论著摘要 |【CT】基于影像组学学的非小细胞肺癌预后分析(双语版)

2017-12-19 10:47:01 admin 5

Radiomics-based Prognosis Analysis for Non-Small Cell Lung Cancer.

发表日期: 2017.04.18   来源Scientific Reports. 2017;7: 46349.

作者

Yucheng Zhang1, Anastasia Oikonomou1, Alexander Wong2, Masoom A. Haider1, and Farzad Khalvatia1

作者介绍: ,

1. Dept. of Medical Imaging, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.

2. Dept. of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada.

摘要

影像组学通过从放射图像中提取大量的定量特征来表征肿瘤表型。放射学特征在几项研究中表现出预测临床结果的预后价值。然而,包括特征冗余,数据不平衡和样本量小在内的几个挑战导致相对较低的预测精度。在这项研究中,我们探讨了克服这些挑战的不同策略,并改善了非小细胞肺癌(NSCLC)中基于影像组学的预后预测性能。采用综合影像组学分析法,对112例接受立体定向放射治疗的非小细胞肺癌患者(平均年龄75岁)的CT图像对复发,死亡,无复发生存等指标进行预测。使用不同的特征选择和预测建模技术来确定预后分析的最佳配置。为了解决特征冗余问题,综合分析表明,随机森林模型和主成分分析分别是实现高预后性能的最佳预测模型和特征选择方法。为了解决数据不平衡问题,发现少量样本合成过采样技术能显著提高预测精度。全面的方差分析显示,数据端点,特征选择技术和分类器是影响预测准确性的重要因素,表明在构建基于影像组学的癌症预后预测模型时,必须对这些因素进行研究。

Abstract

Radiomics characterizes tumor phenotypes by extracting large numbers of quantitative features from radiological images. Radiomic features have been shown to provide prognostic value in predicting clinical outcomes in several studies. However, several challenges including feature redundancy, unbalanced data, and small sample sizes have led to relatively low predictive accuracy. In this study, we explore different strategies for overcoming these challenges and improving predictive performance of radiomics-based prognosis for non-small cell lung cancer (NSCLC). CT images of 112 patients (mean age 75 years) with NSCLC who underwent stereotactic body radiotherapy were used to predict recurrence, death, and recurrence-free survival using a comprehensive radiomics analysis. Different feature selection and predictive modeling techniques were used to determine the optimal configuration of prognosis analysis. To address feature redundancy, comprehensive analysis indicated that Random Forest models and Principal Component Analysis were optimum predictive modeling and feature selection methods, respectively, for achieving high prognosis performance. To address unbalanced data, Synthetic Minority Over-sampling technique was found to significantly increase predictive accuracy. A full analysis of variance showed that data endpoints, feature selection techniques, and classifiers were significant factors in affecting predictive accuracy, suggesting that these factors must be investigated when building radiomics-based predictive models for cancer prognosis.

阅读原文:PMID: 28418006  PMCID: PMC5394465  DOI: 10.1038/srep46349


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