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The complexity of brain tissue requires skillful technicians and expert medical doctors to manually analyze and diagnose Glioma brain tumors using multiple Magnetic Resonance (MR) images with multiple modalities. Unfortunately, manual diagnosis suffers from its lengthy process, as well as elevated cost. With this type of cancerous disease, early detection will increase the chances of suitable medical procedures leading to either a full recovery or the prolongation of the patient's life. This has increased the efforts to automate the detection and diagnosis process without human intervention, allowing the detection of multiple types of tumors from MR images. This research paper proposes a multi-class Glioma tumor classification technique using the proposed deep-learning-based features with the Support Vector Machine (SVM) classifier. A deep convolution neural network is used to extract features of the MR images, which are then fed to an SVM classifier. With the proposed technique, a 96.19% accuracy was achieved for the HGG Glioma type while considering the FLAIR modality and a 95.46% for the LGG Glioma tumor type while considering the T2 modality for the classification of four Glioma classes (Edema, Necrosis, Enhancing, and Non-enhancing). The accuracies achieved using the proposed method were higher than those reported by similar methods in the extant literature using the same BraTS dataset. In addition, the accuracy results obtained in this work are better than those achieved by the GoogleNet and LeNet pre-trained models on the same dataset.Delineation of resected brain cavities on magnetic resonance images (MRIs) of epilepsy surgery patients is essential for neuroimaging/neurophysiology studies investigating biomarkers of the epileptogenic zone. The gold standard to delineate the resection on MRI remains manual slice-by-slice tracing by experts. Here, we proposed and validated a semiautomated MRI segmentation pipeline, generating an accurate model of the resection and its anatomical labeling, and developed a graphical user interface (GUI) for user-friendly usage. We retrieved pre- and postoperative MRIs from 35 patients who had focal epilepsy surgery, implemented a region-growing algorithm to delineate the resection on postoperative MRIs and tested its performance while varying different tuning parameters. Similarity between our output and hand-drawn gold standards was evaluated via dice similarity coefficient (DSC; range 0-1). Additionally, the best segmentation pipeline was trained to provide an automated anatomical report of the resection (based on presurgical brain atlas). We found that the best-performing set of parameters presented DSC of 0.83 (0.72-0.85), high robustness to seed-selection variability and anatomical accuracy of 90% to the clinical postoperative MRI report. We presented a novel user-friendly open-source GUI that implements a semiautomated segmentation pipeline specifically optimized to generate resection models and their anatomical reports from epilepsy surgery patients, while minimizing user interaction.Malignant pleural effusion (MPE) is a common complication of thoracic and extrathoracic malignancies and is associated with high mortality. Treatment is mainly palliative, with symptomatic management achieved via effusion drainage and pleurodesis. Pleurodesis may be hastened by administering a sclerosing agent through a thoracostomy tube, thoracoscopy, or an indwelling pleural catheter (IPC). Over the last decade, several randomized controlled studies shaped the current management of MPE in favor of an outpatient-based approach with a notable increase in IPC usage. Patient preferences remain essential in choosing optimal therapy, especially when the lung is expandable. In this article, we reviewed the last 10 to 15 years of MPE literature with a particular focus on the diagnosis and evolving management.Occult hepatitis C virus infection (OCI) is the absence of HCV RNA in serum and the presence of actively replicating HCV RNA in hepatocytes and peripheral blood mononuclear cells (PBMCs), as evidenced by the presence of antigenomic negative sense single-stranded RNA. This study aimed to determine the prevalence of OCI in Egyptian lymphoma patients and assess changes in biochemical parameters in patients with confirmed OCI. The current study was conducted on 100 apparently healthy subjects as control group and 100 patients with lymphoma as a case group. HCV RNA was extracted and detected in both plasma and PBMCs using qRT-PCR. Total protein, albumin, ALT, AST, and total and direct bilirubin were measured in serum. OCI was detected in 6% of the patient group. OCI patients had lower levels of total protein and serum albumin and higher ALT and AST compared with lymphoma patients without OCI. Our study revealed that six out of 100 patients with lymphoma disorders had occult HCV infection (6%). Therefore, the possibility of this infection should be considered in patients with lymphoma.Unstable carotid plaques are visualized as high-signal plaques (HSPs) on 3D turbo spin-echo T1-weighted black-blood vessel wall imaging (3D TSE T1-BB VWI). The purpose of this study was to compare manual segmentation and semiautomated segmentation for the quantification of carotid HSPs using 3D TSE T1-BB VWI. Twenty cervical carotid plaque lesions in 19 patients with a plaque contrast ratio of > 1.3 compared to adjacent muscle were studied. Using the mean voxel value for the adjacent muscle multiplied by 1.3 as a threshold value, the semiautomated software exclusively segmented and measured the HSP volume. Manual and semiautomated HSP volumes were well correlated (r = 0.965). Regarding reproducibility, the inter-rater ICC was 0.959 (bias 24.63, 95% limit of agreement -96.07, 146.35) for the manual method and 0.998 (bias 15.2, 95% limit of agreement -17.83, 48.23) for the semiautomated method, indicating improved reproducibility by the semiautomated method compared to the manual method. The time required for semiautomated segmentation was significantly shorter than that of manual