Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China.
Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430079, China.
Curr Med Sci. 2020 Dec;40(6):1156-1160. doi: 10.1007/s11596-020-2298-6. Epub 2021 Jan 11.
The mutation status of KRAS is a significant biomarker in the prognosis of rectal cancer. This study investigated the feasibility of MRI-based radiomics in predicting the mutation status of KRAS with a composite index which could be an important criterion for KRAS mutation in clinical practice. In this retrospective study, a total of 127 patients with rectal cancer were enrolled. The 3D Slicer was used to extract the radiomics features from the MRI images, and sparse support vector machine (SVM) with linear kernel was applied for feature reduction. The radiomics classifier for predicting the KRAS status was then constructed by Linear Discriminant Analysis (LDA) and its performance was evaluated. The composite index was determined with LDA model. Out of 127 rectal cancer subjects, there were 44 KRAS mutation cases and 83 wild cases. A total of 104 radiomics features were extracted, 54 features were filtered by linear SVM with L1-norm regularization and 6 features that had no significant correlations within them were finally selected. The radiomics classifier constructed using the 6 features featured an AUC value of 0.669 (specificity: 0.506; sensitivity: 0.773) with LDA. Furthermore, the composite index (Radscore) had statistically significant difference between the KRAS mutation and wild groups. It is suggested that the MRI-based radiomics has the potential in predicting the KRAS status in patients with rectal cancer, which may enhance the diagnostic value of MRI in rectal cancer.
KRAS 突变状态是直肠癌预后的重要生物标志物。本研究旨在探讨基于 MRI 的放射组学是否能利用复合指标预测 KRAS 突变状态，该复合指标可能成为临床 KRAS 突变的重要标准。在这项回顾性研究中，共纳入 127 例直肠癌患者。使用 3D Slicer 从 MRI 图像中提取放射组学特征，采用稀疏支持向量机（SVM）进行特征降维，使用线性核 SVM 进行特征选择。然后使用线性判别分析（LDA）构建预测 KRAS 状态的放射组学分类器，并评估其性能。采用 LDA 模型确定复合指标。在 127 例直肠癌患者中，KRAS 突变 44 例，野生型 83 例。共提取 104 个放射组学特征，经线性 SVM （L1-范数正则化）过滤后保留 54 个特征，最终选择了 6 个特征，它们之间没有显著的相关性。使用这 6 个特征构建的放射组学分类器的 AUC 值为 0.669（特异性：0.506；敏感性：0.773），LDA 分析显示其具有统计学意义。此外，KRAS 突变组和野生组之间的复合指标（Radscore）有显著差异。这表明基于 MRI 的放射组学在预测直肠癌患者的 KRAS 状态方面具有一定的潜力，这可能提高 MRI 在直肠癌诊断中的应用价值。
