Predicting the complexity of minimally invasive liver resection for hepatocellular carcinoma using machine learning

Background: Despite technical advancements, minimally invasive liver surgery (MILS) for hepatocellular carcinoma (HCC) remains challenging. Nonetheless, effective tools to assess MILS complexity are still lacking. Machine learning (ML) models could improve the accuracy of such tools. Methods: Patients who underwent curative-intent MILS for HCC were identified using an international database. An XGBoost ML model was developed to predict surgical complexity using clinical and radiological characteristics. Results: Among 845 patients, 186 (22.0 %) were classified as high-risk patients. In this subgroup, median Charlson Comorbidity Index (CCI) (5.0, IQR 3.0-7.0 vs. 2.0, IQR 2.0-5.0, p < 0.001) and tumor burden score (TBS) (median 4.12, IQR 3.0-5.1 vs. 4.22, IQR 3.2-7.1, p <0.001) were higher. The model was able to effectively predict complexity of surgery in both the training and testing cohorts with high discriminating power (ROC-AUC: 0.86, 95%CI 0.82-0.89 vs. 0.73, 95%CI 0.65-0.81). The most influential variables were CCI, TBS, BMI, extent of resection, and sex. Patients predicted to have a complex surgery were more likely to develop severe complications (OR 4.77, 95%CI 1.82-13.9, p = 0.002). An easy-to-use calculator was developed. Conclusion: Preoperative ML-prediction of complex MILS for HCC may improve preoperative planning, resource allocation, and patient outcomes.