目的:确定用于预测诊断为高度胃肠胰腺神经内分泌肿瘤的患者的总体生存率的最有用的特征对于改善患者的个体化治疗计划至关重要。以及对这种疾病的生物学理解。本研究的主要目的是评估现代集成特征选择技术的使用,以(a)定量性能测量,如预测性能,(b)临床可解释性,和(C)整合先验专家知识的效果。
方法:用于特征选择的重复弹性网络技术(RENT)和用于特征选择的用户引导贝叶斯框架(UBayFS)是最近开发的集成特征选择器。两者都允许用户识别具有低样本量的数据集中的信息特征,并专注于模型可解释性。虽然RENT是纯粹的数据驱动,UBayFS可以在特征选择过程中先验地集成专家知识。在这项工作中,我们比较了包含63名患者和110个来自多个来源的特征的数据集上的两个特征选择器,包括基线患者特征,基线血液值,肿瘤组织学,成像,和治疗信息。
结果:我们的实验涉及数据驱动和专家驱动的设置,以及两者的组合。在没有专业知识的情况下进行了五次交叉验证的实验,我们的结果表明,这两个特征选择器都可以进行准确的预测:从110个特征减少到大约20个特征(约82%)提供了接近最佳的预测性能,但根据特征选择器的选择,变化很小。预测模型,和折叠。此后,我们使用来自临床文献的发现作为专家知识的来源.此外,专家知识对特征集具有稳定作用(稳定性增加约40%),而对预测性能的影响有限。
结论:特征世卫组织绩效状况,白蛋白,血小板,Ki-67,肿瘤形态学,TotalMTV,TotalTLG,和SUVmax是我们研究中最稳定和预测的特征。总的来说,这项研究证明了特征选择在医学应用中的实用价值,不仅可以提高定量性能,还可以为专家提供潜在的新见解。
OBJECTIVE: 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. The main objective of this study is to evaluate the use of modern ensemble feature selection techniques for this purpose with respect to (a) quantitative performance measures such as predictive performance, (b) clinical interpretability, and (c) the effect of integrating prior expert knowledge.
METHODS: The Repeated Elastic Net Technique for Feature Selection (RENT) and the User-Guided Bayesian Framework for Feature Selection (UBayFS) are recently developed ensemble feature selectors investigated in this work. Both allow the user to identify informative features in datasets with low sample sizes and focus on model interpretability. While RENT is purely data-driven, UBayFS can integrate expert knowledge a priori in the feature selection process. In this work, we compare both feature selectors on a dataset comprising 63 patients and 110 features from multiple sources, including baseline patient characteristics, baseline blood values, tumor histology, imaging, and treatment information.
RESULTS: Our experiments involve data-driven and expert-driven setups, as well as combinations of both. In a five-fold cross-validated experiment without expert knowledge, our results demonstrate that both feature selectors allow accurate predictions: A reduction from 110 to approximately 20 features (around 82%) delivers near-optimal predictive performances with minor variations according to the choice of the feature selector, the predictive model, and the fold. Thereafter, we use findings from clinical literature as a source of expert knowledge. In addition, expert knowledge has a stabilizing effect on the feature set (an increase in stability of approximately 40%), while the impact on predictive performance is limited.
CONCLUSIONS: 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. Overall, this study demonstrated the practical value of feature selection in medical applications not only to improve quantitative performance but also to deliver potentially new insights to experts.