ArticleFrontiers in oncology2025
Diagnostic accuracy of rapid on-site evaluation in subtyping lung cancer via bronchoscopic biopsy.
Article in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Annual progress in transbronchial diagnosis and treatment of pulmonary malignant tumors [2025]: a narrative review.Translational lung cancer research · 2026Review
- Clinical application of rapid on-site evaluation of technology for the diagnosis of respiratory system tumors: a retrospective study.European journal of medical research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Rapid on-site evaluation (ROSE) is a valuable technique for ensuring the adequacy of specimens during bronchoscopic biopsy; however, its diagnostic utility in lung cancer pathological classification has yet to be comprehensively assessed. Objective: To evaluate the diagnostic utility of ROSE in lung cancer and its accuracy in classifying lung cancer pathological types. Methods: A retrospective analysis was performed on 510 consecutive patients who underwent bronchoscopic biopsy with concurrent ROSE between March and July 2023. ROSE diagnoses were compared with the final pathological diagnoses to access concordance. Sensitivity analyses were conducted to evaluate concordance across cancer subtypes, lesion locations, and patient demographics. The diagnostic accuracy of ROSE in classifying lung cancer subtypes-specifically small cell lung cancer (SCLC), non-small cell lung cancer (NSCLC), squamous cell carcinoma (SCC), and adenocarcinoma (AC)-was systematically evaluated. Results: Overall concordance between ROSE and the final pathological diagnoses was 93.92% (479/510), with near-perfect agreement (k = 0.87, 95% CI: 0.83-0.92). The accuracy of ROSE in distinguishing malignant from benign lesions was significantly lower in central lesions (89.05%) compared to peripheral lesions (95.66%; p = 0.010), and in AC (89.91%) versus SCC (100%; p = 0.0027). ROSE showed high accuracy, sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) for distinguishing SCLC (95.32%, 87.50%, 97.30%, 96.86%, and 89.09%) and NSCLC (92.45%, 92.34%, 92.86%, 75.36%, and 98.09%). For SCC and AC, they were 84.91%, 89.32%, 80.73%, 88.89%, and 81.42% vs 79.72%, 69.72%, 90.29%, 73.81%, and 88.37%, respectively. Conclusion: ROSE effectively differentiates benign from malignant lesions and accurately classifies SCLC and NSCLC during bronchoscopic biopsy. While useful for preliminary subtyping of SCC and AC, its reduced sensitivity for AC and challenges in central lesion evaluation limit its utility as a standalone diagnostic tool. ROSE remains critical for optimizing biopsy workflows and reducing repeat procedures.
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.