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Background: Colloid cysts are rare benign, slowly growing intracranial tumors of endodermal origin. Most colloid cysts are found incidentally and are asymptomatic, but rarely, they can lead to sudden death., Case Presentation: A 73-year-old female patient was admitted to our emergency department with complaints of dizziness, nausea, vomiting, fatigue, walking difficulties, and behavioral changes. CT imaging revealed acute obstructive hydrocephalus attributable to a third ventricular colloid cyst. The patient was immediately transferred to a tertiary center where she underwent successful neurosurgical resection of the mass. Pathology results of the lesion confirmed the diagnosis of colloid cyst., Conclusion: The case we present emphasizes the critical importance of prompt identification of warning signs, complex thinking, and evaluation. Establishing the right diagnostic approach early on can facilitate accurate diagnosis., (© 2023. The Author(s).)

Background: The nature of input data is an essential factor when training neural networks. Research concerning magnetic resonance imaging (MRI)-based diagnosis of liver tumors using deep learning has been rapidly advancing. Still, evidence to support the utilization of multi-dimensional and multi-parametric image data is lacking. Due to higher information content, three-dimensional input should presumably result in higher classification precision. Also, the differentiation between focal liver lesions (FLLs) can only be plausible with simultaneous analysis of multi-sequence MRI images., Aim: To compare diagnostic efficiency of two-dimensional (2D) and three-dimensional (3D)-densely connected convolutional neural networks (DenseNet) for FLLs on multi-sequence MRI., Methods: We retrospectively collected T2-weighted, gadoxetate disodium-enhanced arterial phase, portal venous phase, and hepatobiliary phase MRI scans from patients with focal nodular hyperplasia (FNH), hepatocellular carcinomas (HCC) or liver metastases (MET). Our search identified 71 FNH, 69 HCC and 76 MET. After volume registration, the same three most representative axial slices from all sequences were combined into four-channel images to train the 2D-DenseNet264 network. Identical bounding boxes were selected on all scans and stacked into 4D volumes to train the 3D-DenseNet264 model. The test set consisted of 10-10-10 tumors. The performance of the models was compared using area under the receiver operating characteristic curve (AUROC), specificity, sensitivity, positive predictive values (PPV), negative predictive values (NPV), and f1 scores., Results: The average AUC value of the 2D model (0.98) was slightly higher than that of the 3D model (0.94). Mean PPV, sensitivity, NPV, specificity and f1 scores (0.94, 0.93, 0.97, 0.97, and 0.93) of the 2D model were also superior to metrics of the 3D model (0.84, 0.83, 0.92, 0.92, and 0.83). The classification metrics of FNH were 0.91, 1.00, 1.00, 0.95, and 0.95 using the 2D and 0.90, 0.90, 0.95, 0.95, and 0.90 using the 3D models. The 2D and 3D networks' performance in the diagnosis of HCC were 1.00, 0.80, 0.91, 1.00, and 0.89 and 0.88, 0.70, 0.86, 0.95, and 0.78, respectively; while the evaluation of MET lesions resulted in 0.91, 1.00, 1.00, 0.95, and 0.95 and 0.75, 0.90, 0.94, 0.85, and 0.82 using the 2D and 3D networks, respectively., Conclusion: Both 2D and 3D-DenseNets can differentiate FNH, HCC and MET with good accuracy when trained on hepatocyte-specific contrast-enhanced multi-sequence MRI volumes., Competing Interests: Conflict-of-interest statement: The authors have no financial relationships to disclose., (©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.)

Background: CT texture analysis (CTTA) has been successfully used to assess tissue heterogeneity in multiple diseases. The purpose of this work is to demonstrate the value of three-dimensional CTTA in the evaluation of diffuse liver disease. We aimed to develop CTTA based prediction models, which can be used for staging of fibrosis in different anatomic liver segments irrespective of variations in scanning parameters., Methods: We retrospectively collected CT scans of thirty-two chronic hepatitis patients with liver fibrosis. The CT examinations were performed on either a 16- or a 64-slice scanner. Altogether 354 anatomic liver segments were manually highlighted on portal venous phase images, and 1117 three-dimensional texture parameters were calculated from each segment. The segments were divided between groups of low-grade and high-grade fibrosis using shear-wave elastography. The highly-correlated features (Pearson r > 0.95) were filtered out, and the remaining 453 features were normalized and used in a classification with k-means and hierarchical cluster analysis. The segments were split between the train and test sets in equal proportion (analysis I) or based on the scanner type (analysis II) into 64-slice train 16-slice validation cohorts for machine learning classification, and a subset of highly prognostic features was selected with recursive feature elimination., Results: A classification with k-means and hierarchical cluster analysis divided segments into four main clusters. The average CT density was significantly higher in cluster-4 (110 HU ± SD = 10.1HU) compared to the other clusters (c1: 96.1 HU ± SD = 11.3HU; p < 0.0001; c2: 90.8 HU ± SD = 16.8HU; p < 0.0001; c3: 93.1 HU ± SD = 17.5HU; p < 0.0001); but there was no difference in liver stiffness or scanner type among the clusters. The optimized random forest classifier was able to distinguish between low-grade and high-grade fibrosis with excellent cross-validated accuracy in both the first and second analysis (AUC = 0.90, CI = 0.85-0.95 vs. AUC = 0.88, CI = 0.84-0.91). The final support vector machine model achieved an excellent prediction rate in the second analysis (AUC = 0.91, CI = 0.88-0.94) and an acceptable prediction rate in the first analysis (AUC = 0.76, CI = 0.67-0.84)., Conclusions: In conclusion, CTTA-based models can be successfully applied to differentiate high-grade from low-grade fibrosis irrespective of the imaging platform. Thus, CTTA may be useful in the non-invasive prognostication of patients with chronic liver disease.

