Optimization of Energy Combination for Gold-based Contrast Agents below K-edges in Dual-energy Micro-CT

Shock index as a predictor for mortality in trauma patients: a systematic review and meta-analysis

PURPOSE

The primary aim was to determine whether a shock index (SI) ≥ 1 in adult trauma patients was associated with increased in-hospital mortality compared to an SI < 1.

METHODS

This systematic review including a meta-analysis was performed in accordance with the PRISMA guidelines. EMBASE, MEDLINE, and Cochrane Library were searched, and two authors independently screened articles, performed the data extraction, and assessed risk of bias. Studies were included if they reported in-hospital, 30-day, or 48-h mortality, length of stay, massive blood transfusion or ICU admission in trauma patients with SI recorded at arrival in the emergency department or trauma center. Risk of bias was assessed using the Newcastle-Ottawa Scale, and the strength and quality of the body of evidence according to GRADE. Data were pooled using a random effects model. Inter-rater reliability was assessed with Cohen's kappa.

RESULTS

We screened 1350 citations with an inter-rater reliability of 0.90. Thirty-eight cohort studies were included of which 14 reported the primary outcome. All studies reported a significant higher in-hospital mortality in adult trauma patients with an SI ≥ 1 compared to those having an SI < 1. Twelve studies involving a total of 348,687 participants were included in the meta-analysis. The pooled risk ratio (RR) of in-hospital mortality was 4.15 (95% CI 2.96-5.83). The overall quality of evidence was low.

CONCLUSIONS

This systematic review found a fourfold increased risk of in-hospital mortality in adult trauma patients with an initial SI ≥ 1 in the emergency department or trauma center.

Clinical Relevance of Gastroesophageal Cancer Associated SNPs for Oncologic Outcome After Curative Surgery

BACKGROUND

Gastric and esophageal cancers are malignant diseases with rising importance in Western countries. To improve oncologic outcome after surgery, it is essential to understand the relevance of germline mutations. The aim of the study was to identify and distinguish clinically relevant single-nucleotide polymorphisms (SNPs).

PATIENTS AND METHODS

In total, 190 patients with curative oncological resections of gastric and distal esophageal adenocarcinomas at Heidelberg University Hospital were eligible for this study. Outcome differences were determined for each SNP by analysis of clinical variables, survival, and mRNA expression levels.

RESULTS

Significant survival differences were found on univariate analysis for usual prognostic variables (such as pTNM) and for six SNPs. On multivariate survival analysis, the SNPs rs12268840 (intron variant of MGMT, p = 0.045) and rs9972882 (intron variant of STARD3 and eQTL of PGAP3, p = 0.030) were independent and significant survival predictors along with R status and pT/pN category. Group TT of rs12268840 had the highest rate of second primary carcinoma (30.4%, p = 0.0003), lowest expression of MGMT based on cis-eQTL analysis in normal gastroesophageal tissue (p = 1.99 × 10-17), and worst oncologic outcome. Group AA of rs9972882 had the highest rate of distant metastases pM1 (42.9%, p = 0.0117), highest expression of PGAP3 (p = 1.29 × 10-15), and worst oncologic outcome.

CONCLUSIONS

Two intron variant SNPs of MGMT and STARD3 were identified that were significant survival predictors and may influence tumor biology. The data indicate that DNA methylation (MGMT) and malfunction of GPI anchoring (PGAP3) are distinct mechanisms that are relevant for tumor progression and relapse.

Spectral Photon Counting CT: Imaging Algorithms and Performance Assessment

Photon counting x-ray detectors (PCDs) with spectral capabilities have the potential to revolutionize computed tomography (CT) for medical imaging. The ideal PCD provides accurate energy information for each incident x-ray, and at high spatial resolution. This information enables material-specific imaging, enhanced radiation dose efficiency, and improved spatial resolution in CT images. In practice, PCDs are affected by non-idealities, including limited energy resolution, pulse pileup, and cross talk due to charge sharing, K-fluorescence, and Compton scattering. In order to maximize their performance, PCDs must be carefully designed to reduce these effects and then later account for them during correction and post-acquisition steps. This review article examines algorithms for using PCDs in spectral CT applications, including how non-idealities impact image quality. Performance assessment metrics that account for spatial resolution and noise such as the detective quantum efficiency (DQE) can be used to compare different PCD designs, as well as compare PCDs with conventional energy integrating detectors (EIDs). These methods play an important role in enhancing spectral CT images and assessing the overall performance of PCDs.

Adaptive Nonlocal Means Method for Denoising Basis Material Images From Dual-Energy Computed Tomography

We propose an adaptive nonlocal means approach for image-domain material decomposition in low-dose dual-energy micro-computed tomography. The key idea is to create a distribution map for decomposition error and assign a smooth weight for a given pixel. This method is applied to the decomposed images of 3 basis materials: bone, soft tissue, and gold in our applications. We assume that bone and gold cannot coexist in the same pixel and regroup these basis materials into 2 categories. For soft tissue, the proposed algorithm is implemented in a noniterative mode. For bone and gold, an iterative mode is used and followed by a postiteration process. Both our numerical simulation and in vivo preclinical experiment results show that the proposed adaptive nonlocal means outperforms other state-of-the-art denoising algorithms, such as the original nonlocal means and total variation minimization methods.