论著摘要 |【PET】选择FDG-PET图像的影像学特征进行癌症治疗结果预测(双语版)

2017-12-27 16:59:41 admin 0

Selecting radiomic features from FDG-PET images for cancer treatment outcome prediction.

发表日期: 2016.05.19   来源:Medical Image Analysis. 2016 Aug;32:257-68.

作者:

Lian C1, Ruan S2, Denœux T3, Jardin F4, Vera P5.

作者介绍:

1. Sorbonne Universités, Université de Technologie de Compiègne, CNRS, UMR 7253 Heudiasyc, France; Université de Rouen, QuantIF, EA 4108 LITIS, France.

2. Université de Rouen, QuantIF, EA 4108 LITIS, France. Electronic address: su.ruan@univ-rouen.fr.

3. Sorbonne Universités, Université de Technologie de Compiègne, CNRS, UMR 7253 Heudiasyc, France.

4. Université de Rouen, QuantIF, EA 4108 LITIS, France; Centre Henri-Becquerel, Department of Hematology, France.

5. Université de Rouen, QuantIF, EA 4108 LITIS, France; Centre Henri-Becquerel, Department of Nuclear Medicine, France.

摘要

作为癌症治疗中的重要任务,准确预测治疗结果对于定制和适应治疗计划是有价值的。为此,在治疗之前和治疗期间收集的多种信息(辐射度,临床特征,基因组表达等)潜在有利的。在本文中,我们提出了这样的预测系统,主要使用从FDG-PET图像提取的影像学特征(例如,纹理特征)。 所提出的系统包括基于Dempster-Shafer理论的特征选择方法,它是处理不确定和不精确信息的强大工具。 它旨在提高预测精度,并减少所选特征子空间中不同类别(治疗结果)之间的不精确和重叠。考虑到培训样本在我们的应用中通常是小尺寸和不平衡的,因此考虑到数据平衡过程和指定的先验知识,以提高所选特征子集的可靠性。 最后,使用证据K-NN(EK-NN)分类器选择特征来输出预测结果。我们的预测系统已经通过合成和临床数据集进行评估,持续显示出良好的性能。

Abstact

As a vital task in cancer therapy, accurately predicting the treatment outcome is valuable for tailoring and adapting a treatment planning . To this end, multi-sources of information (radiomics, clinical characteristics, genomic expressions, etc) gathered before and during treatment are potentially profitable . In this paper, we propose such a prediction system primarily using radiomic features (e.g., texture features) extracted from FDG-PET images . The proposed system includes a feature selection method based on Dempster-Shafer theory, a powerful tool to deal with uncertain and imprecise information . It aims to improve the prediction accuracy, and reduce the imprecision and overlaps between different classes (treatment outcomes) in a selected feature subspace . Considering that training samples are often small-sized and imbalanced in our applications, a data balancing procedure and specified prior knowledge are taken into account to improve the reliability of the selected feature subsets . Finally, the Evidential K-NN (EK-NN) classifier is used with selected features to output prediction results . Our prediction system has been evaluated by synthetic and clinical datasets, consistently showing good performance.

关键词:

癌症,Dempster-Shafer理论,特征选择,不平衡学习,结果预测,PET图像

Keywords:

Cancer; Dempster–Shafer theory; Feature selection; Imbalanced learning; Outcome prediction; PET images

阅读原文:PMID: 27236221  DOI: 10.1016/j.media.2016.05.007


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