Outcome prediction for cholangiocarcinoma prognosis: Embracing the machine learning era

Table 1 Machine learning approaches for preoperative risk stratification in intrahepatic cholangiocarcinoma

ML-driven approach	Description
Radiomics-based ML models	Extract imaging features from magnetic resonance or CT scans to predict tumor aggressiveness and microvascular invasion
Multiparametric clinical models	Integrate laboratory values, liver function scores, and tumor markers to assess perioperative risk
Hybrid AI models	Combine genomic, histopathological, and radiomic data to refine survival predictions and guide personalized treatment strategies

AI: Artificial intelligence; ML: Machine learning.