| 王斌杰,周彦汝,姜澳田.MRI纹理特征定量分析用于T2,3期直肠癌精准分期的初步研究[J].中国肿瘤,2020,29(7):554-560. |
| MRI纹理特征定量分析用于T2,3期直肠癌精准分期的初步研究 |
| Preliminary Study on Precise Staging of Rectal Cancer Stage T2，3 Based on Quantitative Analysis of MRI Texture Features |
| 中文关键词 修订日期：2020-02-22 |
| DOI：10.11735/j.issn.1004-0242.2020.07.A013 |
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| 中文关键词: 直肠癌 纹理特征分析 T分期 精准分期 磁共振成像 |
| 英文关键词:rectal cancer texture analysis T stage precise staging magnetic resonance imaging |
| 基金项目:2020年度河南省重点研发与推广专项（202102310087）；河南省医学科技攻关计划（201404026） |
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| 中文摘要: |
| 摘 要：［目的］ 探讨磁共振成像（magnetic resonance imaging，MRI）纹理特征定量分析用于鉴别T2，3期直肠癌异质性及精准分期的价值。［方法］ 回顾性分析15例经手术病理证实T2及T3期病例，且在术前两周内行直肠MRI高分辨率平扫，每个病例选取显示病变较为满意T2WI轴位图像的若干层面，其中T2期8例选取41层，T3期7例选取40层，在MaZda软件中勾画病变感兴趣区(region of interest，ROI)提取病变纹理特征，用该软件提供的纹理特征选择方法中的交互信息（mutual information，MI）、Fisher系数（Fisher coefficient，Fisher）、分类错误概率联合平均相关系数（classification error probability combined with average correlation coefficients，POE+ACC）3种方法联合（Fisher+POE+ACC+MI，FPM）对提取纹理进行降维处理得到30个纹理特征，然后用该软件提供的纹理特征分类分析方法非线性分类分析（nonlinear discriminant analysis，NDA）对81个样本进行分类分析。采用组间与组内的平方和比例(between-category to within-category sums of squares，BW)和决策曲线分析法（decision curve analysis，DCA）筛选出3个最优特征，并比较两者差异。利用二元Logistic回归及ROC曲线计算三者独立及联合的诊断效能。［结果］ 81层图像分类的准确性为92.6%，以T2期图像作为阴性组、T3期图像作为阳性组计算敏感性为93.0%，特异性为93.2%，漏诊率为7.0%，误诊率为6.8%。以BW及DCA方法筛选出两组最优特征相同，前3个最优特征为S(4，0) 平方和(sum of squares，SumOfSqs)、S(5，0)SumOfSqs、S(3，0)SumOfSqs，效能排序有差异，三者联合的敏感性和特异性分别为85.0%和70.4%，独立的敏感性和特异性分别为80.0%和62.9%，80.0%和62.9%，72.5%和60.3%。三者联合及独立的敏感性和特异性均低于30个纹理NDA分类结果。［结论］ T2，3期直肠癌异质性有明显差异，MRI纹理特征定量分析能为T2，3期直肠癌的术前精准分期提供可靠客观依据。 |
| 英文摘要: |
| Abstract：［Purpose］ To investigate the quantitative analysis of MRI texture features in differentiating heterogeneity and precisely staging for rectal cancer stage T2，3. ［Methods］ Clinical and imaging data of 15 patients with stage T2，3 rectal cancer confirmed by postoperative pathology，who underwent rectal high-resolution MRI scan two weeks before operation，were retrospectively analyzed. The images which were more satisfied with the axial T2WI were selected，and 41 images were selected in 8 stage T2 cases and 40 images were selected in 7 stage T3 cases. By MaZda software，the region of interest(ROI) of the lesion was delineated for extracting the texture features. The mutual information(MI)，Fisher coefficient(Fisher)，and classification error probability combined with average correlation coefficients(POE+ACC) were provided by the software. The Fisher，POE+ACC and MI，and FPM(combination of Fisher，POE+ACC and MI) were used for screening texture features，then 30 texture features were obtained. Nonlinear classification analysis(NDA) was used for classifying 81 samples. Three optimal features were screened by the methods of BW(between-category to within-with-the-sums，BW) and DCA(decision curve analysis，DCA)，then comparing the differences of the result. The diagnostic efficacy of three single features，and their combination was analyzed by using binary Logistic regression and ROC curve. ［Results］ The accuracy of image classification in 81 samples was 92.6%. The sensitivity of T2 image as negative group and T3 image as posiive group was 93.0%，the specificity was 93.2%，the missed rate was 7.0%，and the misdiagnosis rate was 6.8%. The two sets of optimal characteristics were selected by BW and DCA methods，the first three optimal features were S(4，0) sum of squares(SumOfSqs)，S(5，0)SumOfSqs，S(3，0)SumOfSqs，and the efficiency order was different. The sensitivity and specificity of the combination of three optimal features were 85.0% and 70.4%，and the sensitivity and specificity of three single features were 80.0% and 62.9%，80.0% and 62.9%，72.5% and 60.3%，respectively. The sensitivity and specificity of the three single features or their combination were lower than the results of 30 texture features NDA classifications. ［Conclusion］ There are significant differences in the heterogeneity of rectal cancer stage T2，3，and the quantitative analysis of MRI texture feature may provide a reliable and objective basis for precise staging of preoperative rectal cancer stage T2，3. |
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