Towards Consistency in Pediatric Brain Tumor Measurements: Challenges, Solutions, and the Role of AI-Based Segmentation.

MR imaging is central to the assessment of tumor burden and changes over time in neuro-oncology. Several response assessment guidelines have been set forth by the Response Assessment in Pediatric Neuro-Oncology (RAPNO) working groups in different tumor histologies; however, the visual delineation of tumor components using MRIs is not always straightforward, and complexities not currently addressed by these criteria can introduce inter- and intra-observer variability in manual assessments. Differentiation of non-enhancing tumor from peritumoral edema, mild enhancement from absence of enhancement, and various cystic components can be challenging; particularly given a lack of sufficient and uniform imaging protocols in clinical practice. Automated tumor segmentation with artificial intelligence (AI) may be able to provide more objective delineations, but rely on accurate and consistent training data created manually (ground truth). Herein, this paper reviews existing challenges and potential solutions to identifying and defining subregions of pediatric brain tumors (PBTs) that are not explicitly addressed by current guidelines. The goal is to assert the importance of defining and adopting criteria for addressing these challenges, as it will be critical to achieving standardized tumor measurements and reproducible response assessment in PBTs, ultimately leading to more precise outcome metrics and accurate comparisons among clinical studies.

Abbreviated Breast MRI for Supplemental Screening in Patients With Dense Breasts: Comparison of Baseline Versus Subsequent-Round Examinations.

Background: Abbreviated breast MRI (AB-MR) achieves a higher cancer detection rate (CDR) versus digital breast tomosynthesis when applied for baseline (i.e. first-round) supplemental screening in individuals with dense breasts. Limited literature has evaluated subsequent (i.e., sequential) AB-MR screening rounds. Objectives: This study aimed to compare outcomes between baseline and subsequent rounds of screening AB-MR in individuals with dense breasts at otherwise average risk of breast cancer. Methods: This retrospective study included patients with dense breasts and at otherwise average breast-cancer risk who underwent AB-MR for supplemental screening between December 20, 2016 and May 10, 2023. Clinical interpretations and results of recommended biopsies for AB-MR examinations were extracted from the EMR. Baseline and subsequent-round AB-MR examinations were compared. Results: The final sample included 2585 AB-MR examinations (2007 baseline, 578 subsequent-round) performed for supplemental screening in 2007 women (mean age, 57.1 years) with dense breasts. Among baseline examinations, 1658 (82.6%) were assessed as BI-RADS category 1 or 2, 171 (8.5%) as category 3, and 178 (8.9%) as category 4 or 5. Among subsequent-round examinations, 533 (92.2%) were assessed as BI-RADS category 1 or 2, 20 (3.5%) as category 3, and 25 (4.3%) as category 4 or 5 (p<.001). Abnormal interpretation rate (AIR) was 17.4% (349/2007) among baseline examinations, versus 7.8% (45/578) among subsequent-round examinations (p<.001). Among baseline examinations, PPV2 was 21.3% (38/178), PPV3 was 26.6% (38/143), and CDR was 18.9 per 1000 (38/2007). Among subsequent-round examinations PPV2 was 28.0% (7/25) (p=.45), PPV3 was 29.2% (7/24) (p=.81), and CDR was 12.1 per 1000 (7/578) (p=.37). All 45 cancers diagnosed by baseline or subsequent-round AB-MR were stage 0 or 1. Seven cancers diagnosed by subsequent-round AB-MR had a mean interval since prior AB-MR of 872 days, size of 0.3-1.2 cm, and node-negative status at surgical axillary evaluation. Conclusion: Subsequent rounds of AB-MR screening in individuals with dense breasts had lower AIR compared to baseline examinations while maintaining high CDR. All cancers detected by subsequent-round examinations were early-stage node-negative cancers. Clinical Impact: The findings support sequential AB-MR for supplemental screening in individuals with dense breasts. Further investigations are warranted to optimize the screening interval.

Training and Comparison of nnU-Net and DeepMedic Methods for Autosegmentation of Pediatric Brain Tumors.

