Congenital left intrahepatic bile duct draining into gastric wall mimicking biliary reflux gastritis

Abnormalities and variations of the biliary ducts are not rare.Most aberrant bile ducts eventually drain into the descending part of duodenum through the papilla of vater.However,drainage of the left hepatic bile duct into the stomach is extremely rare.A 29-year old man was admitted to the hospital with the diagnosis of biliary reflux gastritis.Comprehensive imaging modalities were performed including electronic endoscopy,endoscopic ultrasonography,endoscopic retrograde cholangiopancreatography and magnetic resonance cholangio-pancreatography.Finally,congenital ectopic left intrahepatic bile duct draining into the stomach was found,which caused biliary reflux gastritis.The patient did not receive any surgery.Good recovery was achieved by medical treatment.

Deep Segmentation Feature-Based Radiomics Improves Recurrence Prediction of Hepatocellular Carcinoma

Objective and Impact Statement.This study developed and validated a deep semantic segmentation feature-based radiomics(DSFR)model based on preoperative contrast-enhanced computed tomography(CECT)combined with clinical information to predict early recurrence(ER)of single hepatocellular carcinoma(HCC)after curative resection.ER prediction is of great significance to the therapeutic decision-making and surveillance strategy of HCC.Introduction.ER prediction is important for HCC.However,it cannot currently be adequately determined.Methods.Totally,208 patients with single HCC after curative resection were retrospectively recruited into a model-development cohort(n=180)and an independent validation cohort(n=28).DSFR models based on different CT phases were developed.The optimal DSFR model was incorporated with clinical information to establish a DSFR-C model.An integrated nomogram based on the Cox regression was established.The DSFR signature was used to stratify high-and low-risk ER groups.Results.A portal phase-based DSFR model was selected as the optimal model(area under receiver operating characteristic curve(AUC):development cohort,0.740;validation cohort,0.717).The DSFR-C model achieved AUCs of 0.782 and 0.744 in the development and validation cohorts,respectively.In the development and validation cohorts,the integrated nomogram achieved C-index of 0.748 and 0.741 and time-dependent AUCs of 0.823 and 0.822,respectively,for recurrence-free survival(RFS)prediction.The RFS difference between the risk groups was statistically significant(P<0.0001 and P=0.045 in the development and validation cohorts,respectively).Conclusion.CECT-based DSFR can predict ER in single HCC after curative resection,and its combination with clinical information further improved the performance for ER prediction.