Innovative Medical Engineering & Technology, Research Institute and Hospital, National Cancer Center, Goyang, Korea.
Center for Colorectal Cancer, Research Institute and Hospital, National Cancer Center, Goyang, Korea.
Cancer Res Treat. 2020 Jan;52(1):51-59. doi: 10.4143/crt.2019.050. Epub 2019 May 7.
Mutation of the Kirsten Ras (KRAS) oncogene is present in 30%-40% of colorectal cancers and has prognostic significance in rectal cancer. In this study, we examined the ability of radiomics features extracted from T2-weighted magnetic resonance (MR) images to differentiate between tumors with mutant KRAS and wild-type KRAS.
Sixty patients with primary rectal cancer (25 with mutant KRAS, 35 with wild-type KRAS) were retrospectively enrolled. Texture analysis was performed in all regions of interest on MR images, which were manually segmented by two independent radiologists. We identified potentially useful imaging features using the two-tailed t test and used them to build a discriminant model with a decision tree to estimate whether KRAS mutation had occurred.
Three radiomic features were significantly associated with KRASmutational status (p < 0.05). The mean (and standard deviation) skewness with gradient filter value was significantly higher in the mutant KRAS group than in the wild-type group (2.04±0.94 vs. 1.59±0.69). Higher standard deviations for medium texture (SSF3 and SSF4) were able to differentiate mutant KRAS (139.81±44.19 and 267.12±89.75, respectively) and wild-type KRAS (114.55±29.30 and 224.78±62.20). The final decision tree comprised three decision nodes and four terminal nodes, two of which designated KRAS mutation. The sensitivity, specificity, and accuracy of the decision tree was 84%, 80%, and 81.7%, respectively.
Using MR-based texture analysis, we identified three imaging features that could differentiate mutant from wild-type KRAS. T2-weighted images could be used to predict KRAS mutation status preoperatively in patients with rectal cancer.
Kirsten Ras（KRAS）癌基因突变存在于 30%-40%的结直肠癌中，对直肠癌具有预后意义。在这项研究中，我们检查了从 T2 加权磁共振（MR）图像中提取的放射组学特征区分 KRAS 突变型和野生型肿瘤的能力。
回顾性纳入 60 例原发性直肠癌患者（KRAS 突变型 25 例，野生型 35 例）。两名独立的放射科医生对 MR 图像上的所有感兴趣区域进行纹理分析，手动分割。我们使用双尾 t 检验识别潜在有用的成像特征，并使用决策树构建判别模型来估计 KRAS 突变是否发生。
有三个放射组学特征与 KRAS 突变状态显著相关（p<0.05）。在突变 KRAS 组中，梯度滤波器值的平均值（和标准差）偏度明显高于野生型组（2.04±0.94 对 1.59±0.69）。较高的中等纹理标准差（SSF3 和 SSF4）能够区分突变 KRAS（分别为 139.81±44.19 和 267.12±89.75）和野生型 KRAS（分别为 114.55±29.30 和 224.78±62.20）。最终的决策树由三个决策节点和四个终端节点组成，其中两个指定了 KRAS 突变。决策树的灵敏度、特异性和准确性分别为 84%、80%和 81.7%。
使用基于 MR 的纹理分析，我们确定了三个可以区分 KRAS 突变型和野生型的成像特征。T2 加权图像可用于预测直肠癌患者术前的 KRAS 突变状态。