segmentation times (81.7 ± 7.8 s versus 189.5 ± 49.6 s; p < 0.01). The results obtained in this study demonstrate that the semiautomated segmentation method allows for reliable assessment of the HSP volume in patients with carotid plaque lesions, with reduced time and effort for the analysis.The use of the faecal immunochemical test (FIT) to stratify the risk of colorectal cancer (CRC) in symptomatic patients in primary healthcare enables improved referrals to colonoscopy. However, its effect on diagnostic delays or the prognosis of patients has been poorly evaluated in this setting. We performed a retrospective cohort study that included symptomatic patients with outpatient CRC diagnosis between 2009 and 2017. We identified whether FIT had been analysed between initial healthcare contact and diagnostic confirmation. We included 589 patients (male = 65%, 71.7 ± 11.6 years, TNM IV = 17.1%) in the analysis. FIT was performed in 411 (69.8%) patients with a positive result (≥10 µg/g of faeces) in 96.4% of the evaluated patients. The use of FIT was associated with increased diagnostic delay (yes = 159 ± 277 days, no = 111 ± 172 days; p = 0.01). At five years follow up, 193 (32.8%) patients died (151 due to CRC). Mean survival was not modified by the use of FIT or its result (not performed = 46.8 ± 1.5 months, FIT+ = 48.9 ± 1 months, FIT- = 45.6 ± 5.5 months; p = 0.5) in Kaplan-Meier analysis, and was confirmed later in multivariate Cox regression analysis. In conclusion, FIT determination in symptomatic patients in primary healthcare did not modify CRC prognosis.Splenic artery pseudoaneurysm (PSA) is a contained vascular wall lesion associated with a high mortality rate, generally related to pancreatitis, trauma, malignancy, iatrogenic injury, and segmental arterial mediolysis. Fluorofurimazine datasheet Computed tomography angiography allows us to visualize the vascular anatomy, differentiate a PSA from an aneurysm, and provide adequate information for endovascular/surgical treatment. The present review reports on the main state-of-the-art splenic artery PSA diagnosis, differentiating between the pros and cons of the imaging methods and about the endovascular treatment.Background Despite great advances in medicine, numerous available laboratory markers, and radiological imaging, the diagnosis of acute appendicitis (AA) in some cases still remains controversial and challenging for clinicians. Because of that, clinicians are still looking for an ideal marker that would be specific to AA. The red blood cell distribution width (RDW) has been recently investigated in several studies as a potential biomarker for AA. The aim of this systematic review and meta-analysis was to systematically summarize and compare all relevant data on RDW as a diagnostic biomarker for AA. Methods This systematic review and meta-analysis were performed as per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Scientific databases (PubMed, Scopus, Web of Science, and Excerpta Medica database-EMBASE) were systematically searched for relevant comparative studies by two independent researches using keywords ((red cell distribution width) OR rdw) AND (appendicitis).d no significant difference among the AA and non-AA groups in terms of the RDW values (WMD = 0.99, 95% CI = (-0.35, 2.33), p = 0.15). Conclusion The RDW value difference demonstrated no statistically significant difference in AA versus healthy individuals and AA versus non-AA individuals. At the moment, there is no evidence of RDW utility in diagnostic testing of AA. Further research with prospective, multicenter studies and studies targeting special patient groups with a large sample size are needed in this field.The aim of this study was to evaluate the interradicular septum bone morphometric characteristics using cone beam computed tomography (CBCT) images, as well as to establish quantitative shortcuts to allow clinicians to make a faster and more reliable plan for immediate implant placement in the maxillary molars area. This retrospective quantitative study was conducted on CBCT images obtained from 100 patients. The morphometric analysis of the maxillary molars region was based on the parameters obtained on the sagittal and axial slices. The analysis performed on sagittal slices showed that the first maxillary molars had a wider interradicular septum when compared to the second molars, but the septum height in the first molars was significantly below the height in the second maxillary molars. The axial CBCT slices analysis showed that both interradicular septum perimeter and surface area were significantly more pronounced in the first than in the second maxillary molars. The interradicular furcation angle significantly correlated with the surface area (positively) and septum height (negatively) for both molars. The results of this study may recommend CBCT image analysis as a useful tool in predefining the circumstances that can allow for substantially better planning of immediate implant placement procedures in the region of maxillary molars.(1) Background Fetal growth restriction is a relatively common disorder in pregnant patients with thrombophilia. New artificial intelligence algorithms are a promising option for the prediction of adverse obstetrical outcomes. The aim of this study was to evaluate the predictive performance of a Feed-Forward Back Propagation Network (FFBPN) for the prediction of small for gestational age (SGA) newborns in a cohort of pregnant patients with thrombophilia. (2) Methods This observational retrospective study included all pregnancies in women with thrombophilia who attended two tertiary maternity hospitals in Romania between January 2013 and December 2020. Bivariate associations of SGA and each predictor variable were evaluated. Clinical and paraclinical predictors were further included in a FFBPN, and its predictive performance was assessed. (3) Results The model had an area under the curve (AUC) of 0.95, with a true positive rate of 86.7%, and a false discovery rate of 10.5%. The overall accuracy of our model was 90%.
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