PURPOSE Liver-directed therapy after transarterial chemoembolization (TACE) can lead to improvement in survival for selected patients with unresectable hepatocellular carcinoma (HCC). However, there is uncertainty in the appropriate application and modality of therapy in current clinical practice guidelines. The aim of this study was to develop a proof-of-concept, machine learning (ML) model for treatment recommendation in patients previously treated with TACE and select patients who might benefit from additional treatment with combination stereotactic body radiotherapy (SBRT) or radiofrequency ablation (RFA). METHODS This retrospective observational study was based on data from an urban, academic hospital system selecting for patients diagnosed with stage I-III HCC from January 1, 2008, to December 31, 2018, treated with TACE, followed by adjuvant RFA, SBRT, or no additional liver-directed modality. A feedforward, ML ensemble model provided a treatment recommendation on the basis of pairwise assessments evaluating each potential treatment option and estimated benefit in survival. RESULTS Two hundred thirty-seven patients met inclusion criteria, of whom 54 (23%) and 49 (21%) received combination of TACE and SBRT or TACE and RFA, respectively. The ML model suggested a different consolidative modality in 32.7% of cases among patients who had previously received combination treatment. Patients treated in concordance with model recommendations had significant improvement in progression-free survival (hazard ratio 0.5; P = .007). The most important features for model prediction were cause of cirrhosis, stage of disease, and albumin-bilirubin grade (a measure of liver function). CONCLUSION In this proof-of-concept study, an ensemble ML model was able to provide treatment recommendations for HCC who had undergone prior TACE. Additional treatment in line with model recommendations was associated with significant improvement in progression-free survival, suggesting a potential benefit for ML-guided medical decision making.
e16229 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.
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