Impact of distance from liver transplant centre on outcomes following liver transplantation: an Australian single-centre study.

Background: Access to liver transplantation (LT) is affected by geographic disparities. Higher waitlist mortality is observed in patients residing farther from LT centres, but the impact of distance on post-LT outcomes is unclear.

Aims: To evaluate whether the distance LT recipients reside from their LT centre affects graft and patient outcomes.

Spatial mapping of the HCC landscape identifies unique intratumoral perivascular-immune neighborhoods.

Background: HCC develops in the context of chronic inflammation; however, the opposing roles the immune system plays in both the development and control of tumors are not fully understood. Mapping immune cell interactions across the distinct tissue regions could provide greater insight into the role individual immune populations have within tumors.

Methods: A 39-parameter imaging mass cytometry panel was optimized with markers targeting immune cells, stromal cells, endothelial cells, hepatocytes, and tumor cells.

Developing an Indocyanine Green Angiography Protocol for Predicting Flap Necrosis During Breast Reconstruction.

Background: Although there is evidence that indocyanine green angiography (ICGA) can predict mastectomy skin flap necrosis during breast reconstruction, consensus on optimal protocol is lacking. This study aimed to evaluate various technical factors which can influence ICG fluorescence intensity and thus interpretation of angiograms.

Method: Single institution retrospective study (2015-2021) of immediate implant-based breast reconstructions postmastectomy using a standardized technique of ICGA, controlling for modifiable factors of ambient lighting, camera distance and ICG dose.

Textbook outcome in liver surgery for intrahepatic cholangiocarcinoma: defining predictors of an optimal postoperative course using machine learning.

Background: We sought to define textbook outcome in liver surgery (TOLS) for intrahepatic cholangiocarcinoma (ICC) by considering the implications of perioperative outcomes on overall survival (OS).

Methods: Using a multi-institutional database, TOLS for ICC was defined by employing novel machine learning (ML) models to identify perioperative factors most strongly predictive of OS ≥ 12 months. Subsequently, clinicopathologic factors associated with achieving TOLS were investigated.

Machine learning based prediction model for bile leak following hepatectomy for liver cancer.

Objective: We sought to develop a machine learning (ML) preoperative model to predict bile leak following hepatectomy for primary and secondary liver cancer.

Methods: An eXtreme Gradient Boosting (XGBoost) model was developed to predict post-hepatectomy bile leak using data from the ACS-NSQIP database. The model was externally validated using data from hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) multi-institutional databases.