ReviewCureus2016
To Screen or not to Screen: Low Dose Computed Tomography in Comparison to Chest Radiography or Usual Care in Reducing Morbidity and Mortality from Lung Cancer.
Review in Cureus, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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
6 citing papers in PubMed, 18 citations in OpenAlex.
- Article
- Development of Debiasing Technique for Lung Nodule Chest X-ray Datasets to Generalize Deep Learning Models.Sensors (Basel, Switzerland) · 2023Article
- Comprehensive analysis of NGS and ARMS-PCR for detecting EGFR mutations based on 4467 cases of NSCLC patients.Journal of cancer research and clinical oncology · 2022Article
- EGFR Gene Mutation and Methodological Evaluation in 399 Patients with Non-small Cell Lung Cancer.Current medical science · 2020Article
- Physician's awareness of lung cancer screening and its related medical radiation exposure in Korea.Epidemiology and health · 2018Article
- Screening and Biosensor-Based Approaches for Lung Cancer Detection.Sensors (Basel, Switzerland) · 2017Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung cancer has the highest mortality rate of all cancers. This paper seeks to address the question: Can the mortality of lung cancer be decreased by screening with low-dose computerized tomography (LDCT) in higher risk patients compared to chest X-rays (CXR) or regular patient care? Currently, CXR screening is recommended for certain high-risk patients. Several recent trials have examined the effectiveness of LDCT versus chest radiography or usual care as a control. These trials include National Lung Screening Trial (NLST), Detection And screening of early lung cancer with Novel imaging TEchnology (DANTE), Lung Screening Study (LSS), Depiscan, Italian Lung (ITALUNG), and Dutch-Belgian Randomized Lung Cancer Screening Trial (Dutch acronym: NELSON study). NLST, the largest trial (n=53, 454), demonstrated a decrease in mortality from lung cancer in the LDCT group (RRR=20%, P=0.004). LSS demonstrated a greater sensitivity in detecting both early stage and any stage of lung cancer in comparison to traditional CXR. Although the DANTE trial yielded data consistent with findings in LSS, it also showed that via LDCT screening a greater proportion of patients were placed under unnecessary surgical procedures. The Depiscan trial yielded a high nodule detection rate at the cost of a high false-positive rate compared to CXR screening. The ITALUNG and NELSON trials demonstrated the early detection capabilities of LDCT for lung cancers compared to usual care without surveillance imaging. False-positive findings with unnecessary workup, intervention, and radiation exposure remain significant concerns for routine LDCT screening. However, current data suggests LDCT may provide a highly sensitive and specific means for detecting lung cancers and reducing mortality.
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.