Citations

Background and Aims: Among those with primary sclerosing cholangitis (PSC), perihilar cholangiocarcinoma (pCCA) is often diagnosed at a late stage and is a leading source of mortality. Detection of pCCA in PSC when curative action can be taken is challenging. Our aim was to create a deep learning model that analyzed MRI to detect early-stage pCCA and compare its diagnostic performance with expert radiologists. Approach and Results: We conducted a multicenter, international, retrospective cohort study involving adults with large duct PSC who underwent contrast-enhanced MRI. Senior abdominal radiologists reviewed the images. All patients with pCCA had early-stage cancer and were registered for liver transplantation. We trained a 3D DenseNet-121 model, a form of deep learning, using MRI images and assessed its performance in a separate test cohort. The study included 398 patients (training cohort n=150; test cohort n=248). pCCA was present in 230 individuals (training cohort n=64; test cohort n=166). In the test cohort, the respective performances of the model compared to the radiologists were: sensitivity 87.9% versus 50.0%, p <0.001; specificity 84.1% versus 100.0%, p <0.001; area under receiving operating curve 86.0% versus 75.0%, p <0.001. Even when a mass was absent, the model had a higher sensitivity for pCCA than radiologists (91.6% vs. 50.6%, p <0.001) and maintained good specificity (84.1%). Conclusions: The 3D DenseNet-121 MRI model effectively detects early-stage pCCA in PSC patients. Compared to expert radiologists, the model missed fewer cases of cancer.

ABSTRACT Metabolic-associated steatohepatitis-driven hepatocellular carcinoma (MASH-HCC) incidence is rapidly rising worldwide. Lipid metabolic reprogramming is a hallmark of solid tumors to satisfy cancer high metabolic demand. However, it may confer sensitivity to ferroptosis, a cell death mode driven by iron-dependent lipid peroxidation. In this report, we describe the lipid metabolic landscape in MASH-HCC and characterize long chain acyl-CoA synthetases (ACSLs), a family of enzymes involved in synthesis of cellular lipids. Bulk RNA-sequencing, single-cell RNA-sequencing, spatial transcriptomics and immunohistochemistry analyses of human MASH-HCC were integrated to identify differentially expressed lipid metabolism genes. Ferroptosis in vitro was assessed in human HCC cell lines. A characterization of ACSLs was also conducted at the single-cell level in a diet-induced experimental murine model of MASH-HCC. Our analysis revealed that in human MASH-HCC, ACSLs exhibit a heterogeneous expression, with ACSL4 notably enriched in tumor tissues, contrasting with ACSL5 upregulation in non-cancerous MASH. We identified a unique lipid metabolic gene signature of MASH-HCC, which included genes associated with ferroptosis vulnerability. In vitro , ACSL4 upregulation was associated with increased ferroptosis sensitivity in human HCC cell lines. Lastly, single-cell RNA-sequencing revealed elevated ACSL4 expression in immune cells in a murine MASH-HCC model, suggesting a role of ACSL4 in shaping the tumor immune microenvironment. Overall, this report offers new insights into lipid metabolic landscape and ferroptosis sensitivity for novel MASH-HCC treatments. GRAPHICAL ABSTRACT

Abstract Reconstructing gene expression atlases for human tissues is challenging due to limited access to healthy samples from live donors. Neurologically deceased donors often show ischemic changes, while tissues near diseased regions may have altered gene expression. The liver, with its unique regenerative capacity, allows analysis from live healthy donors (LHDs). Using spatial transcriptomics (Visum, Visium HD and MERFISH), we analyzed 16 liver samples: eight from young LHDs and eight from patients with liver pathology, sampling ‘adjacent normal’ tissue. LHD livers displayed significant gene expression differences from ‘adjacent normal’ tissues. Hepatocytes exhibited marked zonation along the porto-central axis of liver lobules, with key functions pericentrally shifted compared to other mammals. Our atlas identified dynamic programs in early steatotic hepatocytes, showing transitions from lipid uptake in low-lipid regions to insulin hypersensitivity in high-lipid regions. This study presents a spatial gene expression reference for the healthy human liver and insights into hepatocyte adaptations in steatosis.

