Observational studyChest2020
Standardized Reporting and Management of Suspicious Findings on Chest CT Imaging Is Associated With Improved Lung Cancer Diagnosis in an Observational Study.
Observational study in Chest, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis 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
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.
- Pooled it
- Article
- Using an Integrated, Digital Framework to Standardize and Expand a Multisite Lung Cancer Screening Program.JCO clinical cancer informatics · 2025Article
- Article
- Association of patient and health care organization factors with incidental nodule guidelines adherence: A multi-system observational study.Lung cancer (Amsterdam, Netherlands) · 2024Observational
- Interventions of Advanced Lung Cancer Patient Receiving Chemotherapy by Computed Tomography Image Information Data Analysis-Based Soothing Care Plans.Computational and mathematical methods in medicine · 2022Article
- An Intervention to Tag Findings Suspicious for Lung Cancer on Chest Computed Tomography Has Good Sensitivity and Number Needed to Diagnose.The Permanente journal · 2021Article
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
backgroundFollow-up of chest CT scan findings suspicious for lung cancer may be delayed because of inadequate documentation. Standardized reporting and follow-up may reduce time to diagnosis and care for lung cancer. STUDY DESIGN AND
methodsWe implemented a reporting system that standardizes tagging of chest CT scan reports by classifying pulmonary findings. The system also automates referral of patients with findings suspicious for lung cancer to a multidisciplinary care team for rapid review and follow-up. The system was designed to reduce the time to diagnosis, particularly for early-stage lung cancer. We evaluated the effectiveness of this system, using a quasi-experimental stepped wedge cluster design, examining 99,148 patients who underwent diagnostic (nonscreening) chest CT imaging from 2015 to 2017 and who had not received a chest CT scan in the preceding 24 months. We evaluated the association of the intervention with the incidence of diagnosis and surgical treatment of early-stage (I, II) and late-stage (III, IV) lung cancer within 120 days of chest CT imaging.
resultsForty percent of patients received the intervention. Among 2,856 patients (2.9%) who received diagnoses of lung cancer, 28% had early-stage disease. In multivariable analyses, the intervention was associated with 24% greater odds of early-stage diagnosis (OR, 1.24; 95% CI, 1.09-1.41) and no change in the odds of late-stage diagnosis (OR, 1.04; 95% CI, 0.95-1.14). The intervention was not associated with the rate of surgical treatment within 120 days.
interpretationIn this large quasi-experimental community-based observational study, implementation of a system that combines standardized tagging of chest CT scan reports with clinical navigation was effective for increasing the diagnosis of early-stage lung cancer.
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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.