Background and aims
To evaluate two‐dimensional shear wave elastography (2DSWE) in parallel with transient elastography (TE) for diagnosing clinically significant portal hypertension (CSPH) and ...high‐risk varices (HRV) in patients with chronic liver disease.
Patients and methods
Consecutive patients with suspicion of compensated advanced chronic liver disease (cACLD) liver stiffness measurement (LSM) ≥ 10 kPa by TE, or morphological signs suggestive of cACLD on imaging, with no history of liver decompensation, underwent hepatic venous pressure gradient (HVPG) measurement, transjugular liver biopsy and esophagogastroduodenoscopy, which served as the reference methods for diagnosing CSPH, cACLD and HRV. All patients underwent LSM and spleen stiffness measurements (SSM) by 2DSWE and TE.
Results
Seventy‐six (76) patients were included (78% men, mean age 62 years, body mass index 28.3 kg/m2, 36.8% alcoholic, 30.3% non‐alcoholic fatty liver disease, 14.5% viral hepatitis). Of them, 80.3%, 69.7%, 52.6% and 22.4% had cACLD, cirrhosis, CSPH and HRV respectively. LSM performed better than SSM in diagnosing CSPH and HRV. For CSPH, AUROCs (0.926 vs. 0.866), optimal cut‐offs (20.1 vs. 20.2 kPa) and sensitivity/specificity (80.5%/94.3% vs. 77.5% /86.1%) were comparable for 2DSWE and TE. Ruling‐out of CSPH by 2DSWE (LSM at cut‐off with ≥90% sensitivity (13.5 kPa) and platelets ≥ 150 x 109/L) performed comparably to TE, with 1/24 cases falsely classified as negative. For HRV, AUROCs were similar (0.875 2DSWE, 0.851 TE) with similar optimal LSM cut‐offs enabling 100% sensitivity and ruling‐out HRV.
Conclusion
Liver stiffness measurement by 2DSWE appears to perform equally well as TE for diagnosing CSPH and ruling‐out HRV in compensated chronic liver disease.
Research over the past decade has indicated that melanocortin peptides are potent inhibitors of inflammation and a promising source of new anti-inflammatory and cytoprotective therapies. The purpose ...of the present paper is to compare protective effects of alpha-, beta-, and gamma-melanocyte stimulating hormone on acetaminophen induced liver lesions in male CBA mice. Acetaminophen was applied intragastrically in a dose of 150 mg/kg, and tested substances were applied intraperitoneally 1 hour before acetaminophen. Mice were sacrificed after 24 hours and intensity of liver injury was estimated by measurement of plasma transaminase activity (AST and ALT) and histopathological grading of lesions. It was found that alpha-, beta-, and gamma-MSH decrease intensity of lesions by both criteria in a dose-dependent manner.
To compare and evaluate the hepatoprotective effect of remote ischemic preconditioning (RIPC) with local ischemic preconditioning (LIPC) of the liver during human liver resections.
A prospective, ...single-centre, randomised control trial was conducted in the Clinical Hospital “***” from April 2017 to January 2018. A total of 60 patients, who underwent liver resection due to colorectal cancer liver metastasis, were randomised to one of three study arms: 1) a RIPC group, 2) an LIPC group and 3) a control group (CG) in which no ischemic preconditioning was done before liver resection. The hepatoprotective effect was evaluated by comparing serum transaminase levels, bilirubin levels, albumin, and protein levels, coagulograms and through pathohistological analysis. The trial was registered on ClinicalTrials.gov (NCT***).
Significant differences were found in serum levels of liver transaminases and bilirubin levels between thegroups, the highest level in the CG and the lowest level in the LIPC group. Levels of cholinesterase were also significantly higher in the LIPC group. Pathohistological findings graded by the Rodriguez score showed favourable changes in the LIPC and RIPC groups versus the CG.
Strong evidence supports the hepatoprotective effect of RIPC and LIPC preconditioning from an ischemia-reperfusion injury of the liver. Better synthetic liver function preservation in these two groups supports this conclusion.
•Ischemia-reperfusion injury.•Remote ischemic preconditioning.•Local ischemic preconditioning.•Liver resection.
Low-contrast images, such as color microscopic images of unstained histological specimens, are composed of objects with highly correlated spectral profiles. Such images are very hard to segment. ...Here, we present a method that nonlinearly maps low-contrast color image into an image with an increased number of non-physical channels and a decreased correlation between spectral profiles. The method is a proof-of-concept validated on the unsupervised segmentation of color images of unstained specimens, in which case the tissue components appear colorless when viewed under the light microscope. Specimens of human hepatocellular carcinoma, human liver with metastasis from colon and gastric cancer and mouse fatty liver were used for validation. The average correlation between the spectral profiles of the tissue components was greater than 0.9985, and the worst case correlation was greater than 0.9997. The proposed method can potentially be applied to the segmentation of low-contrast multichannel images with high spatial resolution that arise in other imaging modalities.
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•Publicly available dataset with 82 H&E stained images of frozen sections.•Images are acquired on 19 patients with metastatic colon cancer in a liver.•Pixel wise ground truths ...provided by seven domain experts.•Diagnostic results obtained with SVM, kNN, U-Net, U-Net++ and deeplabv3 classifiers.•Balanced accuracy and F1 score on independent test set amount to 89.34% and 83.67%.
The lack of pixel-wise annotated images severely hinders the deep learning approach to computer-aided diagnosis in histopathology. This research creates a public database comprised of: (i) a dataset of 82 histopathological images of hematoxylin-eosin stained frozen sections acquired intraoperatively on 19 patients diagnosed with metastatic colon cancer in a liver; (ii) corresponding pixel-wise ground truth maps annotated by four pathologists, two residents in pathology, and one final-year student of medicine. The Fleiss' kappa equal to 0.74 indicates substantial inter-annotator agreement; (iii) two datasets with images stain-normalized relative to two target images; (iv) development of two conventional machine learning and three deep learning-based diagnostic models. The database is available at http://cocahis.irb.hr. For binary, cancer vs. non-cancer, pixel-wise diagnosis we develop: SVM, kNN, U-Net, U-Net++, and DeepLabv3 classifiers that combine results from original images and stain-normalized images, which can be interpreted as different views. On average, deep learning classifiers outperformed SVM and kNN classifiers on an independent test set 14% in terms of micro balanced accuracy, 15% in terms of the micro F1 score, and 26% in terms of micro precision. As opposed to that, the difference in performance between deep classifiers is within 2%. We found an insignificant difference in performance between deep classifiers trained from scratch and corresponding classifiers pre-trained on non-domain image datasets. The best micro balanced accuracy estimated on the independent test set by the U-Net++ classifier equals 89.34%. Corresponding amounts of F1 score and precision are, respectively, 83.67% and 81.11%.
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