Evidence map›Paper›PMID 41284032›Full record

ArticleEuropean radiology2026

Crowd-sourcing optimized abdomen CT protocols from 908,000 examinations in a large radiation dose registry.

Rebecca Smith-Bindman, Taewoon Kang, Carly Stewart, Philip W Chu, Yifei Wang, Timothy P Szczykutowicz

Abstract read
In one paragraph

Article in European radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

6 authors.

Rebecca Smith-BindmanDepartment of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA. rebecca.smith-bindman@ucsf.edu.ORCID http://orcid.org/0000-0003-1972-639X
Taewoon KangDepartment of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA.
Carly StewartDepartment of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA.
Philip W ChuDepartment of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA.
Yifei WangDepartment of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA.
Timothy P SzczykutowiczDepartment of Radiology, University of Wisconsin, Madison, WI, USA.

Funding

NCI NIH HHS 1R01CA181191-01A1Patient-Centered Outcomes Research Institute CD-1304-7043Patient-Centered Outcomes Research Institute DI-2018C1-11375
6 · The paper itself

Abstract

objectivesIdentify routine diagnostic abdomen CT protocols in adults that achieve lower radiation doses, crowd-sourced from a large CT dose registry. MATERIALS AND

methodsWe retrospectively captured acquisition parameters, patient diameter, and patient size-adjusted and unadjusted radiation dose (CTDIvol and DLP). A protocol was defined as a unique combination of facility, scanner make/model, CT indication, and protocol name. We used k-means clustering to classify protocols into clusters based on similarity of average acquisition parameters (mAs, pitch, kV, collimation, scan length, phase count), each representing a distinct pattern of protocol design choices. For each cluster, we summarized the mean technical parameters and dose metrics of its constituent protocols.

resultsAnalyses included 907,992 exams from 1767 protocols at 132 facilities, grouped in 9 clusters. The number of protocols and exams within each cluster ranged from 62 to 508 and 15,317 to 381,457, respectively. Mean size-adjusted DLP varied threefold across clusters, ranging from 486 to 1382 mGy-cm, with no difference in patient diameter. Lowest-dosed clusters 1 and 2 minimized radiation primarily through low kV (around 100). Cluster 8 had the highest acquisition techniques (mean mAs = 338 and kV = 125), and cluster 9 had the highest dose due to phase (mean = 3.3, versus 1.1-1.6 for the remaining clusters).

conclusionThe clustering approach offers a new framework for identifying optimized protocols, while highlighting variation in practice and worst-in-class technique, e.g., using three phases for routine abdomen. These findings may guide clinicians in protocol design, by providing best-practice protocols from a large registry, as well as a method for analyzing data within their own health system. KEY POINTS: Question Can we identify best-practice imaging protocols from a large CT registry, to offer clinicians a blueprint for optimizing radiation dose in routine abdomen CT? Findings Protocols were grouped into clusters using average acquisition parameters. Across clusters, patient size-adjusted radiation dose varied 486-1382 mGy-cm. Lowest-dosed protocols had one phase and low kVp. Clinical relevance We used crowd-sourcing and k-means clustering to identify routine abdomen CT protocols that achieve lowest radiation dose, offering a playbook for protocol design from real clinical practice. Non-indicated phases were the greatest driver of excessive radiation dose.

Indexed as

CrowdsourcingRadiation DosageRadiography, AbdominalRegistriesTomography, X-Ray ComputedAdultFemaleHumansMaleMiddle AgedRetrospective StudiesAbdomenComputed tomographyProtocol designProtocol optimizationRadiation dose

Identifiers

PMID41284032
PMCPMC13086641

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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.