Computed tomography scan radiation and brain cancer incidence

Risk of childhood brain tumors after exposure to CT radiation: A nationwide population-based case-control study in Finland

In this nationwide population-based case-control study, we assessed the risk of childhood brain tumors following exposure to radiation from CT. Brain tumors diagnosed in Finland during 1990-2016 were identified by the Finnish Cancer Registry. For each case, three age- and sex-matched controls were sampled from the Population Information System. The study population was linked to a CT dataset encompassing pediatric CTs performed in Finland during 1975-2011. We implemented a 5-year lag period and excluded participants with cancer predisposition syndromes or previous malignancies. We estimated brain doses using the NCICT program. Overall, 1067 brain tumors were diagnosed in children aged 0-15 years during 1990-2016, 58% of which were gliomas. Among eligible participants, nine cases (1%) and 10 controls (0.4%) had undergone at least one head/neck CT scan. The mean cumulative brain dose was 22 mGy for exposed participants. Participants who had undergone one or more head/neck CTs had a higher risk of developing brain tumors compared to unexposed individuals (Odds ratio [OR] = 2.84, 95% CI 1.12, 7.19). The excess OR (EOR) per 100 mGy of brain dose was 5.50 (95% CI 0.31, 10.95) for all brain tumors, and 1.06 (95% CI -6.55, 9.30) for gliomas. Our results suggest a positive association between head/neck CT imaging and the risk of childhood brain tumors. These findings contribute to the existing knowledge about the hazards of low dose ionizing radiation in pediatric populations. Further research with more precise dosimetry, including dose distribution within the brain, is needed.

Untargeted metabolomics for acute intra-abdominal infection diagnosis in serum and urine using UHPLC-TripleTOF MS

Introduction

Acute intra-abdominal infection (IAI) is a prevalent and life-threatening condition in general surgery, with significant implications for patient mortality. However, the timely identification of IAI is often hindered by the limitations of current medical laboratory sciences and imaging diagnostics.

Methods

To address this critical issue, we employed metabolomics to identify early biomarkers for IAI. In this study, we enrolled a cohort of 30 IAI patients and 20 healthy volunteers. Following preliminary experimental processing, all serum and urinary samples were subjected to ultrahigh performance liquid chromatography-triple time-of-flight mass spectrometry analysis. Initial metabolite profiling was conducted using total ion current chromatography and principal component analysis. Differential metabolites were subsequently identified through Student's t-test, partial least squares discriminant analysis, and support vector machine. Hierarchical clustering analysis was then applied to assess the discriminatory power of the selected metabolites. Based on receiver operating characteristic curve analysis, we identified the most promising biomarkers, which were further subjected to enrichment analysis. Additionally, we stratified patients according to the severity and etiology of IAI to explore potential differences among these subgroups.

Results

Our findings revealed five serum and two urinary metabolites as potential biomarkers for IAI. The serum biomarkers were associated with the Fatty Acid Biosynthesis pathway, while the urinary biomarkers were linked to the Catecholamine Biosynthesis pathway. Notably, no significant differences were observed among the three types of IAI or the seven etiologies studied.

Discussion

For individuals at risk of IAI, regular screening of these biomarkers could facilitate the early and convenient identification of the condition, thereby improving patient outcomes.

A Historical Survey of Key Epidemiological Studies of Ionizing Radiation Exposure

In this article we review the history of key epidemiological studies of populations exposed to ionizing radiation. We highlight historical and recent findings regarding radiation-associated risks for incidence and mortality of cancer and non-cancer outcomes with emphasis on study design and methods of exposure assessment and dose estimation along with brief consideration of sources of bias for a few of the more important studies. We examine the findings from the epidemiological studies of the Japanese atomic bomb survivors, persons exposed to radiation for diagnostic or therapeutic purposes, those exposed to environmental sources including Chornobyl and other reactor accidents, and occupationally exposed cohorts. We also summarize results of pooled studies. These summaries are necessarily brief, but we provide references to more detailed information. We discuss possible future directions of study, to include assessment of susceptible populations, and possible new populations, data sources, study designs and methods of analysis.

