| 万强,陈基明,邢涛,邵颖.基于CT密度联合纹理参数建模预测垂体大腺瘤质地[J].中国医学影像技术,2019,35(8):1190~1194 |
| 基于CT密度联合纹理参数建模预测垂体大腺瘤质地 |
| Prediction of the consistency of large pituitary adenoma based on CT density combined with texture parameter modeling |
| 投稿时间：2019-01-04 修订日期：2019-06-20 |
| DOI：10.13929/j.1003-3289.201901019 |
| 中文关键词: 垂体肿瘤 腺瘤 纹理分析 体层摄影术,X线计算机 |
| 英文关键词:pituitary neoplasms adenoma texture analysis tomography,X-ray computed |
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| 中文摘要: |
| 目的 探讨基于CT平扫图像密度联合纹理参数预测垂体大腺瘤质地的价值。方法 收集50例经手术病理证实的垂体大腺瘤，根据术中垂体质地分为质软组（n=30）与质硬组（n=20）。于CT图像肿瘤最大层面手动勾画ROI，测量病变CT值，并提取纹理特征参数。比较2组间CT值及纹理特征差异，对有统计学意义的变量采用多因素Logistic回归分析建立预测垂体腺瘤质地的模型，绘制ROC曲线评价其预测效能。结果 质软组与质硬组间CT值差异有统计学意义（P=0.031），其预测肿瘤质地的AUC为0.662。基于CT平扫图像共提取77个纹理参数，经筛选获得4个2组间差异有统计学意义的参数，包括第90百分位数、惯量、方差和对比度，其预测肿瘤质地的AUC分别为0.662、0.663、0.672和0.663。多因素Logistic回归分析建立的纹理特征模型预测垂体腺瘤质地的AUC为0.690，CT值结合纹理参数模型的AUC为0.782。结论 CT值结合纹理参数建立的模型对于预测垂体腺瘤质地具有较高价值，可为临床选择手术方案提供帮助。 |
| 英文摘要: |
| Objective To explore the value of CT density combined with texture parameters based on CT plain image in predicting the consistency of large pituitary adenoma. Methods Totally 50 patients with large pituitary adenoma confirmed by operation and pathology were enrolled and divided into soft group (n=30) and hard group (n=20) according to intraoperative pituitary consistency. The largest slice of the tumor on the CT image was selected, then ROI was manually outlined, CT value of the lesion was measured, and the texture feature parameters were extracted. CT values and texture features were compared between the two groups. Multivariate Logistic regression analysis was used to analyze the variables, and the model for predicting the pituitary adenoma consistency was established. ROC curve was drawn to evaluate its predictive value. Results There was statistically significant difference in CT value between soft group and the hard group (P=0.031), and AUC in predicting tumor consistency was 0.662. A total of 77 texture parameters were extracted based on plain CT images, and 4 texture parameters were found with statistically significant differences between the two groups, including the Quantile 90, inertia, variance and contrast, with AUC of 0.662, 0.663, 0.672 and 0.663, respectively. AUC of texture feature model established with multivariate Logistic regression analysis in predicting the pituitary adenoma consistency was 0.690, of CT value combined with the texture parameter model was 0.782. Conclusion The model established with CT value combined with texture parameters has high value in predicting the pituitary adenoma consistency, which is helpful to clinical selection of surgical plans. |
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