A Machine-Based Approach to Preoperatively Identify Patients with the Most and Least Benefit Associated with Resection for Intrahepatic Cholangiocarcinoma: An International Multi-institutional Analysis of 1146 Patients

Background Accurate risk stratification and patient selection is necessary to identify patients who will benefit the most from surgery or be better treated with other non-surgical treatment strategies. We sought to identify which patients in the preoperative setting would likely derive the most or least benefit from resection of intrahepatic cholangiocarcinoma (ICC). Methods Patients who underwent curative-intent resection for ICC between 1990 and 2017 were identified from an international multi-institutional database. A machine-based classification and regression tree (CART) was used to generate homogeneous groups of patients relative to overall survival (OS) based on preoperative factors. Results Among 1146 patients, CART analysis revealed tumor number and size, albumin-bilirubin (ALBI) grade and preoperative lymph node (LN) status as the strongest prognostic factors associated with OS among patients undergoing resection for ICC. In turn, four groups of patients with distinct outcomes were generated through machine learning: Group 1 (n = 228): single ICC, size <= 5 cm, ALBI grade I, negative preoperative LN status; Group 2 (n = 708): (1) single tumor > 5 cm, (2) single tumor <= 5 cm, ALBI grade 2/3, and (3) single tumor <= 5 cm, ALBI grade 1, metastatic/suspicious LNs; Group 3 (n = 150): 2-3 tumors; Group 4 (n = 60): >= 4 tumors. 5-year OS among Group 1, 2, 3, and 4 patients was 60.5%, 35.8%, 27.5%, and 3.8%, respectively (p < 0.001). Similarly, 5-year disease-free survival (DFS) among Group 1, 2, 3, and 4 patients was 47%, 27.2%, 6.8%, and 0%, respectively (p < 0.001). Conclusions The machine-based CART model identified distinct prognostic groups of patients with distinct outcomes based on preoperative factors. Survival decision trees may be useful as guides in preoperative patient selection and risk stratification.