Evidence map›Paper›PMID 41026203›Full record

ArticleEuropean radiology2026

Greenhouse gas emissions due to long-term data storage of CT with reformats and strategies for mitigation.

Yifan Jia, Michael Deng, Rebecca Burger, Sarah Sheard, Kate Hanneman, Moran Drucker Iarovich, Evis Sala, Giacomo Avesani, Rowland O Illing, Andrea G Rockall

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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

10 authors.

Yifan JiaDepartment of Medicine, Imperial College London, London, UK. yj7218@ic.ac.uk.ORCID http://orcid.org/0000-0002-6837-7010
Michael DengDepartment of Radiology, Imperial College Healthcare NHS Trust, London, UK.
Rebecca BurgerDepartment of Radiology, Imperial College Healthcare NHS Trust, London, UK.
Sarah SheardDepartment of Radiology, Imperial College Healthcare NHS Trust, London, UK.
Kate HannemanDepartment of Medical Imaging, University of Toronto, Toronto, Canada.
Moran Drucker IarovichDepartment of Medical Imaging, University of Toronto, Toronto, Canada.
Evis SalaDipartimento di Scienze Radiologiche ed Ematologiche, Universita Cattolica del Sacro Cuore, Rome, Italy.
Giacomo AvesaniDipartimento Diagnostica per Immagini e Radioterapia Oncologica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
Rowland O IllingDivision of Surgery and Interventional Science, University College Hospital, London, UK.
Andrea G RockallDepartment of Radiology, Imperial College Healthcare NHS Trust, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesMedical image data storage and associated greenhouse gas (GHG) emissions are increasing. We aimed to measure non-essential storage and model mitigation strategies. MATERIALS AND

methodsThe proportion of stored post-processed series (reformats and reconstructions) was retrospectively recorded in 183 baseline staging CT chest-abdomen-pelvis studies (CT-CAP) for endometrial cancer in a UK referral centre between 2013 and 2016 (Cohort A). File size (megabytes, MB) of each series was recorded for 30 studies (Cohort B) and compared with 100 Canadian studies (Cohort C), contextualised by a survey of protocols across 17 global centres (including Cohort C). Storage-associated GHG emissions were modelled over 20 years for various mitigation strategies.

resultsPost-processed series were stored in 179/183 (97%) of cohort A, 29/30 (97%) of cohort B and 16/17 (94%) of global centres. Median file size was 787 MB (IQR 460, 1257) for the entire CT study (all stored series) and 290 MB (224, 355) for the acquired axial series alone. On-premises storage of all series for new UK endometrial cancer baseline studies 2020-2040 is estimated to generate 381 metric tons CO

conclusionCT data storage has a large environmental cost, necessitating global action. Various mitigation strategies are achievable in reducing storage-related emissions by up to 89%. KEY POINTS: Question Storage of non-essential post-processed CT image series contributes significantly to the accumulating image data storage-associated GHG emissions burden. Findings Modelling predicts emission savings of 69% by avoiding non-essential series storage in staging CTs of UK endometrial cancer patients, with comparable savings globally, based on current practice. Clinical relevance GHG emissions can be substantially reduced by not storing non-essential CT reformats, a mitigation that can be implemented immediately by radiologists. Further GHG mitigation is achievable using cloud storage and data-retention policies.

Indexed as

Endometrial NeoplasmsGreenhouse GasesInformation Storage and RetrievalTomography, X-Ray ComputedFemaleHumansRetrospective StudiesUnited KingdomGreenhouse GasesEndometrial cancerSustainabilityTomography (x-ray computed)

Identifiers

PMID41026203
PMCPMC12963087

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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.