English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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

computer methods and programs in biomedicine

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