Illuminating the black box: Machine learning enhances preoperative prediction in intrahepatic cholangiocarcinoma.
Journal:
World journal of gastroenterology
Published Date:
May 7, 2025
Abstract
The study by Huang , published in the , advances intrahepatic cholangiocarcinoma (ICC) management by developing a machine-learning model to predict textbook outcomes (TO) based on preoperative factors. By analyzing data from 376 patients across four Chinese medical centers, the researchers identified key variables influencing TO, including Child-Pugh classification, Eastern Cooperative Oncology Group score, hepatitis B status, and tumor size. The model, created using logistic regression and the extreme gradient boosting algorithm, demonstrated high predictive accuracy, with area under the curve values of 0.8825 for internal validation and 0.8346 for external validation. The integration of the Shapley additive explanation technique enhances the interpretability of the model, which is crucial for clinical decision-making. This research highlights the potential of machine learning to improve surgical planning and patient outcomes in ICC, opening possibilities for personalized treatment approaches based on individual patient characteristics and risk factors.
