Towards Understanding the Survival of Patients with High-Grade Gastroenteropancreatic Neuroendocrine Neoplasms: An Investigation of Ensemble Feature Selection in the Prediction of Overall Survival

Summary

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.

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DOI : doi.org/10.48550/arXiv.2302.10...
NVA : hdl.handle.net/11250/3063933