Effect of vitamin D supplementation in patients with chronic hepatitis C after direct-acting antiviral treatment: a randomized, double-blind, placebo-controlled trial

Background Replacement of vitamin D (VD) among patients with chronic hepatitis C (CHC) before viral eradication has demonstrated a protective effect on serum markers associated with hepatic fibrogenesis. We therefore hypothesized that VD may facilitate further fibrosis amelioration following curative treatment with direct-acting antivirals (DAA). Methods This study was a randomized, double-blind, placebo-controlled trial conducted between February 2018 and August 2018. Patients with CHC and VD deficiency were randomized in a 1:1 ratio to either receive ergicalciferol or placebo over 6 weeks. Biochemical analysis indicators, including 25-hydroxyvitamin D (25(OH)D), fibrogenic markers [(transforming growth factor beta 1 (TGF-β1) and tissue inhibitors of matrix metalloproteinases 1 (TIMP-1)], and fibrolytic markers [matrix metalloproteinase 9 (MMP-9) and amino terminal type III procollagen peptide (P3NP)], were assessed at baseline and at 6 weeks. Serum 25(OH)D was analyzed by a chemiluminescence immunoassay. Serum hepatic fibrogenesis markers were measured using a quantitative sandwich enzyme-linked immunosorbent assay. Results Seventy-five patients with CHC and VD deficiency were randomly assigned to VD (n = 37) and placebo (n = 38) groups. At the end of the study, the mean serum 25(OH)D level had risen to a normal level in the VD group, but was still deficient in the placebo group (41.8 ±   9.1 vs. 18.1 ±  4.6 ng/mL, p < 0.001). Upon restoration of the VD level, there were no significant mean differences in the change from baseline for TGF-β1 (−0.6 ng/mL (95% confidence interval (95% CI) [−2.8–1.7]), p = 0.63), TIMP-1 (−5.5 ng/mL (95% CI [−26.4 –15.3]), p = 0.60), MMP-9 (122.9 ng/mL (95% CI [−69.0 –314.8]), p = 0.21), and P3NP (−0.1 ng/mL (95% CI [−2.4 –2.2]), p = 0.92) between the VD and placebo groups. Conclusion Short-term VD supplementation after DAA treatment in patients with CHC does not improve serum fibrogenesis markers and may not expedite the residual liver fibrosis healing process. Future studies are warranted to evaluate the long-term effect of VD supplementation on hepatic fibrosis regression

3D plane cuts and cubic Bézier curve for CT liver volume segmentation according to Couinaud’s classification

In pre-operative planning for partial liver transplantation, the total liver volume must be virtually segmented from a set of CT scanned images. The liver, consequently, is divided into eight segments according to Couinaud’s classification using hepatic and portal veins as clues. To facilitate the visualization of the segmented liver model, we propose a computerized process using four 3D plane cuts and cubic Bézier curve. In our experiments, fifteen liver volumes were used, and each of them was cut into eight segments using our program and their average percentage volumes were analyzed. The results were in agreement with the ground truth. Our program is semi-automatic. It requires minimal user interactions. As a result, the user can easily view the segmented liver model in both 2D and 3D perspectives

Development and validation of artificial intelligence to detect and diagnose liver lesions from ultrasound images.

Artificial intelligence (AI) using a convolutional neural network (CNN) has demonstrated promising performance in radiological analysis. We aimed to develop and validate a CNN for the detection and diagnosis of focal liver lesions (FLLs) from ultrasonography (USG) still images. The CNN was developed with a supervised training method using 40,397 retrospectively collected images from 3,487 patients, including 20,432 FLLs (hepatocellular carcinomas (HCCs), cysts, hemangiomas, focal fatty sparing, and focal fatty infiltration). AI performance was evaluated using an internal test set of 6,191 images with 845 FLLs, then externally validated using 18,922 images with 1,195 FLLs from two additional hospitals. The internal evaluation yielded an overall detection rate, diagnostic sensitivity and specificity of 87.0% (95%CI: 84.3-89.6), 83.9% (95%CI: 80.3-87.4), and 97.1% (95%CI: 96.5-97.7), respectively. The CNN also performed consistently well on external validation cohorts, with a detection rate, diagnostic sensitivity and specificity of 75.0% (95%CI: 71.7-78.3), 84.9% (95%CI: 81.6-88.2), and 97.1% (95%CI: 96.5-97.6), respectively. For diagnosis of HCC, the CNN yielded sensitivity, specificity, and negative predictive value (NPV) of 73.6% (95%CI: 64.3-82.8), 97.8% (95%CI: 96.7-98.9), and 96.5% (95%CI: 95.0-97.9) on the internal test set; and 81.5% (95%CI: 74.2-88.8), 94.4% (95%CI: 92.8-96.0), and 97.4% (95%CI: 96.2-98.5) on the external validation set, respectively. CNN detected and diagnosed common FLLs in USG images with excellent specificity and NPV for HCC. Further development of an AI system for real-time detection and characterization of FLLs in USG is warranted