ArticleRadiology. Imaging cancer2021
Assisted versus Manual Interpretation of Low-Dose CT Scans for Lung Cancer Screening: Impact on Lung-RADS Agreement.
Article in Radiology. Imaging cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed, 3 syntheses or guidelines pooled it, 25 citations in OpenAlex.
- Lung cancer screening with low-dose CT: definition of positive, indeterminate, and negative screen results. A nodule management recommendation from the European Society of Thoracic Imaging.European radiology · 2026Guideline
- Software with artificial intelligence-derived algorithms for detecting and analysing lung nodules in CT scans: systematic review and economic evaluation.Health technology assessment (Winchester, England) · 2025Pooled it
- Pooled it
- Pulmonary nodules: classification, biomarkers, signaling pathways, diseases, early diagnosis and management.Signal transduction and targeted therapy · 2026Review
- AI for screening in healthcare: promise and challenges.Abdominal radiology (New York) · 2026Review
- Review
- Opportunistic Screening on Chest CT, From theAJR. American journal of roentgenology · 2026Review
- Clinical applications of deep learning in distinguishing benign from malignant pulmonary nodules in computed tomography scans.Archives of medical science : AMS · 2026Article
- Artificial Intelligence-Empowered Radiology-Current Status and Critical Review.Diagnostics (Basel, Switzerland) · 2025Review
- Errors in Radiology: A Standard Review.Journal of clinical medicine · 2024Review
- Translating potential improvement in the precision and accuracy of lung nodule measurements on computed tomography scans by software derived from artificial intelligence into impact on clinical practice-a simulation study.BJR artificial intelligence · 2024Article
- Application of computer-aided detection for NCCN-based follow-up recommendation in subsolid nodules: Effect on inter-observer agreement.Cancer medicine · 2024Article
- Pricing and cost-saving potential for deep-learning computer-aided lung nodule detection software in CT lung cancer screening.Insights into imaging · 2023Article
- Deep learning for the detection of benign and malignant pulmonary nodules in non-screening chest CT scans.Communications medicine · 2023Article
- A Synthesizing Semantic Characteristics Lung Nodules Classification Method Based on 3D Convolutional Neural Network.Bioengineering (Basel, Switzerland) · 2023Article
- Artificial intelligence in lung cancer: current applications and perspectives.Japanese journal of radiology · 2023Review
- Clinical Scores, Biomarkers and IT Tools in Lung Cancer Screening-Can an Integrated Approach Overcome Current Challenges?Cancers · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 1 institution in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose To compare the inter- and intraobserver agreement and reading times achieved when assigning Lung Imaging Reporting and Data System (Lung-RADS) categories to baseline and follow-up lung cancer screening studies by using a dedicated CT lung screening viewer with integrated nodule detection and volumetric support with those achieved by using a standard picture archiving and communication system (PACS)-like viewer. Materials and Methods Data were obtained from the National Lung Screening Trial (NLST). By using data recorded by NLST radiologists, scans were assigned to Lung-RADS categories. For each Lung-RADS category (1 or 2, 3, 4A, and 4B), 40 CT scans (20 baseline scans and 20 follow-up scans) were randomly selected for 160 participants (median age, 61 years; interquartile range, 58-66 years; 61 women) in total. Seven blinded observers independently read all CT scans twice in a randomized order with a 2-week washout period: once by using the standard PACS-like viewer and once by using the dedicated viewer. Observers were asked to assign a Lung-RADS category to each scan and indicate the risk-dominant nodule. Inter- and intraobserver agreement was analyzed by using Fleiss κ values and Cohen weighted κ values, respectively. Reading times were compared by using a Wilcoxon signed rank test. Results The interobserver agreement was moderate for the standard viewer and substantial for the dedicated viewer, with Fleiss κ values of 0.58 (95% CI: 0.55, 0.60) and 0.66 (95% CI: 0.64, 0.68), respectively. The intraobserver agreement was substantial, with a mean Cohen weighted κ value of 0.67. The median reading time was significantly reduced from 160 seconds with the standard viewer to 86 seconds with the dedicated viewer (
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.