Real-Time Biomarkers of Liver Graft Quality in Hypothermic Oxygenated Machine Perfusion.

: Hypothermic oxygenated machine perfusion has emerged as a strategy to alleviate ischemic-reperfusion injury in liver grafts. Nevertheless, there is limited data on the effectiveness of hypothermic liver perfusion in evaluating organ quality. This study aimed to introduce a readily accessible real-time predictive biomarker measured in machine perfusate for post-transplant liver graft function.

Is operation time over the benchmark value a risk factor for worse short-term outcomes after laparoscopic liver resection?

Introduction: Laparoscopic liver resection is a challenging surgical procedure that may require prolonged operation time, particularly during the learning curve. Operation time significantly decreases with increasing experience; however, prolonged operation time may significantly increase the risk of postoperative complications.

Aim: To assess whether prolonged operation time over the benchmark value influences short-term postoperative outcomes after laparoscopic liver resection.

A novel radiomics approach for predicting TACE outcomes in hepatocellular carcinoma patients using deep learning for multi-organ segmentation.

Transarterial chemoembolization (TACE) represent the standard of therapy for non-operative hepatocellular carcinoma (HCC), while prediction of long term treatment outcomes is a complex and multifactorial task. In this study, we present a novel machine learning approach utilizing radiomics features from multiple organ volumes of interest (VOIs) to predict TACE outcomes for 252 HCC patients. Unlike conventional radiomics models requiring laborious manual segmentation limited to tumoral regions, our approach captures information comprehensively across various VOIs using a fully automated, pretrained deep learning model applied to pre-TACE CT images.