Evidence map›Paper›PMID 42907936›Full record

ReviewCancer imaging : the official publication of the International Cancer Imaging Society2026

ACR lung-RADS: updates and future directions.

Milena Petranović, Ashley E Prosper, Andetta R Hunsaker, Stephen Lam, Jared D Christensen

Abstract readReview
In one paragraph

Review in Cancer imaging : the official publication of the International Cancer Imaging Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Milena PetranovićDepartment of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA. mpetranovic@mgh.harvard.edu.ORCID https://orcid.org/0000-0002-6412-7428
Ashley E ProsperDepartment of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
Andetta R HunsakerHarvard Medical School, Boston, MA, USA.
Stephen LamBC Cancer Research Institute, Vancouver, BC, Canada.
Jared D ChristensenDepartment of Radiology, Duke University Medical Center, Durham, NC, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lung cancer remains the leading cause of cancer-related mortality worldwide, and low-dose CT (LDCT) screening has been shown to significantly reduce lung cancer deaths in high-risk populations. The American College of Radiology (ACR) introduced the Lung CT Screening Reporting and Data System (Lung-RADS) in 2014 to standardize the reporting and management of screen-detected pulmonary nodules. Since its inception, Lung-RADS has undergone iterative updates reflecting advances in evidence, clinical practice, and the expanding scope of lung cancer screening. This review summarizes the major updates to Lung-RADS, highlights areas of active discussion within the Lung-RADS Committee, and outlines future research directions. Key topics include the ongoing evaluation of volumetric versus diameter-based nodule measurement, the management of subsolid nodules and the balance between surveillance and intervention, the implementation of Category 0 for infectious or inflammatory findings, clarification of juxtapleural nodule classification, the approach to incidental findings, and the development of a Lung-RADS Atlas. Future directions address the integration of risk-based and category-based assessment approaches, the role of artificial intelligence and deep learning in nodule stratification, the potential incorporation of incidental nodules into Lung-RADS, and the prospect of international harmonization for lung cancer screening reporting and management. Lung-RADS has become an essential reporting and quality tool for lung cancer screening in the United States and increasingly worldwide. Continued refinement, guided by emerging evidence, multidisciplinary collaboration, and technological innovation, will be critical to advancing precision screening strategies and reducing the global burden of lung cancer.Clinical trial numberNot applicable.

Indexed as

Lung NeoplasmsTomography, X-Ray ComputedEarly Detection of CancerHumansArtificial intelligenceLow-dose CTLung cancer screeningLung-RADSPulmonary nodulesRisk-based assessmentSubsolid nodules

Identifiers

PMID42907936
PMCPMC13685224

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

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.