BACKGROUND AND PURPOSE: Tumor segmentation is essential in surgical and treatment planning and response assessment and monitoring in pediatric brain tumors, the leading cause of cancer-related death among children. However, manual segmentation is time-consuming and has high interoperator variability, underscoring the need for more efficient methods. After training, we compared 2 deep-learning-based 3D segmentation models, DeepMedic and nnU-Net, with pediatric-specific multi-institutional brain tumor data based on multiparametric MR images. MATERIALS AND METHODS: Multiparametric preoperative MR imaging scans of 339 pediatric patients (n = 293 internal and n = 46 external cohorts) with a variety of tumor subtypes were preprocessed and manually segmented into 4 tumor subregions, ie, enhancing tumor, nonenhancing tumor, cystic components, and peritumoral edema. After training, performances of the 2 models on internal and external test sets were evaluated with reference to ground truth manual segmentations. Additionally, concordance was assessed by comparing the volume of the subregions as a percentage of the whole tumor between model predictions and ground truth segmentations using the Pearson or Spearman correlation coefficients and the Bland-Altman method. RESULTS: The mean Dice score for nnU-Net internal test set was 0.9 (SD, 0.07) (median, 0.94) for whole tumor; 0.77 (SD, 0.29) for enhancing tumor; 0.66 (SD, 0.32) for nonenhancing tumor; 0.71 (SD, 0.33) for cystic components, and 0.71 (SD, 0.40) for peritumoral edema, respectively. For DeepMedic, the mean Dice scores were 0.82 (SD, 0.16) for whole tumor; 0.66 (SD, 0.32) for enhancing tumor; 0.48 (SD, 0.27) for nonenhancing tumor; 0.48 (SD, 0.36) for cystic components, and 0.19 (SD, 0.33) for peritumoral edema, respectively. Dice scores were significantly higher for nnU-Net (P ≤ .01). Correlation coefficients for tumor subregion percentage volumes were higher (0.98 versus 0.91 for enhancing tumor, 0.97 versus 0.75 for nonenhancing tumor, 0.98 versus 0.80 for cystic components, 0.95 versus 0.33 for peritumoral edema in the internal test set). Bland-Altman plots were better for nnU-Net compared with DeepMedic. External validation of the trained nnU-Net model on the multi-institutional Brain Tumor Segmentation Challenge in Pediatrics (BraTS-PEDs) 2023 data set revealed high generalization capability in the segmentation of whole tumor, tumor core (a combination of enhancing tumor, nonenhancing tumor, and cystic components), and enhancing tumor with mean Dice scores of 0.87 (SD, 0.13) (median, 0.91), 0.83 (SD, 0.18) (median, 0.89), and 0.48 (SD, 0.38) (median, 0.58), respectively. CONCLUSIONS: The pediatric-specific data-trained nnU-Net model is superior to DeepMedic for whole tumor and subregion segmentation of pediatric brain tumors.

The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs).

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. The MICCAI Brain Tumor Segmentation (BraTS) Challenge is a landmark community benchmark event with a successful history of 12 years of resource creation for the segmentation and analysis of adult glioma. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge, which represents the first BraTS challenge focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The BraTS-PEDs 2023 challenge focuses on benchmarking the development of volumentric segmentation algorithms for pediatric brain glioma through standardized quantitative performance evaluation metrics utilized across the BraTS 2023 cluster of challenges. Models gaining knowledge from the BraTS-PEDs multi-parametric structural MRI (mpMRI) training data will be evaluated on separate validation and unseen test mpMRI dataof high-grade pediatric glioma. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors.

A multi-institutional pediatric dataset of clinical radiology MRIs by the Children's Brain Tumor Network.

Pediatric brain and spinal cancers remain the leading cause of cancer-related death in children. Advancements in clinical decision-support in pediatric neuro-oncology utilizing the wealth of radiology imaging data collected through standard care, however, has significantly lagged other domains. Such data is ripe for use with predictive analytics such as artificial intelligence (AI) methods, which require large datasets. To address this unmet need, we provide a multi-institutional, large-scale pediatric dataset of 23,101 multi-parametric MRI exams acquired through routine care for 1,526 brain tumor patients, as part of the Children's Brain Tumor Network. This includes longitudinal MRIs across various cancer diagnoses, with associated patient-level clinical information, digital pathology slides, as well as tissue genotype and omics data. To facilitate downstream analysis, treatment-naïve images for 370 subjects were processed and released through the NCI Childhood Cancer Data Initiative via the Cancer Data Service. Through ongoing efforts to continuously build these imaging repositories, our aim is to accelerate discovery and translational AI models with real-world data, to ultimately empower precision medicine for children.

