Evidence map›Paper›PMID 32851496›Full record

ArticleRadiation and environmental biophysics2020

ProZES: the methodology and software tool for assessment of assigned share of radiation in probability of cancer occurrence.

Alexander Ulanowski, Elena Shemiakina, Denise Güthlin, Janine Becker, Dale Preston, A Iulian Apostoaei, F Owen Hoffman, Peter Jacob, Jan Christian Kaiser, Markus Eidemüller

Abstract read
In one paragraph

Article in Radiation and environmental biophysics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 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

10 authors.

Alexander UlanowskiInstitute of Radiation Medicine, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany.ORCID 0000-0003-4863-4128
Elena ShemiakinaInstitute of Radiation Medicine, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany.
Denise GüthlinInstitute of Radiation Medicine, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany.
Janine BeckerInstitute of Radiation Medicine, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany.ORCID 0000-0001-9409-4869
Dale PrestonHirosoft International, Eureka, CA, USA.ORCID 0000-0002-0641-2925
A Iulian ApostoaeiOak Ridge Center for Risk Analysis, Inc, Oak Ridge, TN, USA.ORCID 0000-0001-9750-6056
F Owen HoffmanOak Ridge Center for Risk Analysis, Inc, Oak Ridge, TN, USA.ORCID 0000-0002-1520-4124
Peter JacobInstitute of Radiation Medicine, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany.
Jan Christian KaiserInstitute of Radiation Medicine, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany.ORCID 0000-0003-0359-2251
Markus EidemüllerInstitute of Radiation Medicine, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany. markus.eidemueller@helmholtz-muenchen.de.ORCID 0000-0003-0249-3710

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

ProZES is a software tool for estimating the probability that a given cancer was caused by preceding exposure to ionising radiation. ProZES calculates this probability, the assigned share, for solid cancers and hematopoietic malignant diseases, in cases of exposures to low-LET radiation, and for lung cancer in cases of exposure to radon. User-specified inputs include birth year, sex, type of diagnosed cancer, age at diagnosis, radiation exposure history and characteristics, and smoking behaviour for lung cancer. Cancer risk models are an essential part of ProZES. Linking disease and exposure to radiation involves several methodological aspects, and assessment of uncertainties received particular attention. ProZES systematically uses the principle of multi-model inference. Models of radiation risk were either newly developed or critically re-evaluated for ProZES, including dedicated models for frequent types of cancer and, for less common diseases, models for groups of functionally similar cancer sites. The low-LET models originate mostly from the study of atomic bomb survivors in Hiroshima and Nagasaki. Risks predicted by these models are adjusted to be applicable to the population of Germany and to different time periods. Adjustment factors for low dose rates and for a reduced risk during the minimum latency time between exposure and cancer are also applied. The development of the methodology and software was initiated and supported by the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU) taking up advice by the German Commission on Radiological Protection (SSK, Strahlenschutzkommission). These provide the scientific basis to support decision making on compensation claims regarding malignancies following occupational exposure to radiation in Germany.

Indexed as

Models, TheoreticalSoftwareGermanyHumansNeoplasms, Radiation-InducedProbabilityRadiation ExposureRisk AssessmentMalignant neoplasmsRadiation exposureRadiation riskRisk analysis

Identifiers

PMID32851496
PMCPMC7544726

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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.