| MRI图像纹理分析在腮腺良、恶性肿瘤鉴别诊断中的价值 |
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| 引用本文： | 瞿俊晨,贾传海,丁庆国,曹锐,丘佳明. MRI图像纹理分析在腮腺良、恶性肿瘤鉴别诊断中的价值[J]. 中国口腔颌面外科杂志, 2022, 20(2): 173-176. DOI: 10.19438/j.cjoms.2022.02.013 |
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| 作者姓名： | 瞿俊晨 贾传海 丁庆国 曹锐 丘佳明 |
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| 作者单位： | 1.常熟市第二人民医院 影像中心,2.口腔科,3.病理科,江苏 常熟 215500 |
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| 摘 要： | 目的：探讨基于MRI图像纹理分析在腮腺良、恶性肿瘤鉴别诊断中的价值。方法：回顾性分析常熟市第二人民医院2015—2019年经手术病理证实的腮腺良、恶性肿瘤131例，均进行常规MR平扫及增强扫描、DWI扫描。采用MaZda软件在选定的病变最大层面图像上绘制感兴趣区（ROI），得到各个纹理特征参数值。利用MaZda软件自带的B11统计软件模块，根据所选择的纹理特征分类两类肿瘤，最后输出T1WI、T2WI、T1WI增强、DWI 4个序列区分腮腺良、恶性肿瘤的结果，以错判率(MCR)形式表述。结果：基于T2WI图像的纹理分析鉴别能力明显优于基于其他3个序列图像的鉴别能力。在原始数据分析（RDA）、主要成分分析（PCA）、线性分类分析（LDA）和非线性分类分析（NDA）4种分类方法中，NDA方法鉴别腮腺良、恶性肿瘤的错判率显著低于其他方法。结论 ：基于T2WI图像的纹理分析可作为辅助手段，为临床上鉴别腮腺良、恶性肿瘤提供可靠的客观依据，提高诊断的准确性。在鉴别腮腺良、恶性肿瘤时，非线性分类(NDA)比线性分类(RDA、PCA、LDA)更有效，但临床应用时应注意到其推广能力及样本依赖性的特点。
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| 关 键 词： | 腮腺肿瘤 磁共振成像 纹理分析 |
| 收稿时间： | 2021-04-26 |
| 修稿时间： | 2021-07-22 |
Evaluation of MRI image texture analysis in differential diagnosis of benign,malignant parotid gland tumors |
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| QU Jun-chen,JIA Chuan-hai,DING Qing-guo,CAO Rui,QIU Jia-ming. Evaluation of MRI image texture analysis in differential diagnosis of benign,malignant parotid gland tumors[J]. China Journal of Oral and Maxillofacial Surgery, 2022, 20(2): 173-176. DOI: 10.19438/j.cjoms.2022.02.013 |
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| Authors: | QU Jun-chen JIA Chuan-hai DING Qing-guo CAO Rui QIU Jia-ming |
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| Affiliation: | 1. Imaging Center,2. Department of Stomatology,3. Department of Pathology, Changshu No.2 People's Hospital. Changshu 215500, Jiangsu Province, China |
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| Abstract: | PURPOSE:This study was intended to use MaZda software to explore image texture analysis value of T1WI, T2WI, T1WI enhancement, DWI map in differential diagnosis of benign and malignant parotid gland tumors. METHODS: A total of 131 cases undergoing MRI examination and surgery during 2015 to 2019 were included. A radiologist used MaZda software to draw ROIs from the selected images which contained maximal layer of lesions to obtain individual texture feature values. B11 statistical software module from MaZda software was used to classify benign and malignant parotid gland tumors according to the selected texture features. RESULTS: The discriminant ability of texture analysis based on T2WI maps was better than that based on the other three sequence images. In raw data analysis(RDA), principal component analysis(PCA), linear discriminant analysis(LDA) and nonlinear discriminant analysis(NDA) of the four classification methods were carried out. the misclassification rate of NDA method to identify benign and malignant parotid gland tumor was significantly lower than other kinds of methods. CONCLUSION: Texture analysis based on T2WI maps can be used as an auxiliary method to provide reliable and objective evidence for differential diagnosis of benign and malignant parotid gland tumors. NDA is more effective than RDA,PCA,LDA in differential diagnosis. Attention should be paid to its generalization ability and sample-dependent characteristics in clinical application. |
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| Keywords: | Benign and malignant parotid gland tumors Magnetic resonance imaging Texture analysis |
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