Michael Ingrisch1, Franziska Schöppe2, Karolin Paprottka2, Matthias Fabritius2, Frederik F Strobl2, Enrico N De Toni3, Harun Ilhan4, Andrei Todica4, Marlies Michl5, Philipp Marius Paprottka2. 1. Department of Radiology, University Hospital Munich, Ludwig-Maximilians-University Munich, Munich, Germany michael.ingrisch@med.lmu.de. 2. Department of Radiology, University Hospital Munich, Ludwig-Maximilians-University Munich, Munich, Germany. 3. Department of Internal Medicine II, University Hospital Munich, Ludwig-Maximilians-University Munich, Munich, Germany. 4. Department of Nuclear Medicine, University Hospital Munich, Ludwig-Maximilians-University Munich, Munich, Germany; and. 5. Department of Internal Medicine III, University Hospital Munich, Ludwig-Maximilians-University Munich, Munich, Germany.
Abstract
Our objective was to predict the outcome of 90Y radioembolization in patients with intrahepatic tumors from pretherapeutic baseline parameters and to identify predictive variables using a machine-learning approach based on random survival forests. 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, bilirubin, type of primary tumor, age at radioembolization, hepatic tumor burden, presence of extrahepatic disease, 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 mo (95% confidence interval, 9.7-14.2 mo), with 228 deaths occurring during the observation period. The random-survival-forest analysis identified baseline cholinesterase and bilirubin as the most important variables (forest-averaged lowest minimal depth, 1.2 and 1.5, respectively), 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 0.657, with an SE of 0.02, comparable to the concordance index of 0.652 and SE of 0.02 for a previously published Cox proportional hazards model. Conclusion: Random survival forests are a simple and straightforward machine-learning approach for prediction of overall survival. The predictive performance of the trained model was similar to a previously published Cox regression model. The model has revealed a strong predictive value for baseline cholinesterase and bilirubin levels with a highly nonlinear influence for each parameter.
Our objective was to predict the outcome of 90Y radioembolization in patients with intrahepatic tumors from pretherapeutic baseline parameters and to identify predictive variables using a machine-learning approach based on random survival forests. 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, bilirubin, type of primary tumor, age at radioembolization, hepatic tumor burden, presence of extrahepatic disease, 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 mo (95% confidence interval, 9.7-14.2 mo), with 228 deaths occurring during the observation period. The random-survival-forest analysis identified baseline cholinesterase and bilirubin as the most important variables (forest-averaged lowest minimal depth, 1.2 and 1.5, respectively), 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 0.657, with an SE of 0.02, comparable to the concordance index of 0.652 and SE of 0.02 for a previously published Cox proportional hazards model. Conclusion: Random survival forests are a simple and straightforward machine-learning approach for prediction of overall survival. The predictive performance of the trained model was similar to a previously published Cox regression model. The model has revealed a strong predictive value for baseline cholinesterase and bilirubin levels with a highly nonlinear influence for each parameter.
Authors: Erito Marques de Souza Filho; Fernando de Amorim Fernandes; Christiane Wiefels; Lucas Nunes Dalbonio de Carvalho; Tadeu Francisco Dos Santos; Alair Augusto Sarmet M D Dos Santos; Evandro Tinoco Mesquita; Flávio Luiz Seixas; Benjamin J W Chow; Claudio Tinoco Mesquita; Ronaldo Altenburg Gismondi Journal: Front Cardiovasc Med Date: 2021-11-11