Evidence map›Paper›PMID 40416878›Full record

ArticleFrontiers in oncology2025

Diagnostic accuracy of rapid on-site evaluation in subtyping lung cancer via bronchoscopic biopsy.

Shuang Yan, Hua Jiang, Li Gong, Lei Pan, Faguang Jin

Abstract read
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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.

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2citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Shuang YanDepartment of Pulmonary and Critical Care Medicine, Tangdu Hospital, Air Force Medical University, Xi'an, China.
Hua JiangDepartment of Pulmonary and Critical Care Medicine, Tangdu Hospital, Air Force Medical University, Xi'an, China.
Li GongDepartment of Pathology, Tangdu Hospital, Air Force Medical University, Xi'an, China.
Lei Pan *Department of Pulmonary and Critical Care Medicine, Tangdu Hospital, Air Force Medical University, Xi'an, China.
Faguang Jin *Department of Pulmonary and Critical Care Medicine, Tangdu Hospital, Air Force Medical University, Xi'an, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

bronchoscopic biopsyendobronchial ultrasound-guided transbronchial needle aspirationlung cancerpathological typingrapid on-site evaluation

Identifiers

PMID40416878
PMCPMC12098460

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