论著摘要 |【Radiomics-CT】基于CT放射学和模糊分类的放射治疗诱导腮腺收缩和毒性的早期预测(双语版)

2018-05-02 17:48:36 admin
标签:   影像组学 人工智能 腮腺 口腔干燥症 贝叶斯分类器

Early prediction of radiotherapy-induced parotid shrinkage and toxicity based on CT radiomics and fuzzy classification.

发表日期: 2017.09.01   来源:Artif Intell Med. 2017 Sep;81:41-53.

作者:

Pota M1, Scalco E2, Sanguineti G3, Farneti A3,, Cattaneo GM4 , Rizzo G2, Esposito M5.

作者介绍:

1. National Research Council of Italy - Institute for High Performance Computing and Networking (ICAR), Via P. Castellino 111, 80131 Naples, Italy. Electronic address: marco.pota@na.icar.cnr.it.

2. National Research Council of Italy - Institute of Molecular Bioimaging and Physiology (IBFM), Via F.lli Cervi 93, 20090 Segrate, MI, Italy.

3. Radiotherapy, Istituto Nazionale Tumori Regina Elena, Roma, Italy.

4. Medical Physics Department, San Raffaele Scientific Institute, Milano, Italy.

5. National Research Council of Italy - Institute for High Performance Computing and Networking (ICAR), Via P. Castellino 111, 80131 Naples, Italy.

摘要

Abstact

动机

头颈部癌症放疗患者往往患有长期口干症或腮腺萎缩。为了避免这些弊端,如果在治疗的早期阶段之前或期间能够及时获得预测信息,则可以为有风险的患者计划适应性的治疗方案。人工智能可以通过学习示例和建立分类模型来解决问题。特别是模糊逻辑已经显示其适用于医疗应用,它的功能是管理不确定的数据,并建立透明的基于规则的分类器。在以前的工作中,为了找到不同的可能预测腮腺萎缩的因素,临床、剂量测定和基于图像的特征被分开考虑。另一方面,一些研究报道了口腔干燥症的基于图像的预测因素,而不同类型的特征的组合却没有得到解决。

Motivation

Patients under radiotherapy for head-and-neck cancer often suffer of long-term xerostomia, and/or consistent shrinkage of parotid glands. In order to avoid these drawbacks, adaptive therapy can be planned for patients at risk, if the prediction is obtained timely, before or during the early phase of treatment. Artificial intelligence can address the problem, by learning from examples and building classification models. In particular, fuzzy logic has shown its suitability for medical applications, in order to manage uncertain data, and to build transparent rule-based classifiers. In previous works, clinical, dosimetric and image-based features were considered separately, to find different possible predictors of parotid shrinkage. On the other hand, a few works reported possible image-based predictors of xerostomia, while the combination of different types of features has been little addressed.

目标

本文提出了一种基于统计学和模糊逻辑的新型机器学习方法的应用,其目的在于对患有i)腮腺萎缩和ii)12个月口干症风险的患者进行分类。这两个问题的解决都是通过个体化预测器和模型来对各个结果进行分类为目标进行的。

Objective

This paper proposes the application of a novel machine learning approach, based on both statistics and fuzzy logic, aimed at the classification of patients at risk of i) parotid gland shrinkage and ii) 12-months xerostomia. Both problems are addressed with the aim of individuating predictors and models to classify respective outcomes.

方法

根据基于模糊规则模型的统计信息的表征,通过最近开发的名为Likelihood-Fuzzy Analysis的方法,从放射治疗患者的真实数据集中提取信息。该方法能够管理异构变量和缺失数据,并获得具有良好泛化能力(因此具有高性能)的可解释的模糊模型,同时可以测量分类置信度。许多特征被提取以表征来自不同来源的患者,即通过计算机断层摄影图像的纹理分析获得的临床特征,剂量测定参数和基于放射组学的参数。基于简单模型组合的学习方法可以更加复杂地考虑这些特征,以便识别只有一些数据源可用时的预测变量和模型,并且当可以组合更多的信息时获得更准确的结果。

Methods

Knowledge is extracted from a real dataset of radiotherapy patients, by means of a recently developed method named Likelihood-Fuzzy Analysis, based on the representation of statistical information by fuzzy rule-based models. This method enables to manage heterogeneous variables and missing data, and to obtain interpretable fuzzy models presenting good generalization power (thus high performance), and to measure classification confidence. Numerous features are extracted to characterize patients, coming from different sources, i.e. clinical features, dosimetric parameters, and radiomics-based measures obtained by texture analysis of Computed Tomography images. A learning approach based on the composition of simple models in a more complicated one allows to consider the features separately, in order to identify predictors and models to use when only some data source is available, and obtaining more accurate results when more information can be combined.

结果

关于腮腺缩小,一些好的预测器已经被发现,其中一部分是已知的并已获得证实,还有一些被发现的,特别是基于放射组学中的特征。我们还设计了许多模型,其中一些模型使用单一特征,另一些包括了模型组合以提高分类准确性。特别地,在最初治疗阶段使用的最佳模型,以及在半治疗阶段适用的另一个模型也被确定。关于12个月的毒性,一些可能的预测器也被检测到,特别是在基于放射组学的特征中的一些预测器。而且最终腮腺收缩率与12个月口干症之间的关系也得到评估。将该方法与朴素贝叶斯分类器进行比较,该分类器在分类准确性和最佳预测器方面显示出类似的结果。明确提出可解释的基于模糊规则的模型,解释预测因子与结果之间的依赖关系,从而在某些情况下提供有关所考虑问题的有用见解。

Results

Regarding parotid shrinkage, a number of good predictors is detected, some already known and confirmed here, and some others found here, in particular among radiomics-based features. A number of models are also designed, some using single features and others involving models composition to improve classification accuracy. In particular, the best model to be used at the initial treatment stage, and another one applicable at the half treatment stage are identified. Regarding 12-months toxicity, some possible predictors are detected, in particular among radiomics-based features. Moreover, the relation between final parotid shrinkage rate and 12-months xerostomia is evaluated. The method is compared to the naïve Bayes classifier, which reveals similar results in terms of classification accuracy and best predictors. The interpretable fuzzy rule-based models are explicitly presented, and the dependence between predictors and outcome is explained, thus furnishing in some cases helpful insights about the considered problems.

结论

由于所使用的模糊分类方法的性能和可解释性,得已检测了腮腺萎缩和口干症的预测器,并揭示了它们对每个结果的影响。此外,预测初始和半放疗阶段腮腺萎缩的模型被发现。

Conclusions

Thanks to the performance and interpretability of the fuzzy classification method employed, predictors of both parotid shrinkage and xerostomia are detected, and their influence on each outcome is revealed. Moreover, models for predicting parotid shrinkage at initial and half radiotherapy stages are found.

关键词:

分类;模糊逻辑;腮腺;放射组学;基于规则的系统;口腔干燥症

Keywords:

Classification; Fuzzy logic; Parotid gland; Radiomics; Rule-based systems; Xerostomia

阅读原文:PMID: 28325604  DOI: 10.1016/j.artmed.2017.03.004


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