武敬君,刘爱连,赵莹,张钦和,刘义军,李昕,吴艇帆,郭妍,李剑颖.能谱CT成像碘(水)图纹理分析预测结直肠癌微卫星不稳定状态[J].中国医学影像技术,2019,35(11):1683~1688
能谱CT成像碘(水)图纹理分析预测结直肠癌微卫星不稳定状态
Texture analysis of iodine-based material decomposition images with spectral CT imaging for predicting microsatellite instability status in colorectal cancer
投稿时间:2019-05-09  修订日期:2019-07-26
DOI:10.13929/j.1003-3289.201905067
中文关键词:  结直肠肿瘤  微卫星不稳定  碘(水)图像  纹理分析
英文关键词:colorectal neoplasms  microsatellite instability  iodine-based material decomposition image  texture analysis
基金项目:首都科技领军人才培养工程(Z181100006318003)。
作者单位E-mail
武敬君 大连医科大学附属第一医院放射科, 辽宁 大连 116011  
刘爱连 大连医科大学附属第一医院放射科, 辽宁 大连 116011 liuailian@dmu.edu.cn 
赵莹 大连医科大学附属第一医院放射科, 辽宁 大连 116011  
张钦和 大连医科大学附属第一医院放射科, 辽宁 大连 116011  
刘义军 大连医科大学附属第一医院放射科, 辽宁 大连 116011  
李昕 通用电气医疗, 上海 200000  
吴艇帆 通用电气医疗, 上海 200000  
郭妍 通用电气医疗, 上海 200000  
李剑颖 通用电气医疗, 上海 200000  
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中文摘要:
      目的 探讨基于能谱CT成像碘(水)图像的纹理分析在术前预测结直肠癌微卫星不稳定(MSI)状态方面的价值。方法 回顾性分析23例MSI结直肠癌(MSI组)及46例微卫星稳定(MSS)结直肠癌(MSS组)患者的资料。所有患者均经术后病理检查证实,且术前均接受腹部能谱CT成像。采用Viewer分析软件获取动脉期及静脉期碘(水)图像,并将其导入Omni-Kinetics软件进行ROI勾画及特征提取。提取的纹理参数包括最小值、最大值、平均值、中位值、标准差、偏度、峰度、均匀性、能量值、熵。比较2组间各纹理参数的差异。并采用Logistic回归将纹理参数进行联合,通过ROC曲线分析不同纹理参数预测及多种参数联合预测的效能。结果 MSI组动脉期及静脉期最小值、最大值、平均值、中位值、均匀性均明显低于MSS组(P均<0.05),2组间标准差、偏度、峰度、能量值差异均无统计学意义(P均>0.05);MSI组静脉期熵明显高于MSS组(t=1.81,P=0.04),2组间动脉期熵差异无统计学意义(t=0.22,P=0.80)。ROC曲线分析显示,以动脉期及静脉期最小值、最大值、平均值、中位值、均匀性和静脉期熵单一参数在术前预测结直肠癌MSI状态的AUC为0.64~0.82。多参数联合的Logistic回归模型为-2.598-0.124×动脉期最小值-0.039×动脉期最小值-0.774×动脉期中位值+1×动脉期平均值-1.892×动脉期均匀性+0.14×静脉期最小值+0.2×静脉期最大值+0.343×静脉期中位值-0.61×静脉期平均值+13.711×静脉期均匀性-2.598×静脉期熵,联合预测的AUC为0.83。结论 基于能谱CT成像碘(水)图像纹理分析,可在术前无创预测结直肠癌MSI状态,且将多种纹理参数联合后预测效能更优。
英文摘要:
      Objective To investigate the value of texture analysis of iodine-based material decomposition images with spectral CT imaging for predicting microsatellite instability (MSI) status in colorectal cancer (CRC). Methods Data of 23 patients with MSI status CRC and 46 patients with microsatellite stability (MSS) status CRC confirmed by postoperative pathology were retrospectively analyzed. All CRC patients underwent preoperative abdominal gemstone spectral imaging. Iodine-based material decomposition images in arterial and venous phases were produced with Viewer software, and the images were imported into Omni-Kinetics software for ROI sketching and feature extraction. The texture parameters included minimum intensity, maximum intensity, mean intensity, median intensity, standard deviation, kewness, kurtosis, uniformity, energy and entropy. The differences of parameters between the two groups were compared. Logistic regression was used to combine texture parameters. Diagnostic performances of various texture parameters and the combination of multiple parameters were studied with ROC analysis. Results Both in arterial and venous phases, the minimum, maximum, mean, median, and uniformity in MSI group were significantly lower than those in MSS group (all P<0.05), and there was no significant difference of standard deviation, skewness, kurtosis and energy between the two groups (all P>0.05). In venous phase, entropy in MSI group was significantly higher than that in MSS group (t=1.81, P=0.04). In arterial phase, there was no significant difference in entropy between the two groups (t=0.22, P=0.80). ROC analysis showed that the range of AUC for predicting MSI status in CRC patients using single texture parameter as minimum, maximum, mean, median, uniformity in arterial and venous phase or entropy in venous phase was 0.64~0.82. Multi-parameter combined diagnosis Logistic regression model was -2.598-0.124×arterial phase minimum-0.039×arterial phase maximum-0.774×arterial phase median+1×arterial phase mean-1.892×arterial phase uniformity+0.14×venous phase minimum+0.2×venous phase maximum+0.343×venous phase median-0.61×venous phase mean+13.711×venous phase uniformity-2.598×venous phase entropy. When combined multiple texture parameters, the AUC was 0.83. Conclusion Texture analysis of iodine-based material decomposition image with spectral CT can serve as a preoperative non-invasive method for predicting MSI status in CRC patients. And the optimal predictive value was observed when combined all significant texture parameters.
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