An MRI-Based Radiomics Model for Preoperative Prediction of Microvascular Invasion and Outcome in Intrahepatic Cholangiocarcinoma

Outcome prediction for cholangiocarcinoma prognosis: Embracing the machine learning era

We read with great interest the study by Huang et al. Cholangiocarcinoma (CC) is the second most common type of primary liver tumor worldwide. Although surgical resection remains the primary treatment for this disease, almost 50% of patients experience relapse within 2 years after surgery, which negatively affects their prognosis. Key predictors can be used to identify several factors (e.g., tumor size, tumor location, tumor stage, nerve invasion, the presence of intravascular emboli) and their correlations with long-term survival and the risk of postoperative morbidity. In recent years, artificial intelligence (AI) has become a new tool for prognostic assessment through the integration of multiple clinical, surgical, and imaging parameters. However, a crucial question has arisen: Are we ready to trust AI with respect to clinical decisions? The study by Huang et al demonstrated that AI can predict preoperative textbook outcomes in patients with CC and highlighted the precision of machine learning algorithms using useful prognostic factors. This letter to the editor aimed to explore the challenges and potential impact of AI and machine learning in the prognostic assessment of patients with CC.