Evidence map›Paper›PMID 39702791›Full record

ArticleCommunications medicine2024

Optimization of abdominal CT based on a model of total risk minimization by putting radiation risk in perspective with imaging benefit.

Francesco Ria, Anru R Zhang, Reginald Lerebours, Alaattin Erkanli, Ehsan Abadi, Daniele Marin, Ehsan Samei

Abstract read
In one paragraph

Article in Communications medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

7 authors.

Francesco RiaCarl E. Ravin Advanced Imaging Labs, Center for Virtual Imaging Trials, Department of Radiology, Duke University Health System, Durham, NC, USA. francesco.ria@duke.edu.ORCID http://orcid.org/0000-0001-5902-7396
Anru R ZhangDepartment of Biostatistics & Bioinformatics and Department of Computer Science, Duke University, Durham, NC, USA.
Reginald LereboursDepartment of Biostatistics & Bioinformatics and Department of Computer Science, Duke University, Durham, NC, USA.
Alaattin ErkanliDepartment of Biostatistics & Bioinformatics and Department of Computer Science, Duke University, Durham, NC, USA.
Ehsan AbadiCarl E. Ravin Advanced Imaging Labs, Center for Virtual Imaging Trials, Department of Radiology, Duke University Health System, Durham, NC, USA.
Daniele MarinDepartment of Radiology, Duke University Health System, Durham, NC, USA.
Ehsan SameiCarl E. Ravin Advanced Imaging Labs, Center for Virtual Imaging Trials, Department of Radiology, Duke University Health System, Durham, NC, USA.

Funding

TR&D Project 3: Virtual ReadersP41EB028744 · NIBIB · DUKE UNIVERSITY · PI Ehsan Samei · 2021 to 2026
$7.8M
Quality and Safety Monitoring of Clinical Computed Tomography PracticeR44EB031658 · NIBIB · METIS HEALTH ANALYTICS, LLC · PI SAPP, ARMISTEAD WRIGHT · 2021 to 2023
$1.9M
Emotional Factors that Promote Successful AgingR03AG028744 · NIA · MICHIGAN STATE UNIVERSITY · PI LUCAS, RICHARD E · 2006 to 2006
$64k
U.S. Department of Health & Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB) P41EB028744U.S. Department of Health & Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB) R44EB031658
6 · The paper itself

Abstract

backgroundRisk-versus-benefit optimization required a quantitative comparison of the two. The latter, directly related to effective diagnosis, can be associated to clinical risk. While many strategies have been developed to ascertain radiation risk, there has been a paucity of studies assessing clinical risk, thus limiting the optimization reach to achieve a minimum total risk to patients undergoing imaging examinations. In this study, we developed a mathematical framework for an imaging procedure total risk index considering both radiation and clinical risks based on specific tasks and investigated diseases.

methodsThe proposed model characterized total risk as the sum of radiation and clinical risks defined as functions of radiation burden, disease prevalence, false-positive rate, expected life-expectancy loss for misdiagnosis, and radiologist interpretative performance (i.e., AUC). The proposed total risk model was applied to a population of one million cases simulating a liver cancer scenario.

resultsFor all demographics, the clinical risk outweighs radiation risk by at least 400%. The optimization application indicates that optimizing typical abdominal CT exams should involve a radiation dose increase in over 90% of the cases, with the highest risk optimization potential in Asian population (24% total risk reduction; 306%

conclusionsFraming risk-to-benefit assessment as a risk-versus-risk question, calculating both clinical and radiation risk using comparable units, allows a quantitative optimization of total risks in CT. The results highlight the dominance of clinical risk at typical CT examination dose levels, and that exaggerated dose reductions can even harm patients.

Identifiers

PMID39702791
PMCPMC11659579

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Registered trials

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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.