Jhaveri, Kartik

Living donor liver transplantation (LDLT) is an established therapy with curative intent for pediatric and adult patients with acute liver failure and end-stage liver disease. Donor safety remains paramount and commences during preoperative evaluation and assessment. Given the importance of the topic, the International Liver Transplantation Society and International Living Donor Liver Transplantation Group consensus conference on Living Liver Donor Safety was convened in March 2025 (Toronto, Canada). Recommendations were based on the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) system for assessment of recommendations, and the Danish model of consensus was followed. This report presents 28 recommendations from the working group focused on addressing clinically relevant questions related to the preoperative aspects of living donor safety including assessment of donor acceptability (age, body mass index and hepatic steatosis; medical conditions and contraindications), medical and surgical workup (liver function and procoagulant workup; anatomical considerations and safe remnant), and psychosocial evaluation (timing and team; underlying conditions; non-directed, paired, and anonymous directed donors; urgent living donor workups for acute liver failure).

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.

Objective The objective of this study was to assess the accuracy of gadoxetic acid hepatic enhancement indices in predicting posthepatectomy liver failure (PHLF) and other major complications (OMCs). Methods Sixty-five patients underwent prehepatectomy gadoxetic acid–enhanced magnetic resonance imaging. Enhancement indices were calculated by obtaining regions of interest on magnetic resonance images and segmented volumes of the liver and spleen. Multivariate regression analysis was performed to predict PHLF and OMC as a function of the indices, and areas under the receiver operator characteristic (AUROC) curves were calculated. Results Areas under the receiver operator characteristic values varied from 0.412 to 0.681 and 0.462 to 0.738 in predicting PHLF and OMC, respectively. The most accurate indices in predicting PHLF were the region of interest–based, fat-normalized relative liver enhancement and liver enhancement index (AUROC, 0.681). The most accurate index in predicting OMC was the volumetric least-squares regression slope of a pharmacokinetic model (K hep_V, AUROC, 0.738). Conclusions Indices of gadoxetic acid liver enhancement demonstrate variable performance in predicting PHLF and OMC.