Background. Early allograft dysfunction (EAD) affects outcomes in liver transplantation (LT). Existing risk models developed for deceased-donor LT depend on posttransplant factors and fall short in living-donor LT (LDLT), where pretransplant evaluations are crucial for preventing EAD and justifying the donor’s risks. Methods. This retrospective study analyzed data from 2944 adult patients who underwent LDLT at 17 centers between 2016 and 2020. We developed a logistic regression model to predict EAD based on this development cohort. We used data from 1020 patients at the King Faisal Transplant Center for external validation. Results. In the development cohort, 321 patients (10.9%) experienced EAD. These patients had poorer health status, more liver decompensation, and higher requirements of hospitalization than those without EAD. Multivariable logistic regression identified independent pretransplant predictors of EAD: laboratory Model for End-Stage Liver Disease score (odds ratio [OR], 1.08; 95% confidence interval [CI], 1.06-1.09), the necessity for hospitalization at the time of transplant (OR, 2.58; 95% CI, 2.00-3.30), and graft weight in kilogram (OR, 0.27; 95% CI, 0.17-0.45). Using these predictors, we developed the model for EAD after LDLT, which demonstrated strong discriminative ability in the development cohort with an area under the curve (AUC) of 0.71 (95% CI, 0.68-0.74). The model maintained high discrimination during internal validation (AUC, 0.70; 95% CI, 0.67-0.73) and showed a modest reduction in discriminative power in external validation (AUC, 0.65; 95% CI, 0.61-0.68). Conclusions. EAD post-LDLT is influenced by the recipient’s pretransplant health condition and the graft weight. Integrating the model for EAD after LDLT into the pretransplant process of pairing donors and recipients can enhance the safety and efficacy of LDLT.

Background The Scientific Registry for Transplant Recipients (SRTR) publishes outcomes of all transplant centres in the USA two times a year. The outcomes are publicly available and used by insurance payers and patients to assess the performance of a programme. Poor performance can result in temporary suspension or termination of a transplant programme. The estimated 1-year survival hazard ratio (EHR) is an important metric publicly reported by the SRTR. Problem The EHR at our institution was 1.13, indicating a graft loss rate that was 13% higher than the national average. Methods/INTERVENTION We defined an improvement in this metric as achieving an EHR of <1.0. Our balance measure was maintaining similar liver transplant volumes and avoiding limiting access to transplant. Using a causality tree, we identified there was no ‘real time’ assessment of programme risk or objective metric to assess this. An affinity diagram was used to determine high and intermediate risk factors for mortality and graft loss and, using a REDCap form (a web application used to manage our database) to track actual and potential complications, we calculated a weekly ‘risk metric’ that was introduced at multidisciplinary selection conference meetings. Results We remeasured our EHR at each interval release of the SRTR outcomes and found it to be 0.98 and 0.65 after implementing the ‘risk metric.’ During the intervention period, annual liver transplant volume remained above the baseline measure. Conclusion By implementing a ‘risk metric’ to prospectively assess the risk of a low EHR at transplant selection committee meetings, we were able to reduce the EHR well below the national average without limiting access to liver transplants.

With longer survival of patients with primary sclerosing cholangitis (PSC) undergoing liver transplantation (LT), the frequency and risk factors associated with vascular and biliary complications in the allograft and the impact on long-term outcomes are poorly understood. To assess frequency and risk factors for long-term outcomes in patients after LT for PSC, all recipients of LT for advanced stage PSC for a non-cholangiocarcinoma indication from 1984 to 2012, with follow-up through March 2022 (>10+ y follow-up), were identified. One-, 5-, and 10-year cumulative risks of complications were estimated using the Aalen-Johansen method, where death was considered a competing risk. Two hundred ninety-three patients (mean age, 47.3 ± 12 y) formed our study cohort. One hundred and thirty-four patients received LT before 1995, and 159 were transplanted after 1995. Over a median (IQR) follow-up of 15.0 (10.3–22.1) years, LT was complicated by hepatic artery thrombosis (N = 30), portal vein stenosis/thrombosis (N = 48), biliary leak (N = 47), biliary strictures (N = 87), recurrent PSC (N=107), and graft failure (N=70). The 1-, 5-, 10-, and 15-year cumulative incidence of recurrent PSC was 1.0%, 8.0%, 23.5%, and 34.3%, respectively. The type of donor and older donor age were associated with an increased risk of biliary strictures. Donor age >60 years was associated with an increased risk of recurrent PSC. Long-term patient and graft survival have not changed significantly for patients transplanted for PSC. Controlling transplant-related factors, such as donor age, prompt identification of vascular and biliary complications early, and long-term rigorous follow-up, is recommended to continue to improve on these outcomes.

