| 基于增强CT纹理分析联合机器学习鉴别腮腺腺淋巴瘤与混合瘤 |
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| 引用本文： | 茅枭骁,马树声,卢亮,施久刚,张磊.基于增强CT纹理分析联合机器学习鉴别腮腺腺淋巴瘤与混合瘤[J].CT理论与应用研究,2023,32(1):74-80. |
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| 作者姓名： | 茅枭骁 马树声 卢亮 施久刚 张磊 |
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| 作者单位： | 靖江市人民医院影像科, 江苏 靖江214500 |
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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...
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| 关 键 词： | 增强CT 机器学习 纹理分析 腮腺肿瘤 |
| 收稿时间： | 2022-02-22 |
Enhanced CT Based Texture Analysis and Machine Learning for Differentiation between Adenolymphoma and Mixed Tumors of the Parotid Gland |
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| Affiliation: | Department of Imaging, Jingjiang People's Hospital, Jingjiang 214500, China |
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| 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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