Enhanced CT Based Texture Analysis and Machine Learning for Differentiation between Adenolymphoma and Mixed Tumors of the Parotid Gland
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摘要: 目的：探究基于增强CT纹理分析技术联合机器学习在腮腺腺淋巴瘤与混合瘤鉴别中的应用。方法：回顾性分析40例于本院手术并有完整病理资料的腮腺腺淋巴瘤与混合瘤患者，其中腺淋巴瘤组21例，混合瘤组19例。运用Mazda软件在增强CT静脉期图像上手动勾画病灶最大层面ROI区；应用Fisher系数、POE+ACC、MI及三者联合应用（FPM）的方法，筛选出最佳纹理参数，通过ROC曲线评估其诊断效能；最后采用RDA、PCA和LDA、NDA四种机器学习算法进行分类分析，并分析不同算法的诊断效能。结果：纹理特征参数中腺淋巴瘤组的WavEnHH_s-4、GrVariance、45dgr_Fraction低于混合瘤组，WavEnLL_s-4、GrSkewness高于混合瘤组，且均在组间有统计学意义。ROC曲线显示WavEnLL_s-4的敏感性与特异性较为平衡，AUC值、敏感性、特异性分别为0.797、84.2％、76.2％，具有良好诊断效能；RDA、PCA、LDA、NDA算法的误判率范围分别为30.0％～37.5％、30.0％～37.5％、7.5％～37.5％、5.0％～12.5％，其中误判率最低的是FPM联合NDA分类分析法，为5.0％；准确率、敏感性、特异性、阳性预测值、阴性预测值分别为95.0％、95.2％、94.7％、95.2％和94.7％，分类效能最佳。结论：增强CT纹理分析提取的最佳特征参数在腮腺腺淋巴瘤与混合瘤间具有显著差异，FPM联合NDA分类分析法误判率最低，有助于鉴别腮腺腺淋巴瘤与混合瘤。Abstract: Objective: To explore the application of enhanced computed tomography (CT)-based texture analysis combined with machine learning in the differential diagnosis of adenolymphomas and mixed tumors of the parotid gland. Methods: We retrospectively analyzed 21 and 19 cases of adenolymphomas and mixed tumors of the parotid gland, respectively. Regions of interest (ROI) were chosen on axial enhanced-CT images of the tumor’s maximum cross section using the Mazda software. The optimal texture parameters were selected using Fisher’s coefficient, probability of classification error and average correlation coefficients, mutual information, and a combination of the three. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic efficacy. Finally, the texture parameters were classified and analyzed using the following four machine-learning methods: raw data analysis, principal component analysis, linear discriminant analysis, and nonlinear discriminant analysis (NDA). The diagnostic efficiencies of these classification algorithms were analyzed. Results: WavEnHH_s-4, GrVariance, 45dgr_Fraction, WavEnLL_s-4, and GrSkewness were the statistically significant texture feature parameters for differentiating between parotid adenolymphomas and mixed tumors.. ROC curve analysis revealed that WavEnLL_s-4 had a balanced sensitivity and specificity, and the area under the curve, sensitivity, and specificity were 0.797, 84.2%, and 62.5%, respectively. The misclassification rate of NDA (5.0%–12.5%) was lower than that of the other algorithms. The NDA of FPM had the lowest misclassification rate (5.0%); its accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 95.0%, 95.2%, 94.7%, 95.2, and 94.7, respectively. Conclusion: The optimum enhanced CT-based texture features differed significantly between parotid adenolymphomas and mixed tumors. A combination of FPM and NDA had the lowest misclassification rate; it can contribute toward the identification of parotid adenolymphomas and mixed tumors.