Adding Value to Liquid Biopsy for Brain Tumors: The Role of Imaging.

Clinical management in neuro-oncology has changed to an integrative approach that incorporates molecular profiles alongside histopathology and imaging findings. While the World Health Organization (WHO) guideline recommends the genotyping of informative alterations as a routine clinical practice for central nervous system (CNS) tumors, the acquisition of tumor tissue in the CNS is invasive and not always possible. Liquid biopsy is a non-invasive approach that provides the opportunity to capture the complex molecular heterogeneity of the whole tumor through the detection of circulating tumor biomarkers in body fluids, such as blood or cerebrospinal fluid (CSF). Despite all of the advantages, the low abundance of tumor-derived biomarkers, particularly in CNS tumors, as well as their short half-life has limited the application of liquid biopsy in clinical practice. Thus, it is crucial to identify the factors associated with the presence of these biomarkers and explore possible strategies that can increase the shedding of these tumoral components into biological fluids. In this review, we first describe the clinical applications of liquid biopsy in CNS tumors, including its roles in the early detection of recurrence and monitoring of treatment response. We then discuss the utilization of imaging in identifying the factors that affect the detection of circulating biomarkers as well as how image-guided interventions such as focused ultrasound can help enhance the presence of tumor biomarkers through blood-brain barrier (BBB) disruption.

Statistical plots in oncologic imaging, a primer for neuroradiologists.

The simplest approach to convey the results of scientific analysis, which can include complex comparisons, is typically through the use of visual items, including figures and plots. These statistical plots play a critical role in scientific studies, making data more accessible, engaging, and informative. A growing number of visual representations have been utilized recently to graphically display the results of oncologic imaging, including radiomic and radiogenomic studies. Here, we review the applications, distinct properties, benefits, and drawbacks of various statistical plots. Furthermore, we provide neuroradiologists with a comprehensive understanding of how to use these plots to effectively communicate analytical results based on imaging data.

Computed tomography of the spleen in chickens.

The avian spleen is an important immune organ in birds and its size can be used as an index of immune system responses in different conditions. Based on the lack of knowledge in computed tomography of the spleen in chickens, the present study was conducted to assess the inter-and intraobserver reliability in the measurement of the spleen dimensions and attenuation, as well as the feasibility of utilization of these measurements as a predictor of different diseases. For these purposes, the spleens of 47 chickens were included in the study. Two observers measured the dimensions and attenuations of the spleen, which were finally compared with the clinical diagnosis. The results showed an excellent interobserver reliability in the length, width, and height of the spleen (ICC: 0.944, 0.906, and 0.938, retrospectively), and a good interobserver reliability was observed during the evaluation of the average Hounsfield units of the spleen (ICC: 0.818). The intraobserver reliability was excellent in all the measurements (ICC > 0.940). Additionally, no statistical differences were detected in the spleen size and attenuation between the normal and diseased groups. Based on the present results, the computed tomographic measurements of the spleen could not predict the clinical diseases of the chickens; however, the low rates of the inter- and intraobserver variability suggest the reliable utilization of these computed tomographic measurements in routine clinical application and follow-up examinations.

Ultrasonographic features of Marek's disease in a chicken.

A 4-year-old chicken was presented with a history of anorexia, depression, and blindness. An ultrasound examination of the coelomic cavity was performed that revealed splenomegaly, hepatic nodules, and hypoechoic thickening of the intestinal wall. Ultrasonography of the coelomic cavity was done and revealed splenomegaly, nodular hepatic changes, and hypoechoic thickening of the intestinal wall. A diagnosis of Marek's disease was made based on the history and extension of the abdominal organ changes and confirmed by histopathology. This study describes an ultrasonographic appearance of Marek's disease in a chicken and emphasizes the importance and benefits of ultrasonography in staging the progression of Marek's disease.

Aerosol vaccination of chicken pullets with irradiated avian pathogenic Escherichia coli induces a local immunostimulatory effect.

