Purpose: Brain tumors are diagnosed and classified manually and noninvasively by radiologists using Magnetic Resonance Imaging (MRI) data. The risk of misdiagnosis may exist due to human factors such as lack of time, fatigue, and relatively low experience. Deep learning methods have become increasingly important in MRI classification. To improve diagnostic accuracy, researchers emphasize the need to develop Computer-Aided Diagnosis (CAD) computational diagnostics based on artificial intelligence (AI) systems by using deep learning methods such as convolutional neural networks (CNN) and improving the performance of CNN by combining it with other data analysis tools such as wavelet transform. In this study, a novel diagnostic framework based on CNN and DWT data analysis is developed for the diagnosis of glioma tumors in the brain, among other tumors and other diseases, with T2-SWI MRI scans. It is a binary CNN classifier that treats the disease “glioma tumor” as positive and the other pathologies as negative, resulting in a very unbalanced binary problem. The study includes a comparative analysis of a CNN trained with wavelet transform data of MRIs instead of their pixel intensity values in order to demonstrate the increased performance of the CNN and DWT analysis in diagnosing brain gliomas. The results of the proposed CNN architecture are also compared with a deep CNN pre-trained on VGG16 transfer learning network and with the SVM machine learning method using DWT knowledge. Methods: To improve the accuracy of the CNN classifier, the proposed CNN model uses as knowledge the spatial and temporal features extracted by converting the original MRI images to the frequency domain by performing Discrete Wavelet Transformation (DWT), instead of the traditionally used original scans in the form of pixel intensities. Moreover, no pre-processing was applied to the original images. The images used are MRIs of type T2-SWI sequences parallel to the axial plane. Firstly, a compression step is applied for each MRI scan applying DWT up to three levels of decomposition. These data are used to train a 2D CNN in order to classify the scans as showing glioma or not. The proposed CNN model is trained on MRI slices originated from 382 various male and female adult patients, showing healthy and pathological images from a selection of diseases (showing glioma, meningioma, pituitary, necrosis, edema, non-enchasing tumor, hemorrhagic foci, edema, ischemic changes, cystic areas, etc.). The images are provided by the database of the Medical Image Computing and Computer-Assisted Intervention (MICCAI) and the Ischemic Stroke Lesion Segmentation (ISLES) challenges on Brain Tumor Segmentation (BraTS) challenges 2016 and 2017, as well as by the numerous records kept in the public general hospital of Chania, Crete, “Saint George”. Results: The proposed frameworks are experimentally evaluated by examining MRI slices originating from 190 different patients (not included in the training set), of which 56% are showing gliomas by the longest two axes less than 2 cm and 44% are showing other pathological effects or healthy cases. Results show convincing performance when using as information the spatial and temporal features extracted by the original scans. With the proposed CNN model and with data in DWT format, we achieved the following statistic percentages: accuracy 0.97, sensitivity (recall) 1, specificity 0.93, precision 0.95, FNR 0, and FPR 0.07. These numbers are higher for this data format (respectively: accuracy by 6% higher, recall by 11%, specificity by 7%, precision by 5%, FNR by 0.1%, and FPR is the same) than it would be, had we used as input data the intensity values of the MRIs (instead of the DWT analysis of the MRIs). Additionally, our study showed that when our CNN takes into account the TL of the existing network VGG, the performance values are lower, as follows: accuracy 0.87, sensitivity (recall) 0.91, specificity 0.84, precision 0.86, FNR of 0.08, and FPR 0.14. Conclusions: The experimental results show the outperformance of the CNN, which is not based on transfer learning, but is using as information the MRI brain scans decomposed into DWT information instead of the pixel intensity of the original scans. The results are promising for the proposed CNN based on DWT knowledge to serve for binary diagnosis of glioma tumors among other tumors and diseases. Moreover, the SVM learning model using DWT data analysis performs with higher accuracy and sensitivity than using pixel values.

Background: Esophagopericardial fistula is a rare but life-threatening complication of benign, malignant or traumatic esophageal disease. It is most commonly associated with benign etiology and carries a high mortality rate which increases with delay in diagnosis. Case presentation: We present a case of an esophagopericardial fistula as a rare complication in a 53-year-old male patient, 7 months after total gastrectomy for an adenocarcinoma of the esophagogastric junction. Conclusion: The prognosis of esophagopericardial fistula is poor, especially when it is associated with malignancy.

