Predicting futility of upfront surgery in perihilar cholangiocarcinoma: Machine learning analytics model to optimize treatment allocation.

backgroundWhile resection remains the only curative option for perihilar cholangiocarcinoma, it is well known that such surgery is associated with a high risk of morbidity and mortality. Nevertheless, beyond facing life-threatening complications, patients may also develop early disease recurrence, defining a "futile" outcome in perihilar cholangiocarcinoma surgery. The aim of this study is to predict the high-risk category (futile group) where surgical benefits are reversed and alternative treatments may be considered.

methodsThe study cohort included prospectively maintained data from 27 Western tertiary referral centers: the population was divided into a development and a validation cohort. The Framingham Heart Study methodology was used to develop a preoperative scoring system predicting the "futile" outcome.

resultsA total of 2271 cases were analyzed: among them, 309 were classified within the "futile group" (13.6%). American Society of Anesthesiology (ASA) score ≥ 3 (OR 1.60; p = 0.005), bilirubin at diagnosis ≥50 mmol/L (OR 1.50; p = 0.025), Ca 19-9 ≥ 100 U/mL (OR 1.73; p = 0.013), preoperative cholangitis (OR 1.75; p = 0.002), portal vein involvement (OR 1.61; p = 0.020), tumor diameter ≥3 cm (OR 1.76; p < 0.001), and left-sided resection (OR 2.00; p < 0.001) were identified as independent predictors of futility. The point system developed, defined three (ie, low, intermediate, and high) risk classes, which showed good accuracy (AUC 0.755) when tested on the validation cohort.

conclusionsThe possibility to accurately estimate, through a point system, the risk of severe postoperative morbidity and early recurrence, could be helpful in defining the best management strategy (surgery vs. nonsurgical treatments) according to preoperative features.