The present study investigated the expression of cytokines and cellular changes in chickens following vaccination with irradiated avian pathogenic Escherichia coli (APEC) and/or challenge. Four groups of 11-week-old pullets, each consisting of 16 birds were kept separately in isolators before they were sham inoculated (N), challenged only (C), vaccinated (V) or vaccinated and challenged (V+C). Vaccination was performed using irradiated APEC applied via aerosol. For challenge, the homologous strain was administered intratracheally. Birds were sacrificed on 3, 7, 14 and 21 days post challenge (dpc) to examine lesions, organ to body weight ratios and bacterial colonization. Lung and spleen were sampled for investigating gene expression of cytokines mediating inflammation by RT-qPCR and changes in the phenotype of subsets of mononuclear cells by flow cytometry. After re-stimulation of immune cells by co-cultivation with the pathogen, APEC-specific IFN-γ producing cells were determined. Challenged only birds showed more severe pathological and histopathological lesions, a higher probability of bacterial re-isolation and higher organ to body weight ratios compared to vaccinated and challenged birds. In the lung, an upregulation of IL-1ß and IL-6 following vaccination and/or challenge at 3 dpc was observed, whereas in the spleen IL-1ß was elevated. Changes were observed in macrophages and TCR-γδ+ cells within 7 dpc in spleen and lung of challenged birds. Furthermore, an increase of CD4+ cells in spleen and a rise of Bu-1+ cells in lung were present in vaccinated and challenged birds at 3 dpc. APEC re-stimulated lung and spleen mononuclear cells from only challenged pullets showed a significant increase of IFN-γ+CD8α+ and IFN-γ+TCR-γδ+ cells. Vaccinated and challenged chickens responded with a significant increase of IFN-γ+CD8α+ T cells in the lung and IFN-γ+TCR-γδ+ cells in the spleen. Re-stimulation of lung mononuclear cells from vaccinated birds resulted in a significant increase of both IFN-γ+CD8α+ and IFN-γ+TCR-γδ+ cells. In conclusion, vaccination with irradiated APEC caused enhanced pro-inflammatory response as well as the production of APEC-specific IFN-γ-producing γδ and CD8α T cells, which underlines the immunostimulatory effect of the vaccine in the lung. Hence, our study provides insights into the underlying immune mechanisms that account for the defense against APEC.

Other Novel PET Radiotracers for Breast Cancer.

Many novel PET radiotracers have demonstrated potential use in breast cancer. Although not currently approved for clinical use in the breast cancer population, these innovative imaging agents may one day play a role in the diagnosis, staging, management, and even treatment of breast cancer.

Automated tumor segmentation and brain tissue extraction from multiparametric MRI of pediatric brain tumors: A multi-institutional study.

Background: Brain tumors are the most common solid tumors and the leading cause of cancer-related death among all childhood cancers. Tumor segmentation is essential in surgical and treatment planning, and response assessment and monitoring. However, manual segmentation is time-consuming and has high interoperator variability. We present a multi-institutional deep learning-based method for automated brain extraction and segmentation of pediatric brain tumors based on multi-parametric MRI scans. Methods: Multi-parametric scans (T1w, T1w-CE, T2, and T2-FLAIR) of 244 pediatric patients ( n = 215 internal and n = 29 external cohorts) with de novo brain tumors, including a variety of tumor subtypes, were preprocessed and manually segmented to identify the brain tissue and tumor subregions into four tumor subregions, i.e., enhancing tumor (ET), non-enhancing tumor (NET), cystic components (CC), and peritumoral edema (ED). The internal cohort was split into training ( n = 151), validation ( n = 43), and withheld internal test ( n = 21) subsets. DeepMedic, a three-dimensional convolutional neural network, was trained and the model parameters were tuned. Finally, the network was evaluated on the withheld internal and external test cohorts. Results: Dice similarity score (median ± SD) was 0.91 ± 0.10/0.88 ± 0.16 for the whole tumor, 0.73 ± 0.27/0.84 ± 0.29 for ET, 0.79 ± 19/0.74 ± 0.27 for union of all non-enhancing components (i.e., NET, CC, ED), and 0.98 ± 0.02 for brain tissue in both internal/external test sets. Conclusions: Our proposed automated brain extraction and tumor subregion segmentation models demonstrated accurate performance on segmentation of the brain tissue and whole tumor regions in pediatric brain tumors and can facilitate detection of abnormal regions for further clinical measurements.

