Understanding the harm of low‑dose computed tomography radiation to the body (Review)

A Magnetic Resonance Imaging Protocol for the Evaluation of Pediatric Postappendectomy Abscess: A Quality Improvement Project

INTRODUCTION

Computed tomography (CT) scans are often used when cross-sectional imaging is required for evaluation of postappendectomy abscess, exposing children to a source of ionizing radiation. Our aim was to decrease the use of CT scans in pediatric postappendectomy patients by 50% in 12 mo and to sustain those results for 1 y.

METHODS

A comprehensive magnetic resonance imaging protocol was introduced in 2018 at a tertiary children's hospital within a general health system to replace CT scans in suspected pediatric postappendectomy abscess. Diagnostic and clinical outcomes were compared preprotocol (2015-2017) and postprotocol (2018-2022) implementation using standard univariate statistics. P < 0.05 was considered significant. Quality improvement methodology was used to design and implement the protocol.

RESULTS

Sixty eight pediatric postappendectomy patients received cross-sectional imaging during the study period. Overall, CT scans were used exclusively (100%, n = 27) in the preimplementation period compared to 31.7% (n = 13) of cross-sectional imaging in the postimplementation period. However, in the first year of protocol implementation, CT scan use only decreased to 78% of cross-sectional studies performed. The majority of protocol deviations (54%) also occurred in this time period. With improved education and reinforcement, CT scan utilization decreased to approximately 24% of cross-sectional studies annually. Missed abscess rate, time to diagnosis, drainage procedure type, readmission, and reoperation were similar between preimplementation and postimplementation periods.

CONCLUSIONS

Implementation of a postappendectomy abscess magnetic resonance imaging protocol was associated with decreased CT utilization in the pediatric population, while maintaining comparable diagnostic evaluation and clinical outcomes. Adherence to quality improvement principles facilitated achieving goals and sustaining gains.

Methylated Circulating Tumor DNA in Blood as a Tool for Diagnosing Lung Cancer: A Systematic Review and Meta-Analysis

Lung cancer is the leading cause of cancer-related deaths, and early detection is crucial for improving patient outcomes. Current screening methods using computed tomography have limitations, prompting interest in non-invasive diagnostic tools such as methylated circulating tumor DNA (ctDNA). The PRISMA guidelines for systematic reviews were followed. The electronic databases MEDLINE, Embase, Web of Science, and Cochrane Library were systematically searched for articles. The search string contained three main topics: Lung cancer, blood, and methylated ctDNA. The extraction of data and quality assessment were carried out independently by the reviewers. In total, 33 studies were eligible for inclusion in this systematic review and meta-analysis. The most frequently studied genes were SHOX2, RASSF1A, and APC. The sensitivity and specificity of methylated ctDNA varied across studies, with a summary sensitivity estimate of 46.9% and a summary specificity estimate of 92.9%. The area under the hierarchical summary receiver operating characteristics curve was 0.81. The included studies were generally of acceptable quality, although they lacked information in certain areas. The risk of publication bias was not significant. Based on the findings, methylated ctDNA in blood shows potential as a rule-in tool for lung cancer diagnosis but requires further research, possibly in combination with other biomarkers.

Structure-preserved meta-learning uniting network for improving low-dose CT quality

Objective.Deep neural network (DNN) based methods have shown promising performances for low-dose computed tomography (LDCT) imaging. However, most of the DNN-based methods are trained on simulated labeled datasets, and the low-dose simulation algorithms are usually designed based on simple statistical models which deviate from the real clinical scenarios, which could lead to issues of overfitting, instability and poor robustness. To address these issues, in this work, we present a structure-preserved meta-learning uniting network (shorten as 'SMU-Net') to suppress noise-induced artifacts and preserve structure details in the unlabeled LDCT imaging task in real scenarios.Approach.Specifically, the presented SMU-Net contains two networks, i.e., teacher network and student network. The teacher network is trained on simulated labeled dataset and then helps the student network train with the unlabeled LDCT images via the meta-learning strategy. The student network is trained on real LDCT dataset with the pseudo-labels generated by the teacher network. Moreover, the student network adopts the Co-teaching strategy to improve the robustness of the presented SMU-Net.Main results.We validate the proposed SMU-Net method on three public datasets and one real low-dose dataset. The visual image results indicate that the proposed SMU-Net has superior performance on reducing noise-induced artifacts and preserving structure details. And the quantitative results exhibit that the presented SMU-Net method generally obtains the highest signal-to-noise ratio (PSNR), the highest structural similarity index measurement (SSIM), and the lowest root-mean-square error (RMSE) values or the lowest natural image quality evaluator (NIQE) scores.Significance.We propose a meta learning strategy to obtain high-quality CT images in the LDCT imaging task, which is designed to take advantage of unlabeled CT images to promote the reconstruction performance in the LDCT environments.

Low Dose and Non-Targeted Radiation Effects in Environmental Protection and Medicine-A New Model Focusing on Electromagnetic Signaling

The role of signalling in initiating and perpetuating effects triggered by deposition of ionising radiation energy in parts of a system is very clear. Less clear are the very early steps involved in converting energy to chemical and biological effects in non-targeted parts of the system. The paper aims to present a new model, which could aid our understanding of the role of low dose effects in determining ultimate disease outcomes. We propose a key role for electromagnetic signals resulting from physico-chemical processes such as excitation decay, and acoustic waves. These lead to the initiation of damage response pathways such as elevation of reactive oxygen species and membrane associated changes in key ion channels. Critically, these signalling pathways allow coordination of responses across system levels. For example, depending on how these perturbations are transduced, adverse or beneficial outcomes may predominate. We suggest that by appreciating the importance of signalling and communication between multiple levels of organisation, a unified theory could emerge. This would allow the development of models incorporating time, space and system level to position data in appropriate areas of a multidimensional domain. We propose the use of the term "infosome" to capture the nature of radiation-induced communication systems which include physical as well as chemical signals. We have named our model "the variable response model" or "VRM" which allows for multiple outcomes following exposure to low doses or to signals from low dose irradiated cells, tissues or organisms. We suggest that the use of both dose and infosome in radiation protection might open up new conceptual avenues that could allow intrinsic uncertainty to be embraced within a holistic protection framework.

Dose Descriptors and Assessment of Risk of Exposure-Induced Death in Patients Undergoing COVID-19 Related Chest Computed Tomography

For more than two years, coronavirus disease 19 (COVID-19) has represented a threat to global health and lifestyles. Computed tomography (CT) imaging provides useful information in patients with COVID-19 pneumonia. However, this diagnostic modality is based on exposure to ionizing radiation, which is associated with an increased risk of radiation-induced cancer. In this study, we evaluated the common dose descriptors, CTDI_vol and DLP, for 1180 adult patients. This data was used to estimate the effective dose, and risk of exposure-induced death (REID). Awareness of the extensive use of CT as a diagnostic tool in the management of COVID-19 during the pandemic is vital for the evaluation of radiation exposure parameters, dose reduction methods development and radiation protection.