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.

Complete response of a metastatic microsatellite-stable gastric cancer after neoadjuvant chemoimmunotherapy: should we still operate? A case report and review of the literature.

Gastric cancer often presents at an advanced stage in Western populations due to a lack of screening programs, leading to poor prognoses. Historically, palliative chemotherapy resulted in a median survival of 9.9 months.

Analyzing the interaction between time to surgery and tumor burden score in hepatocellular carcinoma.

Background: The effect of "time to surgery (TTS)" on outcomes for curative-intent hepatectomy of hepatocellular carcinoma (HCC) remains debated. The interaction between tumor burden score (TBS) and TTS remains unclear. We sought to evaluate the effects of TBS and TTS on long-term HCC outcomes.