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.

Results: A total of 1556 patients with ICC were included. The ML classification models demonstrated that the absence of post-hepatectomy liver failure, intraoperative blood loss <750 mL, absence of major infectious complications, and R0 resection were the perioperative outcomes associated with prolonged OS, thereby defining TOLS for ICC. On multivariable analysis, older age, ASA class >2, lymph node metastasis, receipt of neoadjuvant therapy, advanced T status, poor histological grade and microvascular invasion were independently associated with lower odds of achieving TOLS (all p-values<0.05). Overall, 60.2 % (n = 936) of the patients achieved TOLS, demonstrating markedly improved OS and recurrence-free survival (RFS) than individuals who did not (both p < 0.05).

Conclusion: A standardized definition of TOLS for ICC was established that may be used to evaluate hospital performance at the patient level and help optimize surgical outcomes for patients with ICC.