A significant difference was found between the two types of needles in terms of reduced visualization of the 25G needle and suboptimal performance rating. However, this did not impact on overall results since both needles were equally successful in terms of a high diagnostic yield and overall accuracy.

Objective: Our main aim was to investigate the serum lipid levels in a series of patients with liver cirrhosis of viral origin. Subjects and Methods: The study comprised 90 patients, 60 with viral liver cirrhosis, equally divided between hepatitis virus C (HCV) and B (HBV) etiologies, and 30 control patients with no known liver pathology. Patients were investigated during a 5-year period in the 1st Medical Clinic of the Emergency County Hospital of Craiova, Romania. The following series of serum lipid parameters were recorded: lipemia, total cholesterol and cholesteryl ester, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, very-low-density lipoprotein (VLDL) cholesterol and triglyceride (TG) values. Statistical analysis of these parameters was performed using the ANOVA test followed by Tukey multiple comparison teststo compare replicate means; p < 0.05 was considered statistically significant. Results: We observed significantly lower values for serum lipids (543.5 and 549.37 mg/dl in the HBV and HCV cirrhosis subgroups, compared with 649.9 mg/dl in controls), total cholesterol (143.6 and 147.9 vs. 198.0 mg/dl, respectively), cholesteryl esters (83.6 and 80, compared to 147.9 mg/dl, respectively), LDL cholesterol (91.6 and 88.5 vs. 132.4 mg/dl) in both cirrhosis groups when compared with controls (p < 0.001), as well as HDL cholesterol (32.1 and 36.9 vs. 47.3 mg/dl, p < 0.05). However, TG and VLDL cholesterol values of controls and cirrhosis groups were similar (p > 0.05). We did not register any differences between the two cirrhosis groups (p > 0.05). Conclusion: Our data showed that both HCV and HBV cirrhosis severely impaired liver lipid metabolism. Late stages of the disease resulted in a pseudonormalization of VLDL cholesterol and TG values.

Despite significant advances in imaging techniques, the incidence of colorectal cancer has been increasing in recent years, with many cases still being diagnosed in advanced stages. Early detection and accurate staging remain the main factors that lead to a decrease in the cost and invasiveness of the curative techniques, significantly improving the outcome. However, the diagnosis of pedunculated early colorectal malignancy remains a current challenge. Data on the management of pedunculated cancer precursors, apart from data on nonpolypoid lesions, are still limited. An adequate technique for complete resection, which provides the best long-term outcome, is mandatory for curative intent. In this context, a discussion regarding the diagnosis of malignancy of pedunculated polyps, separate from non-pedunculated variants, is necessary. The purpose of this review is to provide a critical review of the most recent literature reporting the different features of malignant pedunculated colorectal polyps, including diagnosis and management strategies.

Inflammatory bowel disease I"D with its two entities, ulcerative colitis and Crohn's disease, is at increased risk of developing colorectal cancer CRC . Risk factors for CRC are represented by the duration of the disease, extent of disease, the association of primary sclerosing cholangitis, family history, and early age at onset. In inflammatory bowel disease, colonic carcinogenesis appears on an inflamed colon, being determined by different genetic alterations. The main element of the process of carcinogenesis is the dysplasia, which is a neoplastic intraepithelial transformation, limited to the basal membrane surrounding the glands around which it appears. The stages of carcinogenesis process start with dysplasia of varying degrees as follows indefinite dysplasia, low-grade dysplasia, high-grade dysplasia, and finally invasive adenocarcinoma.

We performed a meta-analysis of published data to investigate the diagnostic value of artificial intelligence for pancreatic cancer. Systematic research was conducted in the following databases: PubMed, Embase, and Web of Science to identify relevant studies up to October 2021. We extracted or calculated the number of true positives, false positives true negatives, and false negatives from the selected publications. In total, 10 studies, featuring 1871 patients, met our inclusion criteria. The risk of bias in the included studies was assessed using the QUADAS-2 tool. R and RevMan 5.4.1 software were used for calculations and statistical analysis. The studies included in the meta-analysis did not show an overall heterogeneity (I2 = 0%), and no significant differences were found from the subgroup analysis. The pooled diagnostic sensitivity and specificity were 0.92 (95% CI, 0.89–0.95) and 0.9 (95% CI, 0.83–0.94), respectively. The area under the summary receiver operating characteristics curve was 0.95, and the diagnostic odds ratio was 128.9 (95% CI, 71.2–233.8), indicating very good diagnostic accuracy for the detection of pancreatic cancer. Based on these promising preliminary results and further testing on a larger dataset, artificial intelligence-assisted endoscopic ultrasound could become an important tool for the computer-aided diagnosis of pancreatic cancer.

Introduction : While the visual inspection of histopathology images by expert pathologists remains the golden standard method for grading of prostate cancer the quest for developing automated algorithms for the job is set and deep-learning techniques have emerged on top of other approaches. Methods : Two pre-trained deep-learning networks, obtained with transfer learning from two general purpose classification networks – AlexNet and GoogleNet, originally trained on a proprietary dataset of prostate cancer were used to classify 6000 cropped images from Gleason2019 Challenge. Results : The average agreement between the two networks and the six pathologists was found to be substantial for AlexNet and moderate for GoogleNet. When tested against the majority vote of the six pathologists the agreement was perfect and moderate for AlexNet, and GoogleNet, respectively. Despite our expectations, the average inter-pathologist agreement was moderate, while between the two networks it was substantial. Resulted accuracy for AlexNet and GoogleNet when tested against the majority vote as ground truth was of 85.51% and 74.75%, respectively. This result was higher than the score obtained on the dataset that they were trained on, showing their generalization capabilities. Conclusions : Both the agreement and the accuracy indicate a better performance of AlexNet over GoogleNet, making it suitable for clinical deployment thus could potentially contribute to faster, more accurate and with higher reproducibility prostate cancer diagnosis.

Two deep-learning algorithms designed to classify images according to the Gleason grading system that used transfer learning from two well-known general-purpose image classification networks (AlexNet and GoogleNet) were trained on Hematoxylin-Eosin histopathology stained microscopy images with prostate cancer. The dataset consisted of 439 images asymmetrically distributed in four Gleason grading groups. Mean and standard deviation accuracy for AlexNet derivate network was of 61.17±7 and for GoogleNet derivate network was of 60.9±7.4. The similar results obtained by the two networks with very different architecture, together with the normal distribution of classification error for both algorithms show that we have reached a maximum classification rate on this dataset. Taking into consideration all the constraints, we conclude that the resulted networks could assist pathologists in this field, providing first or second opinions on Gleason grading, thus presenting an objective opinion in a grading system which has showed in time a great deal of interobserver variability.

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