| 黄栎有.CT平扫图像纹理分析鉴别浸润性肺腺癌与非钙化结核球[J].中国医学影像技术,2020,36(4): |
| CT平扫图像纹理分析鉴别浸润性肺腺癌与非钙化结核球 |
| Identification of invasive lung adenocarcinoma and non-calcified lung tuberculoma on plain CT images based on texture analysis |
| 投稿时间：2019-08-21 修订日期：2020-04-22 |
| DOI： |
| 中文关键词: 肺肿瘤；诊断 人工智能；体层摄影术，X线计算机；纹理分析；影像组学 |
| 英文关键词:lung neoplasms diagnosis artificial intelligence tomography, X-ray computed texture analysis radiomics |
| 基金项目:无 |
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| 摘要点击次数: 2317 |
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| 中文摘要: |
| 目的 探讨采用CT平扫图像纹理分析的方法对浸润性肺腺癌和非钙化结核球进行鉴别诊断的可行性。方法 回顾性分析52例经病理证实的单发肺结节，其中浸润性肺腺癌 31例，非钙化结核球21例。通过MaZda软件从52例结节中提取纹理特征300个。分别采用费希尔参数法( Fisher )、最小分类误差与最小平均相关系数法( POE＋ACC )、相关信息测度法( MI )及三者联合( MPF )筛选出最佳纹理特征参数。使用B11模块提供的线性判别分析( LDA )和非线性判别分析( NDA )进行纹理分类，计算出鉴别浸润性肺腺癌和非钙化结核球的最小错误率。并通过Mann-Whitney U检验每个用于鉴别的纹理特征，对具有显著差异的纹理特征建立ROC曲线，计算曲线下面积AUC，评价纹理诊断效能。结果 NDA/ANN-Fisher法错误率最低为7.69%(4/52)，三者联合NDA/ANN-MPF最低错误率5.77（3/52）。30个纹理特征中对两种病变存在明显差异的有11个,其中差异熵相关纹理特征5个。差异熵S(1,1)、差方差S(1,1)以及梯度方差具有良好的诊断效能（AUC>0.7）。结论 基于CT平扫的纹理分析鉴别浸润性肺腺癌和非钙化结核球性具有一定价值。 |
| 英文摘要: |
| [Abstract] Objective To investigate the feasibility of differential diagnosis of invasive lung adenocarcinoma and non-calcified tuberculosis using CT plain image texture analysis. Methods A retrospective analysis of 52 cases of pathologically confirmed single pulmonary nodules, including invasive lung adenocarcinoma in 31 cases, non-calcified tuberculosis in 21 cases. 300 texture features were extracted from 52 nodules by MaZda software. The optimized texture parameters of texture analysis were selected with fisher coefficient (Fisher), probability of classification error and average correction coefficient minimization of both classification error probability and average correlation coefficients (POE+ACC) , mutual information coefficients (MI) as well as combination of the above three methods (MPF) , respectively. The texture characteristics were analyzed by using linear discriminant analysis (LDA) and nonlinear discriminant analysis (NDA) provided by B11 module in the Mazda software, the minimum error probability of differential diagnosis of invasive lung adenocarcinoma and non-calcified tuberculosis. The texture features for identification were examined by Mann-Whitney U, the ROC curves were established for the texture features with significant differences, the area under the curve AUC was calculated, and the texture diagnostic performance was evaluated. Results The NDA/ANN-Fihser method had a minimum error rate of 7.69% (4/52), and the lowest error rate of the three combined with NDA/ANN-MPF was 5.77 (3/52). Among the 30 texture features, there were 11 significant difference entropy-related texture features for the two lesions. The difference entropy S(1,1) difference variance S(1,1) and gradient variance have good diagnostic performance (AUC>0.7). Conclusion Based on CT plain scan texture analysis, it is valuable to identify invasive lung adenocarcinoma and non-calcified tuberculosis. |
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