Automated Tumor Segmentation and Brain Tissue Extraction from Multiparametric MRI of Pediatric Brain Tumors: A Multi-Institutional Study.

Background: Brain tumors are the most common solid tumors and the leading cause of cancer-related death among all childhood cancers. Tumor segmentation is essential in surgical and treatment planning, and response assessment and monitoring. However, manual segmentation is time-consuming and has high interoperator variability. We present a multi-institutional deep learning-based method for automated brain extraction and segmentation of pediatric brain tumors based on multi-parametric MRI scans. Methods: Multi-parametric scans (T1w, T1w-CE, T2, and T2-FLAIR) of 244 pediatric patients (n=215 internal and n=29 external cohorts) with de novo brain tumors, including a variety of tumor subtypes, were preprocessed and manually segmented to identify the brain tissue and tumor subregions into four tumor subregions, i.e., enhancing tumor (ET), non-enhancing tumor (NET), cystic components (CC), and peritumoral edema (ED). The internal cohort was split into training (n=151), validation (n=43), and withheld internal test (n=21) subsets. DeepMedic, a three-dimensional convolutional neural network, was trained and the model parameters were tuned. Finally, the network was evaluated on the withheld internal and external test cohorts. Results: Dice similarity score (median±SD) was 0.91±0.10/0.88±0.16 for the whole tumor, 0.73±0.27/0.84±0.29 for ET, 0.79±19/0.74±0.27 for union of all non-enhancing components (i.e., NET, CC, ED), and 0.98±0.02 for brain tissue in both internal/external test sets. Conclusions: Our proposed automated brain extraction and tumor subregion segmentation models demonstrated accurate performance on segmentation of the brain tissue and whole tumor regions in pediatric brain tumors and can facilitate detection of abnormal regions for further clinical measurements. Key Points: We proposed automated tumor segmentation and brain extraction on pediatric MRI.The volumetric measurements using our models agree with ground truth segmentations. Importance of the Study: The current response assessment in pediatric brain tumors (PBTs) is currently based on bidirectional or 2D measurements, which underestimate the size of non-spherical and complex PBTs in children compared to volumetric or 3D methods. There is a need for development of automated methods to reduce manual burden and intra- and inter-rater variability to segment tumor subregions and assess volumetric changes. Most currently available automated segmentation tools are developed on adult brain tumors, and therefore, do not generalize well to PBTs that have different radiological appearances. To address this, we propose a deep learning (DL) auto-segmentation method that shows promising results in PBTs, collected from a publicly available large-scale imaging dataset (Children's Brain Tumor Network; CBTN) that comprises multi-parametric MRI scans of multiple PBT types acquired across multiple institutions on different scanners and protocols. As a complementary to tumor segmentation, we propose an automated DL model for brain tissue extraction.

Unsupervised machine learning using K-means identifies radiomic subgroups of pediatric low-grade gliomas that correlate with key molecular markers.

INTRODUCTION: Despite advancements in molecular and histopathologic characterization of pediatric low-grade gliomas (pLGGs), there remains significant phenotypic heterogeneity among tumors with similar categorizations. We hypothesized that an unsupervised machine learning approach based on radiomic features may reveal distinct pLGG imaging subtypes. METHODS: Multi-parametric MR images (T1 pre- and post-contrast, T2, and T2 FLAIR) from 157 patients with pLGGs were collected and 881 quantitative radiomic features were extracted from tumorous region. Clustering was performed using K-means after applying principal component analysis (PCA) for feature dimensionality reduction. Molecular and demographic data was obtained from the PedCBioportal and compared between imaging subtypes. RESULTS: K-means identified three distinct imaging-based subtypes. Subtypes differed in mutational frequencies of BRAF (p < 0.05) as well as the gene expression of BRAF (p<0.05). It was also found that age (p < 0.05), tumor location (p < 0.01), and tumor histology (p < 0.0001) differed significantly between the imaging subtypes. CONCLUSION: In this exploratory work, it was found that clustering of pLGGs based on radiomic features identifies distinct, imaging-based subtypes that correlate with important molecular markers and demographic details. This finding supports the notion that incorporation of radiomic data could augment our ability to better characterize pLGGs.

