The study by Huang et al, published in the World Journal of Gastroenterology, 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.
Keywords: Clinical decision-making; Disease-free survival; Extreme gradient boosting; Intrahepatic cholangiocarcinoma; Machine learning; Predictive model; Preoperative assessment; Shapley additive explanations; Surgical outcomes; Textbook outcome.
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