Chinese Medical Sciences Journal ›› 2020, Vol. 35 ›› Issue (4): 306-314.doi: 10.24920/003770

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  1. 北京协和医院放射科,中国医学科学院 北京协和医学院,北京 100730,中国
  • 收稿日期:2020-04-30 接受日期:2020-07-16 出版日期:2020-12-31 发布日期:2021-01-08
  • 通讯作者: 王志伟

CT Texture Analysis: A Potential Biomarker for Evaluating KRAS Mutational Status in Colorectal Cancer

Jian Cao,Guorong Wang,Zhiwei Wang(),Zhengyu Jin   

  1. Department of Radiology, Peking Union Medical College Hospital,Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
  • Received:2020-04-30 Accepted:2020-07-16 Online:2020-12-31 Published:2021-01-08
  • Contact: Zhiwei Wang


目的 纹理分析可以反映肉眼难以察觉到的肿瘤内异质性。本研究旨在评估CT纹理分析技术鉴别结直肠癌患者KRAS基因突变状态的可行性。
方法 回顾性纳入92例经病理证实且术前接受了腹部增强CT检查的结直肠癌患者。将患者分为训练集(n=51)和验证集(n=41)。使用TexRad纹理分析软件在选定的轴位CT图像上肿瘤区域放置感兴趣区,获得基于不同空间缩放因子(spatial scaling factor, SSF)的特征性纹理参数值,包括均值(mean)、标准差(SD)、熵值(entropy)、偏度值(skewness)、峰值(kurtosis)和正像素均值(mean of positive pixels, MPP)。比较训练集和验证集KRAS突变型患者和野生型患者的CT图像纹理参数值和临床特征(年龄,性别,肿瘤位置,组织病理学,肿瘤大小,TNM分期)。使用皮尔森相关系数计算所有纹理特征的相关性。若某两个纹理特征具有显着的相关性,则删掉较低曲线下面积(area under the curve, AUC)的特征值;纳入最具区分性的单一特征参数,并组合以训练多特征的支持向量机分类器。使用受试者工作特征曲线(receiver operating characteristic, ROC)法评估纹理参数对鉴别结直肠癌患者KRAS突变型与野生型的诊断效能。
结果 在训练集和验证集中,KRAS突变型与野生型患者两组之间的临床特征均无显着差异(P>0.05)。预测结直肠癌患者KRAS突变的最佳模型包括6个纹理特征值,分别是平扫CT中SSF 5的偏度值、SSF 2的熵值、SSF 0的偏度值和峰度值,以及增强CT中SSF3的峰度值和均值。以此建立的诊断模型在训练集中诊断患者KRAS基因突变的曲线下面积为0.951(95% CI:0.895~1,P<0.001),当阈值为0.46时,诊断灵敏度为88.9%,特异度为91.7%。应用于验证集中的曲线下面积为0.995(95% CI:0.982~1, P<0.001),当阈值为0.28时,诊断KRAS突变的灵敏度和特异度分别为100%和93.7%。
结论 利用CT纹理分析技术评估结直肠癌患者KRAS突变状态是可行的。

关键词: 生物标志物, 结直肠肿瘤, 纹理分析, 计算机断层扫描


Objective Texture analysis is deemed to reflect intratumor heterogeneity invisible to the naked eyes. The aim of this study was to evaluate the feasibility of assessing the KRAS mutational status in colorectal cancer (CRC) patients using CT texture analysis.
Methods This retrospective study included 92 patients who had histopathologically confirmed CRC and underwent preoperative contrast-enhanced CT examinations. The patients were assigned into a training cohort (n=51) and a validation cohort (n=41). We placed the region of interest in the tumour regions on the selected axial images using software of TexRad to extract a series of quantitative parameters based on the spatial scaling factors (SSFs), including mean, standard deviation (SD), entropy, mean of positive pixels (MPP), skewness, and kurtosis. The texture parameters and clinical characteristics (age, gender, tumour location, histopathology, tumour size, T, N, M stages) were compared between the mutated and wild-type KRAS patient groups in training cohort and validation cohort. Before building the multiple feature classifier, we calculated the correlations of the features using Pearson’s correlation coefficient, and if any two features were significantly correlated, the one with lower AUC was removed. Ultimately, only the most discriminative isolated features were combined to train a supporting vector machine (SVM) classifier. The receiver operating characteristic (ROC) curve was processed for evaluating the diagnostic efficiency of texture parameters in differentiating CRC patients with mutated KRAS from those with wild-type KRAS.
Results None of the clinical characteristics were significant different between CRC patients with wild-type KRAS and mutated KRAS in both cohorts. For predicting the expression of mutated KRAS in CRC patients, the perfect model which combined skewness on SSF 5 by unenhanced CT, entropy on SSF 2, skewness and kurtosis on SSF 0, and kurtosis and mean on SSF 3 by enhanced CT, showed a desirable AUC of 0.951 (95% CI: 0.895-1, P<0.001), with a sensitivity of 88.9% and a specificity of 91.7%, when the cut-off value was 0.46 in the training cohort; while in the validation cohort, the AUC value was 0.995 (95% CI: 0.982-1, P<0.001), the sensitivity was 100%, and the specificity was 93.7% when the cut-off value was 0.28.
Conclusion It is feasible to evaluate the KRAS mutational status in CRC using CT texture analysis.

Key words: biological markers, colorectal neoplasms, texture analysis, computed tomography

基金资助: 中央型公益性科研院所基本科研业务费项目—影像精准诊疗新技术研发及转化应用(2018PT32003)

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