An Automated Prognostic Model for Pancreatic Ductal Adenocarcinoma.

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.

Revealing the Boundaries of Selected Gastro-Intestinal (GI) Organs by Implementing CNNs in Endoscopic Capsule Images.

PURPOSE: The detection of where an organ starts and where it ends is achievable and, since this information can be delivered in real time, it could be quite important for several reasons. For one, by having the practical knowledge of the Wireless Endoscopic Capsule (WEC) transition through an organ's domain, we are able to align and control the endoscopic operation with any other possible protocol, i.e., delivering some form of treatment on the spot. Another is having greater anatomical topography information per session, therefore treating the individual in detail (not "in general"). Even the fact that by gathering more accurate information for a patient by merely implementing clever software procedures is a task worth exploiting, since the problems we have to overcome in real-time processing of the capsule findings (i.e., wireless transfer of images to another unit that will apply the necessary real time computations) are still challenging. This study proposes a computer-aided detection (CAD) tool, a CNN algorithm deployed to run on field programmable gate array (FPGA), able to automatically track the capsule transitions through the entrance (gate) of esophagus, stomach, small intestine and colon, in real time. The input data are the wireless transmitted image shots of the capsule's camera (while the endoscopy capsule is operating). METHODS: We developed and evaluated three distinct multiclass classification CNNs, trained on the same dataset of total 5520 images extracted by 99 capsule videos (total 1380 frames from each organ of interest). The proposed CNNs differ in size and number of convolution filters. The confusion matrix is obtained by training each classifier and evaluating the trained model on an independent test dataset comprising 496 images extracted by 39 capsule videos, 124 from each GI organ. The test dataset was further evaluated by one endoscopist, and his findings were compared with CNN-based results. The statistically significant of predictions between the four classes of each model and the comparison between the three distinct models is evaluated by calculating the p-values and chi-square test for multi class. The comparison between the three models is carried out by calculating the macro average F1 score and Mattheus correlation coefficient (MCC). The quality of the best CNN model is estimated by calculations of sensitivity and specificity. RESULTS: Our experimental results of independent validation demonstrate that the best of our developed models addressed this topological problem by exhibiting an overall sensitivity (96.55%) and specificity of (94.73%) in the esophagus, (81.08% sensitivity and 96.55% specificity) in the stomach, (89.65% sensitivity and 97.89% specificity) in the small intestine and (100% sensitivity and 98.94% specificity) in the colon. The average macro accuracy is 95.56%, the average macro sensitivity is 91.82%.

Tumor Diagnosis against Other Brain Diseases Using T2 MRI Brain Images and CNN Binary Classifier and DWT.

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.

A Novel Real-time Automatic Angioectasia Detection Method in Wireless Capsule Endoscopy Video Feed.

The development of Wireless Capsule Endoscopy (WCE) revolutionized the examination of the small bowel for diseases. Upon swallowing a capsule (a microscopic camera that resembles an ordinary pill in both shape and size), images of the patient's gastrointestinal (GI) tract are wirelessly transmitted from it to an external recorder. The inspection of these images is, to this day, still manually performed by medical professionals - a lengthy, and especially prone to errors, process. One of the most common diagnoses is the presence of angioectasias, i.e. ectatic vessels on the GI tract that are predisposed to bleeding. In this paper, a novel method for automatic detection of these lesions is proposed, using a combination of low-level image processing, feature detection and machine learning, that can run in real-time without the need for specialized hardware or graphics cards, achieving 92.7% sensitivity and 99.5% specificity to angioectasias. This method can also be expanded to include more pathologies.

Effects of Lornoxicam on Anastomotic Healing: A Randomized, Blinded, Placebo-Control Experimental Study.

Introduction and Aim. With the implementation of multimodal analgesia regimens, Nonsteroidal Anti-Inflammatory Drugs (NSAIDs) are often administered for optimal pain control and reduction of opioid use. The aim of the study was to examine the effects of lornoxicam, a NSAID, on anastomotic healing employing an animal model. Materials and Methods. A total of 28 Wistar rats were randomly assigned in two groups. All animals underwent ascending colonic transection followed by an end-to-end hand sewn anastomosis. Group 1 received intraperitoneally lornoxicam before and daily after surgery. Group 2 received intraperitoneally an equal volume of placebo. Half of the animals in each group were euthanized on the 3rd pod and the remaining on the 7th pod. Macro- and microscopic indicators of anastomotic healing were compared using a two-tailed Fisher exact test. Results. The lornoxicam group significantly decreased fibroblast in growth and reepithelization of the mucosa at the anastomotic site on the 3rd pod and significantly increased occurrence of deep reaching defects, necrosis, and microabscess on the 7th pod. Conclusion. Lornoxicam administration during the perioperative period adversely affects histologic parameters of intestinal anastomotic healing. These effects of lornoxicam administration were not found to induce significant increase of anastomotic dehiscence in the rat model.

Clinical Spectrum and Management of Caustic Ingestion: A Case Series Presenting Three Opposing Outcomes.

