Department of Diagnostic Radiology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Pan Jia Yuan Nan-li, PO Box 2258, Beijing, 100021, China.
CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Eur Radiol. 2018 May;28(5):2058-2067. doi: 10.1007/s00330-017-5146-8. Epub 2018 Jan 15.
To investigate whether CT-based radiomics signature can predict KRAS/NRAS/BRAF mutations in colorectal cancer (CRC).
This retrospective study consisted of a primary cohort (n = 61) and a validation cohort (n = 56) with pathologically confirmed CRC. Patients underwent KRAS/NRAS/BRAF mutation tests and contrast-enhanced CT before treatment. A total of 346 radiomics features were extracted from portal venous-phase CT images of the entire primary tumour. Associations between the genetic mutations and clinical background, tumour staging, and histological differentiation were assessed using univariate analysis. RELIEFF and support vector machine methods were performed to select key features and build a radiomics signature.
The radiomics signature was significantly associated with KRAS/NRAS/BRAF mutations (P < 0.001). The area under the curve, sensitivity, and specificity for predicting KRAS/NRAS/BRAF mutations were 0.869, 0.757, and 0.833 in the primary cohort, respectively, while they were 0.829, 0.686, and 0.857 in the validation cohort, respectively. Clinical background, tumour staging, and histological differentiation were not associated with KRAS/NRAS/BRAF mutations in both cohorts (P>0.05).
The proposed CT-based radiomics signature is associated with KRAS/NRAS/BRAF mutations. CT may be useful for analysis of tumour genotype in CRC and thus helpful to determine therapeutic strategies.
• Key features were extracted from CT images of the primary colorectal tumour. • The proposed radiomics signature was significantly associated with KRAS/NRAS/BRAF mutations. • In the primary cohort, the proposed radiomics signature predicted mutations. • Clinical background, tumour staging, and histological differentiation were unable to predict mutations.
探究基于 CT 的放射组学特征能否预测结直肠癌(CRC)中的 KRAS/NRAS/BRAF 突变。
本回顾性研究纳入了经病理证实的 CRC 患者,包含一个主要队列(n=61)和一个验证队列(n=56)。患者在治疗前均接受了 KRAS/NRAS/BRAF 基因突变检测和增强 CT 检查。从整个原发肿瘤的门静脉期 CT 图像中提取了 346 个放射组学特征。采用单因素分析评估遗传突变与临床背景、肿瘤分期和组织学分化之间的关系。采用 RELIEFF 和支持向量机方法选择关键特征并构建放射组学特征。
放射组学特征与 KRAS/NRAS/BRAF 突变显著相关(P<0.001)。在主要队列中,预测 KRAS/NRAS/BRAF 突变的曲线下面积、敏感度和特异度分别为 0.869、0.757 和 0.833,在验证队列中分别为 0.829、0.686 和 0.857。在两个队列中,临床背景、肿瘤分期和组织学分化均与 KRAS/NRAS/BRAF 突变无关(P>0.05)。
本研究提出的基于 CT 的放射组学特征与 KRAS/NRAS/BRAF 突变相关。CT 可能有助于分析 CRC 肿瘤的基因型,从而有助于确定治疗策略。
• 从原发结直肠肿瘤的 CT 图像中提取了关键特征。• 提出的放射组学特征与 KRAS/NRAS/BRAF 突变显著相关。• 在主要队列中,提出的放射组学特征可预测突变。• 临床背景、肿瘤分期和组织学分化无法预测突变。