Radiomics models in differentiating parotid pleomorphic adenoma from Warthin tumor

WANG Qin; HUANG Dongqiong; YU Dongyang; HAN Lei

doi:10.19732/j.cnki.2096-6210.2021.06.012

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Radiomics models in differentiating parotid pleomorphic adenoma from Warthin tumor

更新时间：2025-12-15

- Radiomics models in differentiating parotid pleomorphic adenoma from Warthin tumor
- Vol. 30, Issue 6, Pages: 499-503(2021)
- 作者机构：
  
  1. 达州市中心医院核医学科,四川,达州,635000
  2. 徐州医科大学附属淮安医院影像科,江苏,淮安,223001
- 作者简介：
- 基金信息：
- DOI：10.19732/j.cnki.2096-6210.2021.06.012
  CLC：
- Published Online：28 December 2021，
  
  Published：28 December 2021
- 稿件说明：
移动端阅览
王琴,黄东琼,于冬洋,等. 放射组学模型鉴别腮腺多形性腺瘤与沃辛瘤[J]. 肿瘤影像学, 2021, 30(6): 499-503 https://doi.

org/10.19732/j.cnki.2096-6210.2021.06.012
王琴,黄东琼,于冬洋,等. 放射组学模型鉴别腮腺多形性腺瘤与沃辛瘤[J]. 肿瘤影像学, 2021, 30(6): 499-503 https://doi. DOI： 10.19732/j.cnki.2096-6210.2021.06.012.

org/10.19732/j.cnki.2096-6210.2021.06.012 DOI：

摘要

目的：

探讨基于计算机体层成像（computed tomography，CT）的放射组学模型在腮腺多形性腺瘤（pleomorphic adenoma，PA）与沃辛瘤（Warthin tumor，WT）鉴别中的价值。

方法：

收集经病理学检查证实的28例PA与25例WT，使用MaZda软件提取CT平扫图像中肿瘤的直方图分析（histogram analysis，HA）、灰度共生矩阵（gray-level co-occurrence matrix，GLCM）、灰度游程矩阵（gray-level run length matrix，GLRLM）、绝对梯度（absolute gradient，AG）和自回归模型（autoregressive model，AR）5种放射组学特征。使用组内相关系数（intraclass correlation coefficient，ICC）分析组学特征，选取PA与WT组中ICC＞0.75的特征，然后选取组间差异有统计学意义的特征参数，再使用最小绝对收缩和选择算子（least absolute shrinkage and selection operator，LASSO）回归分析进一步析筛选，最后以筛选出的特征参数建立随机森林（random forest，RF）、逻辑回归（logistic regression，LR）与支持向量机（support vector machine，SVM）分类模型，并应用受试者工作特征（receiver operating characteristic，ROC）曲线评估模型的效能。

结果：

PA与WT之间差异有统计学意义的特征中，GLCM数量最多。最终筛选出11个特征参数作为子集建立RF、LR、SVM共3个模型，其中RF的效能最佳，准确度、灵敏度、特异度及曲线下面积（area under curve，AUC）分别为83.3%、78.6%、88.0%及0.882。

结论：

基于CT的放射组学模型具有良好的分类效能，可于术前有效地鉴别PA与WT。

Abstract

Objective:

To investigate the value of radiomics models based on computed tomography (CT) in differentiating parotid pleomorphic adenoma (PA) from Warthin tumor (WT).

Methods:

A total of 28 patients with PA and 25 patients with WT

which were confirmed by pathology

were collected. MaZda was used to extract 5 kinds of radiomics features

including histogram analysis (HA)

gray-level co-occurrence matrix (GLCM)

gray-level run length matrix (GLRLM)

absolute gradient (AG) and autoregressive model (AR)

of tumors in plain CT scan images. Intraclass correlation coefficient (ICC) was used to analyze the radiomics

the characteristics with ICC＞0.75 in PA and WT groups were selected. Then

the feature parameters with statistically significant difference between the two groups were selected and further screened by least absolute shrinkage and selection operator (LASSO) regression analysis. Random forest (RF)

logistic regression (LR) and support vector machine (SVM) classifier models were established

using the feature parameters which were finally selected

and their effectiveness were evaluated using the receiver operating characteristic (ROC) curve.

Results:

Among the features with statistically significant difference between PA and WT

the number of GLCM was the largest. Finally

11 feature parameters were selected as subset to establish three models of RF

LR and SVM

among which RF had the best performance

corresponding accuracy

sensitivity

specificity and area under curve (AUC) were 83.3%

78.6%

88.0% and 0.882

respectively.

Conclusion:

Radiomics models based on CT have good classification efficiency

which can distinguish PA and WT effectively before operation.

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