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      Individual Calculation of Effective Dose and Risk of Malignancy Based on Monte Carlo Simulations after Whole Body Computed Tomography

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          Abstract

          Detailed knowledge about radiation exposure is crucial for radiology professionals. The conventional calculation of effective dose (ED) for computed tomography (CT) is based on dose length product (DLP) and population-based conversion factors (k). This is often imprecise and unable to consider individual patient characteristics. We sought to provide more precise and individual radiation exposure calculation using image based Monte Carlo simulations (MC) in a heterogeneous patient collective and to compare it to phantom based MC provided from the National Cancer Institute (NCI) as academic reference. Dose distributions were simulated for 22 patients after whole-body CT during Positron Emission Tomography-CT. Based on MC we calculated individual Lifetime Attributable Risk (LAR) and Excess Relative Risk (ERR) of cancer mortality. ED MC was compared to ED DLP and ED NCI. ED DLP (13.2 ± 4.5 mSv) was higher compared to ED NCI (9.8 ± 2.1 mSv) and ED MC (11.6 ± 1.5 mSv). Relative individual differences were up to −48% for ED MC and −44% for ED NCI compared to ED DLP. Matching pair analysis illustrates that young age and gender are affecting LAR and ERR significantly. Because of these uncertainties in radiation dose assessment automated individual dose and risk estimation would be desirable for dose monitoring in the future.

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          Most cited references22

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          CT dose index and patient dose: they are not the same thing.

          Cynthia McCollough, Shuai Leng, Lifeng Yu … (2011)
          Article 0 comments Cited 66 times – based on 0 reviews     Review now
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            Pixel-Level Deep Segmentation: Artificial Intelligence Quantifies Muscle on Computed Tomography for Body Morphometric Analysis

            Hyunkwang Lee, Fabian M Troschel, Shahein Tajmir … (2017)
            Pretreatment risk stratification is key for personalized medicine. While many physicians rely on an “eyeball test” to assess whether patients will tolerate major surgery or chemotherapy, “eyeballing” is inherently subjective and difficult to quantify. The concept of morphometric age derived from cross-sectional imaging has been found to correlate well with outcomes such as length of stay, morbidity, and mortality. However, the determination of the morphometric age is time intensive and requires highly trained experts. In this study, we propose a fully automated deep learning system for the segmentation of skeletal muscle cross-sectional area (CSA) on an axial computed tomography image taken at the third lumbar vertebra. We utilized a fully automated deep segmentation model derived from an extended implementation of a fully convolutional network with weight initialization of an ImageNet pre-trained model, followed by post processing to eliminate intramuscular fat for a more accurate analysis. This experiment was conducted by varying window level (WL), window width (WW), and bit resolutions in order to better understand the effects of the parameters on the model performance. Our best model, fine-tuned on 250 training images and ground truth labels, achieves 0.93 ± 0.02 Dice similarity coefficient (DSC) and 3.68 ± 2.29% difference between predicted and ground truth muscle CSA on 150 held-out test cases. Ultimately, the fully automated segmentation system can be embedded into the clinical environment to accelerate the quantification of muscle and expanded to volume analysis of 3D datasets.
            Article 0 comments Cited 49 times – based on 0 reviews     Review now
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              Volume CT dose index and dose-length product displayed during CT: what good are they?

              Walter Huda, F Mettler (2010)
              The average medical radiation effective dose to the U.S. population in 2006 was estimated at approximately 3.0 mSv, an increase of 600% in a single generation. Computed tomography (CT) alone accounts for approximately half of this medical radiation dose. Ongoing advances suggest that CT will continue to be the most important contributor, by far, to medical doses in the United States. The use of ionizing radiation in medical imaging, including CT, provides valuable diagnostic information that undoubtedly benefits many patients. Exposure to radiation, however, is currently believed to carry a small, but nonzero, risk. Accordingly, the medical imaging community must ensure that the benefits of a radiologic examination in any given patient exceed the corresponding risks. It is also the responsibility of the radiologist to ensure that no more radiation is used than needed for obtaining diagnostic information in any radiologic examination, especially CT. © RSNA, 2010
              Article 0 comments Cited 30 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Markus Kopp: markus.kopp@uk-erlangen.de
                Journal
                Journal ID (nlm-ta): Sci Rep
                Journal ID (iso-abbrev): Sci Rep
                Title: Scientific Reports
                Publisher: Nature Publishing Group UK (London )
                ISSN (Electronic): 2045-2322
                Publication date (Electronic): 11 June 2020
                Publication date PMC-release: 11 June 2020
                Publication date Collection: 2020
                Volume: 10
                Electronic Location Identifier: 9475
                Affiliations
                [1 ]ISNI 0000 0000 9935 6525, GRID grid.411668.c, Department of Radiology, , University Hospital Erlangen, ; Erlangen, Germany
                [2 ]ISNI 0000 0001 2111 7257, GRID grid.4488.0, Institute of Medical Microbiology and Hygiene, , University of Technology Dresden, ; Dresden, Germany
                [3 ]ISNI 0000 0000 9935 6525, GRID grid.411668.c, Department of Nuclear Medicine, , University Hospital Erlangen, ; Erlangen, Germany
                [4 ]ISNI 0000 0004 0552 4145, GRID grid.481749.7, Siemens Healthineers GmbH, ; Forchheim, Germany
                Author information
                http://orcid.org/0000-0002-2540-850X
                Article
                Publisher ID: 66366
                DOI: 10.1038/s41598-020-66366-2
                PMC ID: 7289876
                PubMed ID: 32528028
                SO-VID: 3c4d01fa-2f72-4349-923b-541d8455ab72
                Copyright © © The Author(s) 2020
                License:

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                Date received : 21 January 2019
                Date accepted : 14 May 2020
                Categories
                Subject: Article
                Custom metadata
                issue-copyright-statement © The Author(s) 2020

                ScienceOpen disciplines: Uncategorized
                Keywords: cancer,tomography,whole body imaging,risk factors
                Data availability:
                ScienceOpen disciplines: Uncategorized
                Keywords: cancer, tomography, whole body imaging, risk factors

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