Background: The GALAD score is a serum biomarkerbased model that predicts the probability of having hepatocellular carcinoma (HCC) in patients with chronic liver disease. We aimed to assess the performance of the GALAD score in comparison with liver ultrasound for detection of HCC.Methods: A single-center cohort of 111 HCC patients and 180 controls with cirrhosis or chronic hepatitis B and a multicenter cohort of 233 early HCC and 412 cirrhosis patients from the Early Detection Research Network (EDRN) phase II HCC Study were analyzed.Results: The area under the ROC curve (AUC) of the GALAD score for HCC detection was 0.95 [95% confidence interval (CI), 0.93-97], which was higher than the AUC of ultrasound (0.82, P <0.01). At a cutoff of À0.76, the GALAD score had a sensitivity of 91% and a specificity of 85% for HCC detection. The AUC of the GALAD score for early-stage HCC detection remained high at 0.92 (95% CI, 0.88-0.96; cutoff À1.18, sensitivity 92%, specificity 79%). The AUC of the GALAD score for HCC detection was 0.88 (95% CI, 0.85-0.91) in the EDRN cohort. The combination of GALAD and ultrasound (GALADUS score) further improved the performance of the GALAD score in the single-center cohort, achieving an AUC of 0.98 (95% CI, 0.96-0.99; cutoff À0.18, sensitivity 95%, specificity 91%). a For the calculation of AUC, the continuous GALAD score is used, whereas for sensitivity and specificity, we used the GALAD cutoff. b P value looking at difference in AUC between GALAD and ultrasound.Yang et al.
Differences in height and MELD exception points explained most of the sex-based disparity in LT. Additionally, MELD score underestimated disease severity in women by up to 2.4 points and MELD Na exacerbated this disparity. The degree of underestimation based on MELD had significant impact on allocation.
Diabetes increases the risk of liver disease progression and cirrhosis development in patients with nonalcoholic steatohepatitis (NASH). The association between diabetes and the risk of hepatocellular carcinoma (HCC) in NASH patients with cirrhosis is not well quantified. All patients with the diagnosis of NASH cirrhosis seen at Mayo Clinic Rochester between January 2006 and December 2015 were identified. All adult liver transplant registrants with NASH between 2004 and 2017 were identified using the United Network for Organ Sharing (UNOS)/Organ Procurement and Transplantation registry for external validation. Cox proportional hazard analysis was performed to investigate the association between diabetes and HCC risk. Among 354 Mayo Clinic patients with NASH cirrhosis, 253 (71%) had diabetes and 145 (41%) were male. Mean age at cirrhosis evaluation was 62. During a median follow‐up of 47 months, 30 patients developed HCC. Diabetes was associated with an increased risk of developing HCC in univariate (hazard ratio [HR] = 3.6; 95% confidence interval [CI] = 1.1‐11.9; P = 0.04) and multivariable analysis (HR = 4.2; 95% CI = 1.2‐14.2; P = 0.02). In addition, age (per decade, HR = 1.8; 95% CI = 1.2‐2.6; P < 0.01) and low serum albumin (HR = 2.1; 95% CI = 1.5‐2.9; P < 0.01) were significantly associated with an increased risk of developing HCC in multivariable analysis. Other metabolic risk factors, including body mass index, hyperlipidemia, and hypertension, were not associated with HCC risk. Among UNOS NASH registrants (N = 6,630), 58% had diabetes. Diabetes was associated with an increased risk of developing HCC in univariate (HR = 1.4; 95% CI = 1.1‐1.8; P < 0.01) and multivariable (HR = 1.3; 95% CI = 1.0‐1.7; P = 0.03) analysis. Conclusion: Diabetes is associated with an increased risk of HCC in patients with NASH cirrhosis.
ObjectiveThe diagnosis of autoimmune pancreatitis (AIP) is challenging. Sonographic and cross-sectional imaging findings of AIP closely mimic pancreatic ductal adenocarcinoma (PDAC) and techniques for tissue sampling of AIP are suboptimal. These limitations often result in delayed or failed diagnosis, which negatively impact patient management and outcomes. This study aimed to create an endoscopic ultrasound (EUS)-based convolutional neural network (CNN) model trained to differentiate AIP from PDAC, chronic pancreatitis (CP) and normal pancreas (NP), with sufficient performance to analyse EUS video in real time.DesignA database of still image and video data obtained from EUS examinations of cases of AIP, PDAC, CP and NP was used to develop a CNN. Occlusion heatmap analysis was used to identify sonographic features the CNN valued when differentiating AIP from PDAC.ResultsFrom 583 patients (146 AIP, 292 PDAC, 72 CP and 73 NP), a total of 1 174 461 unique EUS images were extracted. For video data, the CNN processed 955 EUS frames per second and was: 99% sensitive, 98% specific for distinguishing AIP from NP; 94% sensitive, 71% specific for distinguishing AIP from CP; 90% sensitive, 93% specific for distinguishing AIP from PDAC; and 90% sensitive, 85% specific for distinguishing AIP from all studied conditions (ie, PDAC, CP and NP).ConclusionThe developed EUS-CNN model accurately differentiated AIP from PDAC and benign pancreatic conditions, thereby offering the capability of earlier and more accurate diagnosis. Use of this model offers the potential for more timely and appropriate patient care and improved outcome.
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