{"title":"Application of multi-method-multi-model inference to radiation related solid cancer excess risks models for astronaut risk assessment","authors":"Luana Hafner , Linda Walsh","doi":"10.1016/j.zemedi.2023.06.003","DOIUrl":null,"url":null,"abstract":"<div><p>The impact of including model-averaged excess radiation risks (ER) into a measure of radiation attributed decrease of survival (RADS) for the outcome all solid cancer incidence and the impact on the uncertainties is demonstrated. It is shown that RADS applying weighted model averaged ER based on AIC weights result in smaller risk estimates with narrower 95% CI than RADS using ER based on BIC weights. Further a multi-method-multi-model inference approach is introduced that allows calculating one general RADS estimate providing a weighted average risk estimate for a lunar and a Mars mission. For males the general RADS estimate is found to be 0.42% (95% CI: 0.38%; 0.45%) and for females 0.67% (95% CI: 0.59%; 0.75%) for a lunar mission and 2.45% (95% CI: 2.23%; 2.67%) for males and 3.91% (95% CI: 3.44%; 4.39%) for females for a Mars mission considering an age at exposure of 40 years and an attained age of 65 years. It is recommended to include these types of uncertainties and to include model-averaged excess risks in astronaut risk assessment.</p></div>","PeriodicalId":54397,"journal":{"name":"Zeitschrift fur Medizinische Physik","volume":"34 1","pages":"Pages 83-91"},"PeriodicalIF":2.4000,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0939388923000806/pdfft?md5=c3e7e327440b0492d75125a9932acf05&pid=1-s2.0-S0939388923000806-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Zeitschrift fur Medizinische Physik","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0939388923000806","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0
Abstract
The impact of including model-averaged excess radiation risks (ER) into a measure of radiation attributed decrease of survival (RADS) for the outcome all solid cancer incidence and the impact on the uncertainties is demonstrated. It is shown that RADS applying weighted model averaged ER based on AIC weights result in smaller risk estimates with narrower 95% CI than RADS using ER based on BIC weights. Further a multi-method-multi-model inference approach is introduced that allows calculating one general RADS estimate providing a weighted average risk estimate for a lunar and a Mars mission. For males the general RADS estimate is found to be 0.42% (95% CI: 0.38%; 0.45%) and for females 0.67% (95% CI: 0.59%; 0.75%) for a lunar mission and 2.45% (95% CI: 2.23%; 2.67%) for males and 3.91% (95% CI: 3.44%; 4.39%) for females for a Mars mission considering an age at exposure of 40 years and an attained age of 65 years. It is recommended to include these types of uncertainties and to include model-averaged excess risks in astronaut risk assessment.
期刊介绍:
Zeitschrift fur Medizinische Physik (Journal of Medical Physics) is an official organ of the German and Austrian Society of Medical Physic and the Swiss Society of Radiobiology and Medical Physics.The Journal is a platform for basic research and practical applications of physical procedures in medical diagnostics and therapy. The articles are reviewed following international standards of peer reviewing.
Focuses of the articles are:
-Biophysical methods in radiation therapy and nuclear medicine
-Dosimetry and radiation protection
-Radiological diagnostics and quality assurance
-Modern imaging techniques, such as computed tomography, magnetic resonance imaging, positron emission tomography
-Ultrasonography diagnostics, application of laser and UV rays
-Electronic processing of biosignals
-Artificial intelligence and machine learning in medical physics
In the Journal, the latest scientific insights find their expression in the form of original articles, reviews, technical communications, and information for the clinical practice.