Evidence map›Paper›PMID 37656137›Full record

ArticleMedical physics2023

The risk index as a basis for risk/benefit analyses and protocol optimization in diagnostic nuclear imaging.

Juan C Ocampo Ramos, Lukas M Carter, Justin L Brown, Harry Marquis, Carlos F Uribe, Pat B Zanzonico, Wesley E Bolch, Adam L Kesner

Abstract read
In one paragraph

Article in Medical physics, 2023. 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. MIRD Pamphlet No. 34, Part 1: MIRDct-A Customizable Software Tool for CT Dosimetry.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2026
    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

8 authors.

Juan C Ocampo RamosDepartment of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
Lukas M CarterDepartment of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
Justin L BrownJ. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, Florida, USA.
Harry MarquisDepartment of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
Carlos F UribeFunctional Imaging Department, BC Cancer, Vancouver, British Columbia, Canada.
Pat B ZanzonicoDepartment of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
Wesley E BolchJ. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, Florida, USA.
Adam L KesnerDepartment of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Nicholas D Socci · 1985 to 2026
$347.4M
MIRDCalc – A Community Tool for Deriving and Reporting Patient Organ Doses in Nuclear Medicine, Computed Tomography, and Hybrid ImagingU01EB028234 · NIBIB · SLOAN-KETTERING INST CAN RESEARCH · PI BOLCH, WESLEY E, KESNER, ADAM LEON · 2019 to 2023
$3.2M
NCI NIH HHS P30 CA008748NIBIB NIH HHS U01 EB028234NIH HHS U01 EB028234
6 · The paper itself

Abstract

backgroundPotential risk associated with low-dose radiation exposures is often expressed using the effective dose (E) quantity. Other risk-related quantities have been proposed as alternatives. The recently introduced risk index (RI) shares similarities with E but expands the metric to incorporate medical imaging-appropriate risks factors including patient-specific size, age, and sex. PURPOSE: The aim of this work is to examine the RI metric for quantifying stochastic radiation risk and demonstrate its applications in nuclear imaging. The advantages in this improved metric may help the field progress toward stratified risk consideration in the course of patient management, improve efforts for procedure optimization, and support an evolution in the science of radiation risk assessment.

methodsIn this study we describe, implement, and calculate RI for various diagnostic nuclear imaging scenarios using reference biokinetics published in ICRP Publication 128 for commonly utilized radiopharmaceuticals. All absorbed dose, E and RI calculations were performed using the freely available MIRDcalc nuclear medicine dosimetry software; the organ specific risk parameters used in the software are also benchmarked in this text. The resulting RI and E values are compared and various trends in RI values identified.

resultsE and RI coefficients were calculated for 3016 use cases. Notably RI values vary depending on patient characteristics. Overall, across the population, global trends in RI values can be identified. In general, RI values were 2.15 times higher for females than males, due to higher risk coefficients and activities being distributed in smaller reference masses. The pediatric patients showed higher RIs than adults, as younger patients generally receive higher absorbed doses per administered activity, and are more radiosensitive, and have a longer projected lifespan at risk. A compendium of E and RI values is also provided in table format to serve as a reference for the community.

conclusionsRI is a rational quantity that could be used for justification, risk communication and protocol optimization in medical imaging. It has some advantages when compared to the long-utilized E value with respect to personalization, since accounts for patient size, age, sex, and natural incidence of cancer risk.

Indexed as

RadiometryRadiopharmaceuticalsAdultChildFemaleHumansMalePhantoms, ImagingRadiation DosageRadiographySoftwareRadiopharmaceuticalseffective doselifetime attributable riskoptimizationrisk assessment

Identifiers

PMID37656137
PMCPMC12445442

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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.