Radiomics and radiogenomics in pediatric neuro-oncology: A review.

The current era of advanced computing has allowed for the development and implementation of the field of radiomics. In pediatric neuro-oncology, radiomics has been applied in determination of tumor histology, identification of disseminated disease, prognostication, and molecular classification of tumors (ie, radiogenomics). The field also comes with many challenges, such as limitations in study sample sizes, class imbalance, generalizability of the methods, and data harmonization across imaging centers. The aim of this review paper is twofold: first, to summarize existing literature in radiomics of pediatric neuro-oncology; second, to distill the themes and challenges of the field and discuss future directions in both a clinical and technical context.

Exploration of Shoulder Abscess Association With Prompt Aggregatibacter aphrophilus Growth in Infective Endocarditis.

Aggregatibacter aphrophilus, formerly known as Haemophilus aphrophilus, is one member of a group of bacteria referred to as HACEK (Haemophilus, Aggregatibacter, Cardiobacterium, Eikenella, Kingella) organisms. Infections from any of the HACEK organisms typically lead to very poor outcomes and can be difficult to manage, especially when complicated by intracranial hemorrhage (ICH). HACEK organisms can also be difficult to grow on blood cultures, and A. aphrophilus is rarely seen, if at all. Traditionally, most laboratories follow an extended incubation protocol of 14 to 21 days to aid the growth of HACEK bacteria. Herein we report a case of infective endocarditis where A. aphrophilus resulted on blood culture in three days, in a patient with a right shoulder abscess, complicated by septic embolization leading to ICH. We explore a potential link between the prompt growth of A. aphrophilus on blood culture and the presence of the right shoulder abscess.

Production of interferon gamma and interleukin 17A in chicken T-cell subpopulations hallmarks the stimulation with live, irradiated and killed avian pathogenic Escherichia coli.

Avian pathogenic Escherichia coli (APEC) causes colibacillosis with different clinical manifestations. The disease is associated with compromised animal welfare and results in substantial economic losses in poultry production worldwide. So far, immunological mechanisms of protection against colibacillosis are not comprehensively resolved. Therefore, the present study aimed to use an ex vivo model applying chicken mononuclear cells stimulated by live and inactivated APEC. For this purpose, an 8-color flow cytometry panel was set up to target viable chicken immune cells including CD45+, CD8α+, CD4+, TCR-γδ+, Bu-1+ cells and monocytes/macrophages along with the cytokines interferon gamma (IFN-γ) or interleukin 17A (IL-17A). The 8-color flow cytometry panel was applied to investigate the effect of live and two different types of inactivated APEC (formalin-killed APEC and irradiated APEC) on the cellular immune response. For that, mononuclear cells from spleen, lung and blood of 10-week-old specific pathogen-free layer birds were isolated and stimulated with live, irradiated or killed APEC. Intracellular cytokine staining and RT-qPCR assays were applied for the detection of IFN-γ and IL-17A protein level, as well as at mRNA level for spleenocytes. Ex vivo stimulation of isolated splenocytes, lung and peripheral blood mononuclear cells (PBMCs) from chickens with live, irradiated or killed APEC showed an increasing number of IFN-γ and IL-17A producing cells at protein and mRNA level. Phenotyping of the cells from blood and organs revealed that IFN-γ and IL-17A were mainly produced by CD8α+, TCR-γδ+ T cells as well as CD4+ T cells following stimulation with APEC. Expression level of cytokines were very similar following stimulation with live and irradiated APEC but lower when killed APEC were applied. Consequently, in the present study, an ex vivo model using mononuclear cells of chickens was applied to investigate the cellular immune response against APEC. The results suggest the relevance of IFN-γ and IL-17A production in different immune cells following APEC infection in chickens which needs to be further investigated in APEC primed birds.

Applications of Radiomics and Radiogenomics in High-Grade Gliomas in the Era of Precision Medicine.