Purpose: Our study aimed to evaluate the technical success rate, interobserver reproducibility, and accuracy of shearwave elastography (SWE) in the staging of hepatitis C virus (HCV)-associated liver fibrosis., Methods: A total of 10 healthy controls and 49 patients with chronic liver disease were enrolled prospectively. Two examiners performed point shearwave elastography (pSWE) and two-dimensional shearwave elastography (2D-SWE) measurements with an RS85A ultrasound scanner using the S-Shearwave application (Samsung Medison, Hongcheon, Korea). The performance of S-Shearwave in the staging (METAVIR F0-F4) of liver fibrosis was compared with prior transient elastography (TE) with receiver operating characteristic (ROC) curve analysis., Results: The interobserver reproducibility was excellent with pSWE (ICC = 0.92, 95% CI: 0.86-0.95, P < .001). A very good agreement was found between pSWE and TE measurements (ICC =0.85, 95% CI: 0.78-0.89, P < .001). The ROC analysis determined the optimal cut-off values of pSWE for the staging of chronic hepatitis C-associated fibrosis (F2, 1.46 m/s; F3, 1.63 m/s; F4, 1.95 m/s). Both observers achieved excellent diagnostic accuracy (AUROC: 94% vs 97%) in the detection of significant (≥F2) liver fibrosis., Conclusion: The interobserver agreement is excellent with S-Shearwave pSWE, and observers can diagnose significant liver fibrosis with a comparable accuracy to TE., (© 2019 The Authors. Journal of Clinical Ultrasound published by Wiley Periodicals, Inc.)

It has been demonstrated recently in several solid tumors that thrombocytosis at diagnosis may correlate with tumor invasion, metastatic progression and worse outcome. Several details of the pathomechanism of the relationship of thrombocytosis and cancer have been elucidated; however, the complete process is not clearly understood. Several hypotheses have been proposed. Recently, it was suggested that in ovarian cancer elevated IL-6 production by the tumor may induce increased megakaryopoiesis via hepatic thrombopoietin production leading to thrombocytosis. The importance of the prognostic power of elevated platelet count is still debated in gastrointestinal cancer. The aims of this review were to evaluate the prognostic significance of thrombocytosis in gastrointestinal tumors, to see whether clinical practice confirmed the hypotheses and to reveal the causes of the inconsistent findings.

Blocking poly(ADP-ribosyl)ation of nuclear proteins protects the heart from ischemia-reperfusion injury. In addition, activation of Akt and mitogen-activated protein kinase (MAPK) cascades also plays a pivotal role in the survival of cardiomyocytes during ischemia-reperfusion; however, the potential interplay between these pathways is yet to be elucidated. We therefore tested the hypothesis whether poly(ADP-ribose) polymerase (PARP) inhibition can modulate Akt and MAPK signaling of ischemic-reperfused rat hearts. A novel PARP inhibitor, L-2286 [2-[(2-piperidin-1-yletil)thio]quinazolin-4(3H)-one] was administered during ischemia-reperfusion in Langendorff perfused rat hearts and in isoproterenol-induced myocardial infarction. Thereafter, the cardiac energy metabolism, oxidative damage, and the phosphorylation state of Akt and MAPK cascades were monitored. L-2286 exerted significant protective effect against ischemia-reperfusion-induced myocardial injury in both experimental models. More importantly, L-2286 facilitated the ischemia-reperfusion-induced activation of Akt, extracellular signal-regulated kinase, and p38-MAPK in both isolated hearts and in vivo cardiac injury. By contrast, isoproterenol-induced rapid c-Jun N-termainal kinase activation was repressed by L-2286. Here, we provide evidence for the first time that PARP inhibition beneficially modulates the cardiac Akt and MAPK signaling in ex vivo and in vivo ischemia-reperfusion models. We therefore propose that this novel mechanism may contribute to the cardioprotective properties of PARP inhibitors.