Use Cases Requiring Privacy-Preserving Record Linkage in Paediatric Oncology

Large datasets in paediatric oncology are inherently rare. Therefore, it is paramount to fully exploit all available data, which are distributed over several resources, including biomaterials, images, clinical trials, and registries. With privacy-preserving record linkage (PPRL), personalised or pseudonymised datasets can be merged, without disclosing the patients' identities. Although PPRL is implemented in various settings, use case descriptions are currently fragmented and incomplete. The present paper provides a comprehensive overview of current and future use cases for PPRL in paediatric oncology. We analysed the literature, projects, and trial protocols, identified use cases along a hypothetical patient journey, and discussed use cases with paediatric oncology experts. To structure PPRL use cases, we defined six key dimensions: distributed personalised records, pseudonymisation, distributed pseudonymised records, record linkage, linked data, and data analysis. Selected use cases were described (a) per dimension and (b) on a multi-dimensional level. While focusing on paediatric oncology, most aspects are also applicable to other (particularly rare) diseases. We conclude that PPRL is a key concept in paediatric oncology. Therefore, PPRL strategies should already be considered when starting research projects, to avoid distributed data silos, to maximise the knowledge derived from collected data, and, ultimately, to improve outcomes for children with cancer.

Cranial Computer Tomography with Photon Counting and Energy-Integrated Detectors: Objective Comparison in the Same Patients

This study provides an objective comparison of cranial computed tomography (CT) imaging quality and radiation dose between photon counting detectors (PCCTs) and energy-integrated detectors (EIDs). We retrospectively analyzed 158 CT scans from 76 patients, employing both detector types on the same individuals to ensure a consistent comparison. Our analysis focused on the Computed Tomography Dose Index and the Dose-Length Product together with the contrast-to-noise ratio and the signal-to-noise ratio for brain gray and white matter. We utilized standardized imaging protocols and consistent patient positioning to minimize variables. PCCT showed a potential for higher image quality and lower radiation doses, as highlighted by this study, thus achieving diagnostic clarity with reduced radiation exposure, underlining its significance in patient care, particularly for patients requiring multiple scans. The results demonstrated that while both systems were effective, PCCT offered enhanced imaging and patient safety in neuroradiological evaluations.

Finite-Sample Bias of the Linear Excess Relative Risk in Cohort Studies of Computed Tomography-Related Radiation Exposure and Cancer

The linear excess relative risk (ERR) is the most commonly reported measure of association in radiation epidemiological studies, when individual dose estimates are available. While the asymptotic properties of the ERR estimator are well understood, there is evidence of small sample bias in case-control studies of treatment-related radiation exposure and second cancer risk. Cohort studies of cancer risk after exposure to low doses of radiation from diagnostic procedures, e.g., computed tomography (CT) examinations, typically have small numbers of cases and risks are small. Therefore, understanding the properties of the estimated ERR is essential for interpretation and analysis of such studies. We present results of a simulation study that evaluates the finite-sample bias of the ERR estimated by time-to-event analyses and its confidence interval using simulated data, resembling a retrospective cohort study of radiation-related leukemia risk after CT examinations in childhood and adolescence. Furthermore, we evaluate how the Firth-corrected estimator reduces the finite-sample bias of the classical estimator. We show that the ERR is overestimated by about 30% for a cohort of about 150,000 individuals, with 42 leukemia cases observed on average. The bias is reduced for higher baseline incidence rates and for higher values of the true ERR. As the number of cases increases, the ERR is approximately unbiased. The Firth correction reduces the bias for all cohort sizes to generally around or under 5%. Epidemiological studies showing an association between radiation exposure from pediatric CT and cancer risk, unless very large, may overestimate the magnitude of the relationship, while there is no evidence of an increased chance for false-positive results. Conducting large studies, perhaps by pooling individual studies to increase the number of cases, should be a priority. If this is not possible, Firth correction should be applied to reduce small-sample bias.

Protein structuromics: A new method for protein structure-function crosstalk in glioma

Glioma is a type of tumor that starts in the glial cells of the brain or spine. Since the 1800s, when the disease was first named, its survival rates have always been unsatisfactory. Despite great advances in molecular biology and traditional treatment methods, many questions regarding cancer occurrence and the underlying mechanism remain to be answered. In this study, we assessed the protein structural features of 20 oncogenes and 20 anti-oncogenes via protein structure and dynamic analysis methods and 3D structural and systematic analyses of the structure-function relationships of proteins. All of these results directly indicate that unfavorable group proteins show more complex structures than favorable group proteins. As the tumor cell microenvironment changes, the balance of oncogene-related and anti-oncogene-related proteins is disrupted, and most of the structures of the two groups of proteins will be disrupted. However, more unfavorable group proteins will maintain and refold to achieve their correct shape faster and perform their functions more quickly than favorable group proteins, and the former thus support cancer development. We hope that these analyses will help promote mechanistic research and the development of new treatments for glioma.