Machine learning (ML) integrated with medical imaging has introduced new perspectives in precision diagnostics of high-grade gliomas, through radiomics and radiogenomics. This has raised hopes for characterizing noninvasive and in vivo biomarkers for prediction of patient survival, tumor recurrence, and genomics and therefore encouraging treatments tailored to individualized needs. Characterization of tumor infiltration based on pre-operative multi-parametric magnetic resonance imaging (MP-MRI) scans may allow prediction of the loci of future tumor recurrence and thereby aid in planning the course of treatment for the patients, such as optimizing the extent of resection and the dose and target area of radiation. Imaging signatures of tumor genomics can help in identifying the patients who benefit from certain targeted therapies. Specifying molecular properties of gliomas and prediction of their changes over time and with treatment would allow optimization of treatment. In this article, we provide neuro-oncology, neuropathology, and computational perspectives on the promise of radiomics and radiogenomics for allowing personalized treatments of patients with gliomas and discuss the challenges and limitations of these methods in multi-institutional clinical trials and suggestions to mitigate the issues and the future directions.

Effectiveness of Tranexamic Acid in Reducing Hemorrhage in Isolated Blunt Solid Organ Injury.

INTRODUCTION: There is considerable interest in the use of tranexamic acid (TXA) for the control of hemorrhages in trauma patients. Multiple recent studies found that TXA used in the setting of a suspected significant hemorrhage in trauma patients significantly reduced mortality. To date, there are no cited studies that specifically address hemorrhage due to solid organ injury (i.e., kidneys, liver, and spleen) and TXA use in humans. Our current research addresses whether TXA is effective in reducing complications and mortality from traumatic hemorrhage in the setting of a specific solid organ injury. METHODS: We conducted a retrospective observational cohort study utilizing propensity score matching at Arrowhead Regional Medical Center (ARMC) from February 1, 2009 to February 1, 2019. This study period marks five years prior to and five years after February 1, 2004, which is the date when TXA first started to be used at ARMC in the management of traumatic hemorrhage. We compared for statistical difference between corresponding injury types in the TXA and non-TXA groups. RESULTS: Before the propensity matching, there were 123 patients who received TXA and 118 patients who did not. After propensity match for age and injury severity score (ISS), 35 patients were included in each group. We found no statistically significant difference between TXA and non-TXA treatment groups in terms of mortality at 24 hours (p-value=0.4945), mortality at 48 hours (p-value=0.4945), and mortality at 28 days (p-value=0.7426). We found no statistically significant difference between the need for interventional radiology intervention at 72 hours (p-value=0.3932), surgical intervention at 72 hours (p-value=0.2123) and possible TXA related complications (p-value=1). CONCLUSION: Although prior studies showed that TXA use in the setting of trauma may be beneficial, the specific candidate-selection criteria remain unclear. The results of our study suggest that the benefit from TXA in the setting of the isolated splenic, liver, and or renal injury may be negligible. We believe that this first-of-its-kind study adds to the growing body of knowledge about the utility of TXA and helps guide patient-selection criteria.

Antimicrobial Resistance Profiling of Gallibacterium anatis from Layers Reveals High Number of Multiresistant Strains and Substantial Variability Even Between Isolates from the Same Organ.

In this investigation, data on antimicrobial resistance (AMR) profiles of 213 Gallibacterium anatis isolates were determined from 93 laying hens originating from 39 flocks. Each flock was sampled three times during its life time for the presence of G. anatis. The broth microdilution method was applied comprising 21 antimicrobial substances. Multidrug resistance was found in 96.2% of the G. anatis isolates. Most of the isolates were resistant to tetracycline (89.2%), tylosin (94.8%), enrofloxacin (58.2%), nalidixic acid (77.4%), and sulfamethoxazole (77.0%). Resistance against antimicrobial substances increased significantly with the age of birds. A total of 99 different AMR profiles were detected. On flock level, different AMR profiles were found in 71.8% of the flocks independent of the sampling time point. On bird level, identical AMR profiles were mostly found in isolates originating from the same organ of a single bird, but 22 such paired isolates differed in their AMR profile. Variations of AMR profiles were found within isolates from a single bird, but from different organs. Isolates from systemic organs were significantly more resistant to different antimicrobial substances compared to isolates from the reproductive tract. No influence could be found in regard to an increase of resistance and applied antibiotic treatment.