A Novel CT-Based Radiomics Features Analysis for Identification and Severity Staging of COPD

IF 3.9 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Zongli Li MS, Ligong Liu PhD, Zuoqing Zhang MS, Xuhong Yang MS, Xuanyi Li BS, Yanli Gao MD, Kewu Huang MD
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引用次数: 9

Abstract

Rationale and Objectives

To evaluate the role of radiomics based on Chest Computed Tomography (CT) in the identification and severity staging of chronic obstructive pulmonary disease (COPD).

Materials and Methods

This retrospective analysis included 322 participants (249 COPD patients and 73 control subjects). In total, 1395 chest CT-based radiomics features were extracted from each participant's CT images. Three feature selection methods, including variance threshold, Select K Best method, and least absolute shrinkage and selection operator (LASSO), and two classification methods, including support vector machine (SVM) and logistic regression (LR), were used as identification and severity classification of COPD. Performance was compared by AUC, accuracy, sensitivity, specificity, precision, and F1-score.

Results

38 and 10 features were selected to construct radiomics models to detect and stage COPD, respectively. For COPD identification, SVM classifier achieved AUCs of 0.992 and 0.970, while LR classifier achieved AUCs of 0.993 and 0.972 in the training set and test set, respectively. For the severity staging of COPD, the mentioned two machine learning classifiers can better differentiate less severity (GOLD1 + GOLD2) group from greater severity (GOLD3 + GOLD4) group. The AUCs of SVM and LR is 0.907 and 0.903 in the training set, and that of 0.799 and 0.797 in the test set.

Conclusion

The present study showed that the novel radiomics approach based on chest CT images that can be used for COPD identification and severity classification, and the constructed radiomics model demonstrated acceptable performance.

一种新的基于ct的COPD诊断和严重程度分期的放射组学特征分析
理由与目的评价基于胸部计算机断层扫描(CT)的放射组学在慢性阻塞性肺疾病(COPD)的识别和严重程度分期中的作用。材料和方法本研究纳入322名参与者(249名COPD患者和73名对照组)。总共从每位参与者的CT图像中提取了1395个基于胸部CT的放射组学特征。采用方差阈值法(variance threshold)、Select K Best法(Select K Best method)、最小绝对收缩和选择算子(least absolute contraction and selection operator, LASSO) 3种特征选择方法和支持向量机(support vector machine, SVM)、logistic回归(logistic regression, LR) 2种分类方法对COPD进行识别和严重程度分类。通过AUC、准确性、敏感性、特异性、精密度和f1评分进行比较。结果分别选择38个和10个特征构建COPD的放射组学模型来检测和分期COPD。对于COPD识别,SVM分类器在训练集和测试集的auc分别达到0.992和0.970,LR分类器的auc分别达到0.993和0.972。对于COPD的严重程度分期,上述两种机器学习分类器可以更好地区分轻度(GOLD1 + GOLD2)组和重度(GOLD3 + GOLD4)组。SVM和LR在训练集中的auc分别为0.907和0.903,在测试集中的auc分别为0.799和0.797。结论基于胸部CT图像的新型放射组学方法可用于COPD的识别和严重程度分级,构建的放射组学模型具有良好的性能。
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来源期刊
Academic Radiology
Academic Radiology 医学-核医学
CiteScore
7.60
自引率
10.40%
发文量
432
审稿时长
18 days
期刊介绍: Academic Radiology publishes original reports of clinical and laboratory investigations in diagnostic imaging, the diagnostic use of radioactive isotopes, computed tomography, positron emission tomography, magnetic resonance imaging, ultrasound, digital subtraction angiography, image-guided interventions and related techniques. It also includes brief technical reports describing original observations, techniques, and instrumental developments; state-of-the-art reports on clinical issues, new technology and other topics of current medical importance; meta-analyses; scientific studies and opinions on radiologic education; and letters to the Editor.
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