Background: Previous data suggested that the outcomes for liver-alone transplant recipients following transjugular intrahepatic portosystemic shunt (TIPS) insertion were comparable to those without TIPS. This study investigates the association between TIPS and outcomes among simultaneous liver–kidney (SLK) recipients in the United States. Methods: Utilizing the Scientific Registry for Transplant Recipients (SRTR) standard analysis file from 2003 to 2022, we examined 9717 adult SLK recipients, among whom 858 had undergone TIPS before transplantation. Kaplan–Meier curves were generated to assess recipient and death-censored liver and kidney graft survival. Mixed-effects Cox proportional hazard models were employed to analyze the association between TIPS and the outcomes of interest, where the transplant center was treated as a random effect. The models were adjusted for recipient age, sex, MELD score, diabetes, duration of listing, induction, steroid maintenance, hepatitis C status, donor age, donor sex, cold ischemia time, local vs. shipped organs, and allocation era. Results: Overall, the two groups were comparable, with minor differences. Notably, the median liver waiting time was significantly longer in the TIPS group compared to the non-TIPS group (4.1 vs. 2 months, p < 0.001). One-year rejection rates for liver and kidney allografts did not differ significantly between groups. Univariable Cox regression analyses demonstrated no association between TIPS and worse outcomes for recipient, liver, and kidney survival (p = 0.65, p = 0.22, and p = 0.54, respectively). TIPS did not emerge as a predictor of recipient or death-censored liver or kidney graft survival in multivariable models. Conclusion: In this extensive national cohort of SLK transplant recipients, pre-transplant TIPS was not linked to adverse outcomes for recipients or their allografts.

ABSTRACT Background and Objectives Hepatic epithelioid hemangioendothelioma (HEHE) is an uncommon vascular neoplasm characterized by variable clinical behavior. Our aim was to describe the therapeutic approach for HEHE at diagnosis and define clinicopathological characteristics associated with tumor progression and long‐term survival. Methods This is a retrospective study that includes patients with HEHE who received treatment at Mayo Clinic Rochester between 1984 and 2023. Results Eighty patients were included in the study (median age: 44 years; 62.5% female), 24 underwent liver transplantation, 26 underwent liver resection, and 30 were managed medically. The 3‐year overall survival rates were 86.7%, 80.9%, and 51.1%, respectively. Notably, 26 patients had extrahepatic metastases at the time of diagnosis, four (16.7%) in the transplantation group, four (15.4%) in the resection group, and 18 (69.2%) in the nonsurgical group. On multivariable modeling, bone metastasis was independently associated with long‐term mortality (HR 6.3, p < 0.001) while lung metastasis and surgical intervention were not associated with long‐term mortality (HR 0.8, p = 0.8; HR 1.1, p = 0.9, respectively). Conclusion Bone metastasis emerged as a strong predictor of poor survival. Hence, aggressive surgical intervention may not be advantageous in patients with skeletal metastases but can still be offered for those with other extrahepatic metastases.

Objectives: Determine whether the timing of transplantation affects patient mortality. Background: Neoadjuvant therapy and liver transplantation have emerged as an excellent treatment option for select patients with perihilar cholangiocarcinoma (pCCA). However, the optimal timing of transplantation is not known. Methods: We reviewed all patients registered for a standardized pCCA protocol between 1996 and 2020 at our center. After adjusting for confounders, we examined the association of waiting time with patient mortality in an intention-to-treat cohort (n=392) and those who received a liver transplant (n=256). Results: The median (interquartile range) time from registration to transplant or dropout was 5.74 (3.25–7.06) months. Compared with a short wait time (0–3 mo), longer waiting times did not affect all-cause mortality: (3–6 mo) hazard ratio (HR) 0.98; 95% CI: 0.52–1.84; (6–9 mo) HR: 0.80; 95% CI: 0.39–1.65; (9–12 mo) HR: 0.56; 95% CI: 0.26–1.22. Subgroups with a shorter waiting time had similar survival to those with long waiting times: living donor available HR: 0.97; 95% CI: 0.67–1.42; AB or B blood group HR: 0.93; 95% CI: 0.62–1.39. Longer waiting times were associated with decreased all-cause mortality after transplantation (HR: 0.92; 95% CI: 0.87–0.97). This benefit began after a 6-month waiting time minimum (HR: 0.53; 95% CI: 0.26–1.10) and increased further after 9 months (HR: 0.43 95% CI: 0.20–0.93). Waiting time was not associated with residual adenocarcinoma in the explant (odds ratio 0.99; 95% CI: 0.98–1.00). Conclusions: A waiting time of at least 6 months will optimize results with transplantation without affecting overall (intention-to-treat) patient survival.