Evidence map›Paper›PMID 37558637›Full record

ArticleMedical physics2024

Generalized methods for predicting biological response to mixed radiation types and calculating equieffective doses (EQDX).

Sumudu Katugampola, Robert F Hobbs, Roger W Howell

Abstract read
In one paragraph

Article in Medical physics, 2024. 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

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2 · The registry

The trial behind it

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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. Review
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

3 authors.

Sumudu KatugampolaDepartment of Radiology and Center for Cell Signaling, New Jersey Medical School, Rutgers University, Newark, New Jersey, USA.
Robert F HobbsDepartment of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Roger W HowellDepartment of Radiology and Center for Cell Signaling, New Jersey Medical School, Rutgers University, Newark, New Jersey, USA.ORCID https://orcid.org/0000-0002-7057-8110

Funding

MIRDcell Version 3R01CA245139 · NCI · RUTGERS BIOMEDICAL AND HEALTH SCIENCES · PI Roger W. Howell · 2020 to 2026
$1.8M
NCI NIH HHS 1R01CA245139NCI NIH HHS R01 CA245139
6 · The paper itself

Abstract

backgroundPredicting biological responses to mixed radiation types is of considerable importance when combining radiation therapies that use multiple radiation types and delivery regimens. These may include the use of both low- and high-linear energy transfer (LET) radiations. A number of theoretical models have been developed to address this issue. However, model predictions do not consistently match published experimental data for mixed radiation exposures. Furthermore, the models are often computationally intensive. Accordingly, there is a need for efficient analytical models that can predict responses to mixtures of low- and high-LET radiations. Additionally, a general formalism to calculate equieffective dose (EQDX) for mixed radiations is needed. PURPOSE: To develop a computationally efficient analytical model that can predict responses to complex mixtures of low- and high-LET radiations as a function of either absorbed dose or EQDX.

methodsThe Zaider-Rossi model (ZRM) was modified by replacing the geometric mean of the quadratic coefficients in the interaction term with the arithmetic mean. This modified ZRM model (mZRM) was then further generalized to any number of radiation types and its validity was tested against published experimental observations. Comparisons between the predictions of the ZRM and mZRM, and other models, were made using two and three radiation types. In addition, a generalized formalism for calculating EQDX for mixed radiations was developed within the context of mZRM and validated with published experimental results.

resultsThe predictions of biological responses to mixed-LET radiations calculated with the mZRM are in better agreement with experimental observations than ZRM, especially when high- and low-LET radiations are mixed. In these situations, the ZRM overestimated the surviving fraction. Furthermore, the EQDX calculated with mZRM are in better agreement with experimental observations.

conclusionThe mZRM is a computationally efficient model that can be used to predict biological response to mixed radiations that have low- and high-LET characteristics. Importantly, interaction terms are retained in the calculation of EQDX for mixed radiation exposures within the mZRM framework. The mZRM has application in a wide range of radiation therapies, including radiopharmaceutical therapy.

Indexed as

Radiation ExposureDose-Response Relationship, RadiationRelative Biological Effectivenessbiologically effective dosedose responseequieffective doseIonizing radiationlinear energy transferlinear quadratic modelradiation mixture

Identifiers

PMID37558637
PMCPMC11330299

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