BACKGROUND: Ingestion of caustic substances is a medical emergency in both the adult and pediatric population and is associated with high morbidity and mortality. The extent of injuries after ingestion of caustic substances depends on the nature, amount, and concentration of the agent and on the exposure time. Acutely, caustic substances may cause massive hemorrhage and gastrointestinal tract perforation; the most markedly affected cases require urgent surgical treatment. Patients surviving the initial event may present with aorto-enteric or gastrocolic fistulae, esophageal strictures, dysphagia, and increased risk of esophageal cancer as long term sequelae. CASE REPORT: The features of three cases of caustic ingestion are reported to demonstrate significantly different complaints presented at the emergency department. Two patients had free gastric perforation, one at presentation, and one delayed. The third patient presented with late severe strictures of the esophagus and pylorus. The outcomes of the three patients are discussed in detail along with the most current management strategies. CONCLUSIONS: Among adults, ingestion of caustic substances is usually associated with more severe lesions due to the increased amount of ingested substance, as compared with pediatric patients. The most serious presentation is that of visceral perforation, most commonly of the stomach and rarely of the esophagus. Management involves urgent resuscitation with correction of fluid and electrolyte and acid-base abnormalities and immediate surgical exploration in those patients with signs of perforation. Once the perioperative period is managed successfully, the long-term results can be satisfactory. Managing of strictures or else reconstructive procedures must be well timed to allow for psychological and nutritional rehabilitation.

Eosinophilic Cholangitis--A Challenging Diagnosis of Benign Biliary Stricture: A Case Report.

When confronting a biliary stricture, both benign and malignant etiologies must be carefully considered as a variety of benign biliary strictures can masquerade as hilar cholangiocarcinoma (CCA). Therefore, patients could undergo a major surgery despite the possibility of a benign biliary disease. Approximately 15% to 24% of patients undergoing surgical resection for suspected biliary malignancy will have benign pathology. Eosinophilic cholangitis (EC) is a rare benign disorder of the biliary tract, which can cause obstructive jaundice and can pose a difficult diagnostic task. We present a rare case of a young woman who was referred to our hospital with obstructive painless jaundice due to a biliary stricture at the confluence of the hepatic bile ducts, with a provisional diagnosis of cholangiocarcinoma. Though, during her work up she was found to have EC, an extremely rare benign cause of biliary stricture, which is characterized by a dense eosinophilic infiltration of the biliary tree causing stricturing, fibrosis, and obstruction and which is reversible with short-term high-dose steroids. Despite its rarity, EC should be taken into consideration when imaging modalities demonstrate a biliary stricture, especially if preoperative diagnosis of malignancy cannot be made, in the setting of peripheral eosinophilia and the absence of cardinal symptoms of malignancy.

Diagnostic and Therapeutic Challenges of Intrahepatic Biliary Cystadenoma and Cystadenocarcinoma: A Report of 10 Cases and Review of the Literature.

The objective of this study was to present our experience with intrahepatic biliary cystadenomas and cystadenocarcinomas in 10 patients surgically managed in our department. Intrahepatic biliary cystadenomas and cystadenocarcinomas are rare cystic tumors that are often misdiagnosed preoperatively as simple cysts or hydatid cysts. They recur after incomplete resection and entail a risk of malignant transformation to cystadenocarcinoma. A retrospective review was conducted of patients with histologically confirmed intrahepatic biliary cystadenomas and cystadenocarcinomas between August 2004 and February 2013 who were surgically managed in our department. A total of 10 patients, 9 female and 1 male (mean age, 50 years), with cystic liver were reviewed. The size of the cysts ranged between 3.5 and 16 cm (mean, 10.6). Five patients had undergone previous interventions elsewhere and presented with recurrences. Liver resections included 6 hepatectomies, 2 bisegmentectomies, 1 extended right hepatectomy, and 1 enucleation due to the central position and the large size of the lesion. Pathology reports confirmed R0 resections in all cases. All patients were alive after a median follow-up of 6 years (range, 1-10 years), and no recurrence was detected. Intrahepatic biliary cystadenoma and cystadenocarcinoma should be considered in differential diagnosis in patients with liver cystic tumors. Because of the high recurrence rate and difficult accurate preoperative diagnosis, formal liver resection is mandatory. Enucleation with free margins is an option and is indicated where resection is impossible.

Cutaneous Metastatic Adenocarcinoma of the Colon to the Scalp.

Cutaneous metastases from colorectal cancer are relatively uncommon presenting in fewer than 5% of patients but they are very important to recognize as they signify disseminated disease and poor prognosis. We describe a case a 62-year-old patient diagnosed with scalp metastasis during his systemic chemotherapy treatment for a colorectal carcinoma stage IVb who underwent excisional biopsy of the metastatic lesion. The identification of cutaneous metastases from colorectal cancer can radically alter therapeutic plans as they typically indicate a wide spread disease. Although they can be observed at any stage of malignancy, early recognition can lead to accurate and prompt diagnosis and timely treatment.