Evidence map›Paper›PMID 42740662›Full record

ArticleJournal of Korean medical science2026

Evaluating Korean Lung-RADS Category 2b: Diagnostic Performance and Interreader Agreement in Lung Cancer Screening.

You Na Kim, Seulgi You, Joo Sung Sun, Kyung Joo Park, Seohyeon Park

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Article in Journal of Korean medical science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited 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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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

You Na KimDepartment of Radiology, Ajou University School of Medicine, Suwon, Korea.ORCID https://orcid.org/0000-0003-2771-2182
Seulgi YouDepartment of Radiology, Ajou University School of Medicine, Suwon, Korea. seulgi88322@gmail.com.ORCID https://orcid.org/0000-0003-1136-9287
Joo Sung SunDepartment of Radiology, Ajou University School of Medicine, Suwon, Korea.ORCID https://orcid.org/0000-0002-3408-8544
Kyung Joo ParkDepartment of Radiology, Ajou University School of Medicine, Suwon, Korea.ORCID https://orcid.org/0000-0002-7934-7212
Seohyeon ParkDepartment of Statistics, Korea University, Seoul, Korea.ORCID https://orcid.org/0000-0002-0320-4629

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundKorean Lung Computed Tomography Screening Reporting and Data System (Lung-RADS) category 2b was introduced to reduce false positives in a tuberculosis-endemic setting. Although its clinical validity has been partly demonstrated, interreader agreement in applying category 2b remains unclear. The aim of this study was to evaluate the diagnostic performance and interreader agreement of the modified classification.

methodsThis retrospective study included 1,371 participants who underwent low-dose chest computed tomography (CT) scans as part of the Korean national lung cancer screening program between September 2019 and January 2024 at a single tertiary hospital. Korean Lung-RADS incorporates category 2b for nodules that are likely benign-such as tuberculous granulomas or intrapulmonary lymph nodes-even if their size corresponds to category 3 or 4. All CT scans were interpreted using Korean Lung-RADS (version 1.0 until 2021, version 1.1 from 2022 through 2023, and version 2022 implemented in 2024). To compare diagnostic performance, all category 2b nodules were reclassified into original Lung-RADS categories based on nodule size and type, and diagnostic metrics were evaluated for both systems. To assess interreader agreement, three thoracic radiologists independently reviewed nodules initially categorized as 2b to determine whether they would also assign category 2b. Diagnostic performance was compared using McNemar and Park's permutation tests, whereas interreader agreement was analyzed using the Fleiss κ statistic.

resultsCategory 2b nodules were found in 29 of the 1,371 participants (2.1%). Among them, one (3.5%) was diagnosed with lung cancer, whereas the remaining 28 (96.6%) were benign. Interreader agreement for 2b categorization was fair (Fleiss κ = 0.35; 95% confidence interval, 0.17-0.56). In terms of diagnostic performance, Korean Lung-RADS demonstrated significantly higher specificity (92.5% vs. 90.4%,

conclusionKorean Lung-RADS category 2b contributes to reducing false positives in lung cancer screening. However, its subjective nature results in fair interreader agreement, underscoring the need for greater standardization to improve consistency in its application.

Indexed as

Early Detection of CancerLung NeoplasmsTomography, X-Ray ComputedAgedFemaleHumansLungMaleMiddle AgedObserver VariationRepublic of KoreaRetrospective StudiesSensitivity and SpecificityDiagnostic Screening ProgramsEarly Detection of CancerLung NeoplasmsMultidetector Computed TomographyRetrospective Studies

Identifiers

PMID42740662
PMCPMC13574556

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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.