Abstract
To predict outcome of 90Y radioembolization (RE) in patients with intrahepatic tumors from pre-therapeutic baseline parameters and to identify predictive variables using a machine-learning approach based on random survival forests (RSF). Materials and Methods: In this retrospective study, 366 patients with primary (n = 92) or secondary (n = 274) liver tumors who had received 90Y radioembolization were analyzed. A random survival forest was trained to predict individual risk from baseline values of cholinesterase (CHE), bilirubin, type of primary tumor, age at radioembolization, hepatic tumor burden, presence of extrahepatic disease (EHD) and sex. The predictive importance of each baseline parameter was determined using the minimal depth concept, and the partial dependency of predicted risk on the continuous variables bilirubin level and cholinesterase level was determined. Results: Median overall survival was 11.4 months (95% C.I. 9.7-14.2 months) with 228 deaths observed during the observation period. The random survival forest analysis identified baseline cholinesterase and bilirubin as the most important variables with the forest-averaged lowest minimal depth of 1.2 and 1.5, followed by the type of primary tumor (1.7), age (2.4), tumor burden (2.8) and presence of extrahepatic disease (3.5). Sex had the highest forest-averaged minimal depth (5.5), indicating little predictive value. Baseline bilirubin levels above 1.5 mg/dl were associated with a steep increase in predicted mortality. Similarly, cholinesterase levels below 7.5 U/ predicted a strong increase in mortality. The trained random survival forest achieved a concordance index of c=0.657, with a standard error of 0.02, comparable to c=0.652 (0.02) of a previously published Cox proportional hazards model. Conclusion: Random survival forests are a simple and straightforward machine learning approach for prediction of overall survival. Predictive performance of the trained model was similar to a previously published Cox regression model. The model has revealed a strong predictive value of baseline cholinesterase and bilirubin levels with a highly nonlinear influence of each parameter.
- Oncology: Liver
- Radionuclide Therapy
- Statistical Analysis
- Statistics
- Radioembolization
- bilirubin
- cholinesterase
- prediction
- random survival forest
- Copyright © 2017 by the Society of Nuclear Medicine and Molecular Imaging, Inc.