Preoperative blood and CT-image nutritional indicators in short-term outcomes and machine learning survival framework of intrahepatic cholangiocarcinoma.

BACKGROUND&AIMS: Intrahepatic cholangiocarcinoma (iCCA) is aggressive with limited treatment and poor prognosis. Preoperative nutritional status assessment is crucial for predicting outcomes in patients. This study aimed to compare the predictive capabilities of preoperative blood like albumin-bilirubin (ALBI), controlling nutritional status (CONUT), prognostic nutritional index (PNI) and CT-imaging nutritional indicators like skeletal muscle index (SMI), visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), visceral to subcutaneous adipose tissue ratio (VSR) in iCCA patients undergoing curative hepatectomy.

METHODS

290 iCCA patients from two centers were studied. Preoperative blood and CT-imaging nutritional indicators were evaluated. Short-term outcomes like complications, early recurrence (ER) and very early recurrence (VER), and overall survival (OS) as long-term outcome were assessed. Six machine learning (ML) models, including Gradient Boosting (GB) survival analysis, were developed to predict OS.

RESULTS

Preoperative blood nutritional indicators significantly associated with postoperative complications. CT-imaging nutritional indicators show insignificant associations with short-term outcomes. All preoperative nutritional indicators were not effective in predicting early tumor recurrence. For long-term outcomes, ALBI, CONUT, PNI, SMI, and VSR were significantly associated with OS. Six ML survival models demonstrated strong and stable performance. GB model showed the best predictive performance (C-index: 0.755 in training cohorts, 0.714 in validation cohorts). Time-dependent ROC, calibration, and decision curve analysis confirmed its clinical value.

CONCLUSION

Preoperative ALBI, CONUT, and PNI scores significantly correlated with complications but not ER. Four Image Nutritional Indicators were ineffective in evaluating short-term outcomes. Six ML models were developed based on nutritional and clinicopathological variables to predict iCCA prognosis.