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.

该研究比较了使用 REPT 和 UBayFS 这两种特征选择算法，基于临床文献的专家知识和数据驱动的实验来确定最稳定和预测的高等级肠胃胰神经内分泌肿瘤病人特征集，并发现对特征进行专家知识的引导能够提高特征集的稳定性，但对预测性能的影响有限。

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

高级胃肠神经内分泌肿瘤患者生存情况的探究：集成特征选择在总生存预测中的应用