Dual outflow upper arm arteriovenous fistula: An effective technique to prevent cephalic arch stenosis.

Cephalic arch stenosis (CAS) is critical point to maintain functional arteriovenous fistula (AVF) in patients undergoing hemodialysis with brachio-cephalic AVFs. In this study, we aimed to determine the effectiveness of dual outflow (cephalic and basilic veins) as a surgical method to prevent CAS. Between July 2016 and December 2019, 369 patients underwent upper arm AVF creation. Among them the 251 patients were enrolled in this retrospective study. Two hundred seven underwent brachio-cephalic arteriovenous fistula (BCAVF) and 44 underwent brachio-cephalicbasilic arteriovenous fistula (BCBAVF). From the 251 patients, diabetes mellitus (66.7% vs 36.4%, P < .001) and hypertension (91.3% vs 75%, P = .002) were more common in the patient group who underwent BCAVF surgery; however, the difference in volume flow to the fistula did not differ between the 2 groups. CAS (30.4% vs 9.1%, P = .004) and fistula occlusion (15.9% vs 4.5%, P = .048) were likely to occur in the BCAVF group. The primary patency rates at 12 months were 74.3% and 86.4% for the BCAVFs and BCBAVFs, respectively (P = .075). The primary-assisted patency rates at 12 months were 87.0% for BCAVFs and 93.2% for BCBAVFs, respectively (P = .145). Secondary patency rates at 12 months were 92.2% for BCAVFs and 93.2% for BCBAVFs, respectively (P = .023). Compared to BCAVF, traditional upper arm AVF, upper arm AVF with cephalic and basilic vein dual drainage can be optimal surgical method to preventing CAS.

Estimation of right lobe graft weight for living donor liver transplantation using deep learning-based fully automatic computed tomographic volumetry.

This study aimed at developing a fully automatic technique for right lobe graft weight estimation using deep learning algorithms. The proposed method consists of segmentation of the full liver region from computed tomography (CT) images, classification of the entire liver region into the right and left lobes, and estimation of the right lobe graft weight from the CT-measured right lobe graft volume using a volume-to-weight conversion formula. The first two steps were performed with a transformer-based deep learning model. To train and evaluate the model, a total of 248 CT datasets (188 for training, 40 for validation, and 20 for testing and clinical evaluation) were used. The Dice similarity coefficient (DSC), mean surface distance (MSD), and the 95th percentile Hausdorff distance (HD95) were used for evaluating the segmentation accuracy of the full liver region and the right liver lobe. The correlation coefficient (CC), percentage error (PE), and percentage absolute error (PAE) were used for the clinical evaluation of the estimated right lobe graft weight. The proposed method achieved high accuracy in segmentation for DSC, MSD, and HD95 (95.9% ± 1.0%, 1.2 ± 0.4 mm, and 5.2 ± 1.9 mm for the entire liver region; 92.4% ± 2.7%, 2.0 ± 0.7 mm, and 8.8 ± 2.9 mm for the right lobe) and in clinical evaluation for CC, PE, and PAE (0.859, - 1.8% ± 9.6%, and 8.6% ± 4.7%). For the right lobe graft weight estimation, the present study underestimated the graft weight by - 1.8% on average. A mean difference of - 21.3 g (95% confidence interval: - 55.7 to 13.1, p = 0.211) between the estimated graft weight and the actual graft weight was achieved in this study. The proposed method is effective for clinical application.

Proposal of nomograms to predict clinical outcomes in patients with ampulla of Vater cancer based on the Korea-Japan collaborative study.

BACKGROUND: In this study, we aimed to develop and validate a nomogram to predict overall survival (OS) and recurrence-free survival (RFS) in patients who underwent curative resection of ampulla of Vater (AOV) cancer. This is the first study for nomograms in AOV cancer patients using retrospective data based on an international multicenter study. METHODS: A total of 2007 patients with AOV adenocarcinoma who received operative therapy between 2002 January and 2015 December in Korea and Japan were retrospectively assessed to develop a prediction model. Nomograms for 5-year OS and 3-year RFS were constructed by dividing the patients who received and who did not receive adjuvant therapy after surgery, respectively. Significant risk factors were identified by univariate and multivariate Cox analyses. Performance assessment of the four prediction models was conducted by the Harrell's concordance index (C-index) and calibration curves using bootstrapping. RESULTS: A total of 2007 and 1873 patients were collected for nomogram construction to predict 5-year OS and 3-year RFS. We developed four types of nomograms, including models for 5-year OS and 3-year RFS in patients who did not receive postoperative adjuvant therapy, and 5-year OS and 3-year RFS in patients who received postoperative adjuvant therapy. The C-indices of these nomograms were 0.795 (95% confidence interval [CI]: 0.766-0.823), 0.712 (95% CI: 0.674-0.750), 0.804 (95% CI: 0.7778-0.829), and 0.703 (95% CI: 0.669-0.737), respectively. CONCLUSIONS: This predictive model could help clinicians to choose optimal treatment and precisely predict prognosis in AOV cancer patients.

Expression of FAM83H and ZNF16 are associated with shorter survival of patients with gallbladder carcinoma.

BACKGROUND: Recently, FAM83H was reported to have roles in cancer progression in conjunction with oncogenic molecules such as MYC and b-catenin. Moreover, the data from the public database indicates a molecular relationship between FAM83H and zinc finger proteins, especially between FAM83H and ZNF16. However, studies on FAM83H and ZNF16 in gallbladder cancer have been limited. METHODS: This study investigated the expression of FAM83H and ZNF16 in 105 gallbladder carcinomas. RESULTS: In human gallbladder carcinomas, immunohistochemical expression of FAM83H was significantly associated with ZNF16 expression. In univariate analysis, nuclear and cytoplasmic expression of FAM83H or ZNF16 were significantly associated with shorter survival of gallbladder carcinoma patients. Multivariate analysis revealed the nuclear expression of FAM83H as an independent indicator of poor prognosis of overall survival (p = 0.005) and relapse-free survival (p = 0.005) of gallbladder carcinoma patients. Moreover, co-expression patterns of nuclear FAM83H and ZNF16 were also independent indicators of shorter survival of gallbladder carcinoma patients (overall survival; p <  0.001, relapse-free survival; p <  0.001). CONCLUSIONS: This study suggests FAM83H and ZNF16 are associated with the progression of gallbladder carcinoma, and the expressions of FAM83H and ZNF16 might be novel prognostic indicators of gallbladder carcinoma patients.