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Key words:
- enhanced CT /
- machine learning /
- texture analysis /
- parotid tumor
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图 1 腮腺肿瘤CT静脉期图像以及ROIs
（a）和（b）腺淋巴瘤，男，45岁；（c）和（d）多形性腺瘤，男，40岁。
Figure 1. Enhanced CT images (with ROIs) of a parotid tumor
图 2 腮腺腺淋巴瘤与混合瘤组间WavEnHH_s-4、WavEnLL_s-4、GrVariance、GrSkewness、45 dgr_Fraction的ROC曲线
Figure 2. ROC curves for WavEnHH_s-4, WavEnLL_s-4, GrVariance, GrSkewness, 45 dgr_Fraction for differentiating between adenolymphomas and mixed tumors of the parotid gland
表 1 腮腺腺淋巴瘤与混合瘤间最佳纹理特征参数比较
Table 1. Comparison of the optimal texture feature parameters between parotid adenolymphomas and mixed tumors
参数 组别 统计检验 腺淋巴瘤组 混合瘤组 t/Z P WavEnHH_s-4 4.162±1.908 7.493±3.157 -4.084 ＜0.01 WavEnLL_s-4 21044.469±3887.164 16649.289±4309.226 3.392 0.002 GrVariance 0.185±0.046 0.236±0.033 -4.055 ＜0.01 GrSkewness 1.996±0.516 1.475±0.295 -3.291 0.001 45 dgr_Fraction 0.328±0.080 0.422±0.074 -3.854 ＜0.01 
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表 2 腮腺腺淋巴瘤与混合瘤间最佳纹理特征参数的诊断效能
Table 2. Diagnostic performance of the optimal texture feature parameters for parotid adenolymphomas and mixed tumors
参数 AUC 阈值 敏感性/％ 特异性/％ P WavEnHH_s-4 0.827 4.979 84.2 66.7 ＜0.01 WavEnLL_s-4 0.797 19227.148 84.2 76.2 0.001 GrVariance 0.815 0.200 89.5 66.7 0.001 GrSkewness 0.805 1.819 94.7 61.9 0.001 45 dgr_Fraction 0.802 0.384 73.7 71.4 0.001
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表 3 腮腺腺淋巴瘤与混合瘤间不同机器学习算法的误判率
Table 3. False-positive rates of different machine-learning algorithms for parotid adenolymphomas and mixed tumors
组别 RDA/％ PCA/％ LDA/％ NDA/％ Fisher 37.5（15/40） 37.5（15/40） 10.0（4/40） 7.5（3/40） POE+ACC 35.0（14/40） 30.0（12/40） 22.5（9/40） 10.0（4/40） MI 30.0（12/40） 30.0（12/40） 37.5（15/40） 12.5（5/40） FPM 35.0（14/40） 32.5（13/40） 7.5（3/40） 5.0（2/40）
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表 4 腮腺腺淋巴瘤与混合瘤间不同机器学习算法的效能比较
Table 4. Comparison of the performance of different machine-learning algorithms for parotid adenolymphomas and mixed tumors
分类算法 准确率/％ 敏感性/％ 特异性/％ 阳性预测值 阴性预测值 Fisher/RDA 62.5 61.9 63.2 65.0 60.0 Fisher/PCA 62.5 61.9 63.2 65.0 60.0 Fisher/LDA 90.0 95.2 84.2 87.0 94.1 Fisher/NDA 92.5 90.5 94.7 95.0 90.0 POE+ACC/RDA 65.0 76.2 52.6 64.0 66.7 POE+ACC/PCA 70.0 76.2 63.2 69.6 70.6 POE+ACC/LDA 77.5 76.2 78.9 80.0 75.0 POE+ACC/NDA 90.0 85.7 94.7 94.7 85.7 MI/RDA 70.0 76.2 63.2 69.6 70.6 MI/PCA 70.0 76.2 63.2 69.6 70.6 MI/LDA 62.5 66.7 57.9 63.6 61.1 MI/NDA 87.5 81.0 94.7 94.4 81.8 FPM/RDA 65.0 66.7 63.2 66.7 63.2 FPM/PCA 67.5 71.4 63.2 68.2 66.7 FPM/LDA 92.5 95.2 89.5 90.9 94.4 FPM/NDA 95.0 95.2 94.7 95.2 94.7
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