Lithiasis is a known side effect of ceftriaxone administration in children. Sex, age, weight, dosage, and duration of intake have been reported as risk factors for the formation of calcification or stones in the bile and urine excretory systems of children who received ceftriaxone. The purpose of this systematic review is to investigate the reported effects of ceftriaxone administration in pediatric patients who were admitted to a hospital due to infection, the likelihood of gallstones, nephroliths, or precipitations in both the biliary and urinary systems, as well as investigate the relationship with their mother’s history during pregnancy. Original studies and literature reviews from the PubMed database were included in the study. No time limit related to research or publication was set for the articles. The results were evaluated, aiming to understand the outcomes and identify any predisposing factors relevant to this side effect. Of the 181 found articles, 33 were appropriate for inclusion in the systematic review. The administered dose of ceftriaxone presented variability. Symptoms, such as abdominal pain and vomiting, were associated with ceftriaxone-related lithiasis in many cases. It was noted that most of the results were the outcomes of retrospective observation and not of prospective randomized research. Definitively, more randomized control studies with long-term outcomes are needed to identify the exact association between ceftriaxone and lithiasis in children.

Division of major vascular and biliary structures during major hepatectomies can be carried out either extrahepatically at the porta hepatic or intrahepatically during the parenchymal transection. In this retrospective study we test the hypothesis that the intrahepatic technique is associated with less early biliary complications.150 patients who underwent major hepatectomies were retrospectively allocated into an intrahepatic group (n = 100) and an extrahepatic group (n = 50) based on the technique of hilar division. The two groups were operated by two different surgical teams, each one favoring one of the two approaches for hilar dissection. Operative data (warm ischemic time, operative time, blood loss), biliary complications, morbidity and mortality rates were analyzed.In extrahepatic patients, operative time was longer (245 +/- 50 vs 214 +/- 38 min, p < 0.05) while the overall complication rate (55% vs 52%), hospital stay (13 +/- 7 vs 12 +/- 4 days), bile leak rate (22% vs 20%) and mortality (2% vs 2%) were similar compared to intrahepatic patients. However, most (57%) bile leaks in extrahepatic patients were grade II (leaks that required non-operative interventional treatment, while most (70%) leaks in the intrahepatic group were grade I (leaks that resolved and presented two injuries (4%) of the remaining bile ducts (p < 0.05).Intrahepatic hilar division is as safe as extrahepatic hilar division in terms of intraoperative blood requirements, morbidity and mortality. The extrahepatic technique is associated with more severe bile leaks and biliary injuries.

Pancreatic ductal adenocarcinoma (PDAC) constitutes a leading cause of cancer-related mortality despite advances in detection and treatment methods. While computed tomography (CT) serves as the current gold standard for initial evaluation of PDAC, its prognostic value remains limited, as it relies on diagnostic stage parameters encompassing tumor size, lymph node involvement, and metastasis. Radiomics have recently shown promise in predicting postoperative survival of PDAC patients; however, they rely on manual pancreas and tumor delineation by clinicians. In this study, we collected a dataset of pre-operative CT scans from a cohort of 40 PDAC patients to evaluate a fully automated pipeline for survival prediction. Employing nnU-Net trained on an external dataset, we generated automated pancreas and tumor segmentations. Subsequently, we extracted 854 radiomic features from each segmentation, which we narrowed down to 29 via feature selection. We then combined these features with the Tumor, Node, Metastasis (TNM) system staging parameters, as well as the patient’s age. We trained a random survival forest model to perform an overall survival prediction over time, as well as a random forest classifier for the binary classification of two-year survival, using repeated cross-validation for evaluation. Our results exhibited promise, with a mean C-index of 0.731 for survival modeling and a mean accuracy of 0.76 in two-year survival prediction, providing evidence of the feasibility and potential efficacy of a fully automated pipeline for PDAC prognostication. By eliminating the labor-intensive manual segmentation process, our streamlined pipeline demonstrates an efficient and accurate prognostication process, laying the foundation for future research endeavors.

Pancreatic injury is uncommon and the management remains controversial. The integrity of the main pancreatic duct is considered the most important determinant for prognosis.