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.

Perspectives of the medical oncologist regarding adjuvant chemotherapy for pancreatic cancer: An international expert survey and case vignette study.

Introduction: Adjuvant chemotherapy improves survival in patients with resected pancreatic ductal adenocarcinoma (PDAC). The decision to initiate chemotherapy involves both patient and physician factors, decision-specific criteria, and contextual considerations. This study aimed to assess medical oncologists' views on adjuvant chemotherapy following pancreatic resection for PDAC.