Using machine learning-based boosting algorithm to predict hepatotoxicity after local therapy and association with survival in patients undergoing liver directed therapy.

person Allen Mo Montefiore Medical Center, Bronx, NY info_outline Allen Mo, Christian Velten, Justin Tang, Nitin Ohri, Shalom Kalnicki, Parsa Mirhaji, Andreas Kaubisch, Selvin Soby, Alexandre Peshansky, Erin Henninger, Madhur Garg, Chandan Guha, Rafi Kabarriti

Authors person Allen Mo Montefiore Medical Center, Bronx, NY info_outline Allen Mo, Christian Velten, Justin Tang, Nitin Ohri, Shalom Kalnicki, Parsa Mirhaji, Andreas Kaubisch, Selvin Soby, Alexandre Peshansky, Erin Henninger, Madhur Garg, Chandan Guha, Rafi Kabarriti Organizations Montefiore Medical Center, Bronx, NY, Albert Einstein College of Medicine, Bronx, NY, Alber, Bronx, NY, Albert Einstein College of Medicine - Montefiore Medical Center, Bronx, NY Abstract Disclosures Research Funding Other Foundation Radiation Oncology Institute Background: Hepatocellular carcinoma typically arises in the setting of chronic liver disease. In carefully selected patients, liver directed therapy has been demonstrated to improve survival; however, this benefit from cancer specific survival may be offset by increased mortality from diminished hepatic functional reserve. Treatment-related hepatotoxicity also may vary between liver directed treatment modalities for a given tumor. Our aim was to determine if a machine learning approach could estimate hepatotoxicity prior to treatment. Methods: We identified all patients with hepatocellular carcinoma who underwent liver directed therapy at a large urban academic health system who also had post-treatment laboratory follow-up at 6 months. Patients were categorized as having worse liver function if they experienced a decline in albumin-bilirubin (ALBI) grade or more than 0.5 absolute decline in ALBI score at post treatment follow-up. A machine learning, extreme-gradient boosting (XGBoost) approach was employed to identify predictive features for hepatotoxicity using fine-tuned hyperparameters. Results: The study cohort consisted of 269 patients. 48 (18%) were categorized as having worsened hepatic function after liver directed therapy, with an average decline of -0.72 in ALBI score. Median survival after liver directed therapy was 5.8 years, with worsening hepatic function associated with a 5 month decline in overall survival. 158 (59%) patients received transarterial chemoembolization, 99 (37%) patients received stereotactic body radiotherapy and 12 (7%) patients received Y-90. A machine learning model was developed resulting in an Area Under the ROC Curve (AUC) of 0.894 in a 10-point decision tree model. On SHAP analysis, the features with the largest impact on hepatotoxicity prior to treatment was pretreatment T-stage followed by Child-Pugh Score. Conclusions: A machine learning model was developed to accurately classify patients who sustained treatment related hepatotoxicity. Significant decline in ALBI score or grade was associated with a worsening of overall survival. Future studies in larger prospective clinical trials utilizing this machine learning-based methodology of assessing liver dysfunction after focal therapy are required to validate these findings.