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.


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


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.






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.




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.


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。


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.




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.




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.




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

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