  Determining the most informative features for predicting the overall survival
of patients diagnosed with high-grade gastroenteropancreatic neuroendocrine
neoplasms is crucial to improve individual treatment plans for patients, as
well as the biological understanding of the disease. Recently developed
ensemble feature selectors like the Repeated Elastic Net Technique for Feature
Selection (RENT) and the User-Guided Bayesian Framework for Feature Selection
(UBayFS) allow the user to identify such features in datasets with low sample
sizes. While RENT is purely data-driven, UBayFS is capable of integrating
expert knowledge a priori in the feature selection process. In this work we
compare both feature selectors on a dataset comprising of 63 patients and 134
features from multiple sources, including basic patient characteristics,
baseline blood values, tumor histology, imaging, and treatment information. Our
experiments involve data-driven and expert-driven setups, as well as
combinations of both. We use findings from clinical literature as a source of
expert knowledge. Our results demonstrate that both feature selectors allow
accurate predictions, and that expert knowledge has a stabilizing effect on the
feature set, while the impact on predictive performance is limited. The
features WHO Performance Status, Albumin, Platelets, Ki-67, Tumor Morphology,
Total MTV, Total TLG, and SUVmax are the most stable and predictive features in
our study.
