Lifetime attributable cancer risk related to prevalent CT scan procedures in pediatric medical imaging centers

Tahmasebzadeh, Atefeh; Paydar, Reza; Soltani-Kermanshahi, Mojtaba; Maziar, Asghar; Reiazi, Reza

doi:10.1080/09553002.2021.1931527

Cited by 15 publications

(13 citation statements)

References 26 publications

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“…for the ≤15 years old pediatric patients. [ 41 ] This discrepancy may stem from differences in the radiation dose measurement methods and the scan settings used (kVp, mAs, and pitch). Bismuth shields have been used to reduce radiation exposure of the eyes in pediatric brain CT.[ 19 42 ] In our study, a bismuth shield with a 1-cm shield-to-eyelid spacer decreased the eye dose by 28% relative to the reference scan, that is consistent with others.…”

Section: Discussionmentioning

confidence: 99%

Tradeoffs between radiation exposure to the lens of the eyes and diagnostic image quality in pediatric brain computed tomography

Karami,

Albosof,

Gholami

et al. 2023

J Med Signals Sens

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Section: Discussionmentioning

confidence: 99%

Tradeoffs between radiation exposure to the lens of the eyes and diagnostic image quality in pediatric brain computed tomography

Karami,

Albosof,

Gholami

et al. 2023

J Med Signals Sens

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“…It can cause harmful biological and health effects such as hair loss, illness and cancer [4]. Some may argue that the radiation from commercial CT scans will not have any serious effects on the patients, however, the rapid increase of CT application has already contributed to the global annual cumulative ionizing radiation dose by 34% [5]. For minimizing the risks, an effective way is to deliver lesser X-rays to the patient i.e.…”

Section: Problem Statementmentioning

confidence: 99%

Fused Attention Modules Embedded in Artificial Neural Networks for Low Dose CT Denoising With Integrated Loss Functions

Marcos

2024

Preprint

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<p>X-ray Computed Tomography (CT) is a non-invasive medical diagnostic tool that has raised public concerns due to the associated health risks of radiation dose to patients. Reducing the radiation dose leads to noise artifacts, making the low-dose CT images unreliable for diagnosis. Hence, low-dose computed tomography (LDCT) image reconstruction techniques have offered a new challenge in the research area. This thesis focuses on reconstructing LDCT images using deep learning techniques to provide an efficient, effective, and accurate training regimes for LDCT image denoising. A fusion of spatial and channel attention modules integrated into a dilated residual network is proposed to improve the structural details of denoised LDCT images. Further, a combination of perceptual loss, per-pixel loss, and structural dissimilarity loss is used for the optimization of the overall network. These objective functions aim to preserve structural details, avoid edge over-smoothing and enhance the image texture, respectively. Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Metrics (SSIM) are used for measuring the quantitative results. A comparative experiment was done between the proposed model and the recent denoising models, such as Block Matching and 3D Filtering (BM3D), patch Markovnian Generative Adversarial Network (patch-GAN) and dilated residual learning with edge detection (DRL-E-MP). Not only with quantitative results, but these models were also compared visually. To further strengthen the validity of the outcomes, five different CT image datasets were used. The proposed model obtained the highest PSNR/SSIM value of 34.36/0.6971 while BM3D resulted in the lowest value with 30.24/0.4461 using the chest dataset from the Mayo Clinic. Overall, the proposed network demonstrated that it could outperform state-of-the-art models.</p>

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“…Several studies have addressed the LAR of cancer incidence and mortality from low- and standard-dose chest CT scans [ 89 , 90 , 91 , 92 , 97 , 98 ]. From Table 4 and Table 5 , it is evident that the effective dose resulting from a low-dose chest CT for COVID-19 is much lower than that from standard-dose chest CT (0.20–1.8 mSv Vs. 2.20–8.70 mSv).…”

Section: Low- and Standard-dose Chest Ct In Covid-19: Radiation Riskmentioning

confidence: 99%

“…A pooled analysis of seven cohort studies assessing thyroid cancer after exposure to external radiation supports thyroid cancer increase at 10–90 mSv doses [ 103 ]. Overall, it is estimated that a standard-dose chest CT may increase the risk of breast cancer by 20–287 [ 89 , 90 , 92 , 97 , 98 ] and lung cancer by 22–152 [ 90 , 91 , 97 , 98 ] per 100,000 people.…”

Section: Low- and Standard-dose Chest Ct In Covid-19: Radiation Riskmentioning

confidence: 99%

Radiation Exposure and Lifetime Attributable Risk of Cancer Incidence and Mortality from Low- and Standard-Dose CT Chest: Implications for COVID-19 Pneumonia Subjects

et al. 2022

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Since the novel coronavirus disease 2019 (COVID-19) outbreak, there has been an unprecedented increase in the acquisition of chest computed tomography (CT) scans. Nearly 616 million people have been infected by COVID-19 worldwide to date, of whom many were subjected to CT scanning. CT exposes the patients to hazardous ionizing radiation, which can damage the genetic material in the cells, leading to stochastic health effects in the form of heritable genetic mutations and increased cancer risk. These probabilistic, long-term carcinogenic effects of radiation can be seen over a lifetime and may sometimes take several decades to manifest. This review briefly describes what is known about the health effects of radiation, the lowest dose for which there exists compelling evidence about increased radiation-induced cancer risk and the evidence regarding this risk at typical CT doses. The lifetime attributable risk (LAR) of cancer from low- and standard-dose chest CT scans performed in COVID-19 subjects is also discussed along with the projected number of future cancers that could be related to chest CT scans performed during the COVID-19 pandemic. The LAR of cancer Incidence from chest CT has also been compared with those from other radiation sources, daily life risks and lifetime baseline risk.

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Lifetime attributable cancer risk related to prevalent CT scan procedures in pediatric medical imaging centers

Cited by 15 publications

References 26 publications

Tradeoffs between radiation exposure to the lens of the eyes and diagnostic image quality in pediatric brain computed tomography

Tradeoffs between radiation exposure to the lens of the eyes and diagnostic image quality in pediatric brain computed tomography

Fused Attention Modules Embedded in Artificial Neural Networks for Low Dose CT Denoising With Integrated Loss Functions

Radiation Exposure and Lifetime Attributable Risk of Cancer Incidence and Mortality from Low- and Standard-Dose CT Chest: Implications for COVID-19 Pneumonia Subjects

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