Predicting treatment outcome in metastatic melanoma through automated multi-objective model with hyperparameter optimization

In recent years, the immunotherapy through immunocheckpoint inhibitors significantly improves the survival rate and reduce recurrence risk in metastatic melanoma. Moreover, accurately predicting immunotherapy response is of great importance to improve treatment effectiveness. We are aiming to develop a new automated multi-objective model with hyperparameter optimization (AutoMO-HO) for improving treatment outcome prediction performance. Delta-radiomic features which calculates the difference between pre- and post-treatment radiomic features were used in this study. To obtain balanced sensitivity and specificity as well as higher confidence output, an automated multi-objective model (AutoMO) is applied. However, there are several hyperparameters to be set manually before training, leading to the nonoptimal model performance. As such, Bayesian optimization is introduced to train the model hyperparameter, and a new model termed as AutoMO-HO is developed based on AutoMO. In AutoMO-HO, the training stage consists of two phases, they are Bayesian hyperparameter optimization through the Tree Parzen estimator algorithm and Pareto-optimal model set generation. In testing stage, the evidential reasoning (ER) strategy is used to fuse the output of each Paretooptimal model to obtain more reliable results. Finally, the label with the maximal output confidence is taken as final output label. The experimental results demonstrated that AutoMO-HO outperformed AutoMO and other available methods.