung Cancer: Systematic Review.Journal of medical Internet research · 2025Pooled it
- An umbrella review of systematic evidence on the Low Dose Computed Tomography (LDCT) for lung cancer screening.Future oncology (London, England) · 2025Pooled it
- Patient barriers and facilitators to lung cancer screening uptake and intention to screen: a systematic review using the Theoretical Domains Framework.BMJ open respiratory research · 2025Pooled it
- Innovative technologies and their clinical prospects for early lung cancer screening.Clinical and experimental medicine · 2025Pooled it
- Lung Cancer Risk in US Adults with COPD: A Systematic Review and Meta-Analysis.International journal of chronic obstructive pulmonary disease · 2025Pooled it
- Performance and safety of a multi-cancer early detection test: the PATHFINDER 2 study.Nature medicine · 2026Trial
- Biomarker Detection of Lung Cancers Before Clinical Symptoms or the Use of Traditional Imaging Methods in the ITALUNG Trial.International journal of molecular sciences · 2026Trial
- Effect of Lung Cancer Screening, Smoking Cessation, and Cessation Smartphone App to Health-Related Quality of Life Among Heavy Smokers: Randomized Controlled Trial.Journal of medical Internet research · 2026Trial
- Temporal Trends in Lung Cancer and Histological Subtype Incidence and Bayesian Projections to 2030 in China: Population-Based Quantitative Study.JMIR public health and surveillance · 2026Article
- Automatic phenotyping of emergency department patients with incidental hepatic steatosis: A machine learning clustering analysis.The American journal of emergency medicine · 2026Article
- Nodule Dye-Localization Using Interventional Fluoroscopy for Image-Guided Sublobar Lung Resection.Thoracic cancer · 2026Article
- Article
- The Vanguard Study: Assessing feasibility of conducting randomized trials of novel multi-cancer detection tests.Contemporary clinical trials · 2026Article
- A novel three-CpG signature for lung adenocarcinoma diagnosis: integrating multi-omics, machine learning, and clinical validation.Translational cancer research · 2026Article
- Refining Lung Cancer Care: The Emerging Role of Liquid Biopsy in Screening, Diagnosis, and Precision Oncology.Cancers · 2026Review
- Global Burden and Temporal Trends of Early-Onset Tracheal, Bronchial, and Lung Cancer: An Analysis of GLOBOCAN 2022 and GBD 2023 Data.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Image-Domain GAN Denoising for Sn100 kVp Ultra-Low-Dose Chest CT: A Retrospective Paired Image-Quality Study.Diagnostics (Basel, Switzerland) · 2026Article
- CDR-Informed Graph Neural Network for Plasma Metabolomics in Lung Cancer and Pulmonary Neuroendocrine Neoplasms.International journal of molecular sciences · 2026Article
307 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
14 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Importance: Lung cancer is the leading cause of cancer-related death in the US. Objective: To review the evidence on screening for lung cancer with low-dose computed tomography (LDCT) to inform the US Preventive Services Task Force (USPSTF). Data Sources: MEDLINE, Cochrane Library, and trial registries through May 2019; references; experts; and literature surveillance through November 20, 2020. Study Selection: English-language studies of screening with LDCT, accuracy of LDCT, risk prediction models, or treatment for early-stage lung cancer. Data Extraction and Synthesis: Dual review of abstracts, full-text articles, and study quality; qualitative synthesis of findings. Data were not pooled because of heterogeneity of populations and screening protocols. Main Outcomes and Measures: Lung cancer incidence, lung cancer mortality, all-cause mortality, test accuracy, and harms. Results: This review included 223 publications. Seven randomized clinical trials (RCTs) (N = 86 486) evaluated lung cancer screening with LDCT; the National Lung Screening Trial (NLST, N = 53 454) and Nederlands-Leuvens Longkanker Screenings Onderzoek (NELSON, N = 15 792) were the largest RCTs. Participants were more likely to benefit than the US screening-eligible population (eg, based on life expectancy). The NLST found a reduction in lung cancer mortality (incidence rate ratio [IRR], 0.85 [95% CI, 0.75-0.96]; number needed to screen [NNS] to prevent 1 lung cancer death, 323 over 6.5 years of follow-up) with 3 rounds of annual LDCT screening compared with chest radiograph for high-risk current and former smokers aged 55 to 74 years. NELSON found a reduction in lung cancer mortality (IRR, 0.75 [95% CI, 0.61-0.90]; NNS to prevent 1 lung cancer death of 130 over 10 years of follow-up) with 4 rounds of LDCT screening with increasing intervals compared with no screening for high-risk current and former smokers aged 50 to 74 years. Harms of screening included radiation-induced cancer, false-positive results leading to unnecessary tests and invasive procedures, overdiagnosis, incidental findings, and increases in distress. For every 1000 persons screened in the NLST, false-positive results led to 17 invasive procedures (number needed to harm, 59) and fewer than 1 person having a major complication. Overdiagnosis estimates varied greatly (0%-67% chance that a lung cancer was overdiagnosed). Incidental findings were common, and estimates varied widely (4.4%-40.7% of persons screened). Conclusions and Relevance: Screening high-risk persons with LDCT can reduce lung cancer mortality but also causes false-positive results leading to unnecessary tests and invasive procedures, overdiagnosis, incidental findings, increases in distress, and, rarely, radiation-induced cancers. Most studies reviewed did not use current nodule evaluation protocols, which might reduce false-positive results and invasive procedures for false-positive results.
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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.