From the Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning.
GE Healthcare, Shanghai, China.
J Comput Assist Tomogr. 2021;45(6):932-940. doi: 10.1097/RCT.0000000000001213.
This study investigated the role of radiomics in evaluating the alterations of oncogenic signaling pathways in head and neck cancer.
Radiomics features were extracted from 106 enhanced computed tomography images with head and neck squamous cell carcinoma. Support vector machine-recursive feature elimination was used for feature selection. Support vector machine algorithm was used to develop radiomics scores to predict genetic alterations in oncogenic signaling pathways. The performance was evaluated by the area under the curve (AUC) of the receiver operating characteristic curve.
The alterations of the Cell Cycle, HIPPO, NOTCH, PI3K, RTK RAS, and TP53 signaling pathways were predicted by radiomics scores. The AUC values of the training cohort were 0.94, 0.91, 0.94, 0.93, 0.87, and 0.93, respectively. The AUC values of the validation cohort were all greater than 0.7.
Radiogenomics is a new method for noninvasive acquisition of tumor molecular information at the genetic level.
本研究探讨了影像组学在评估头颈部鳞状细胞癌致癌信号通路改变中的作用。
从 106 例头颈部鳞状细胞癌增强 CT 图像中提取影像组学特征。采用支持向量机递归特征消除法进行特征选择。利用支持向量机算法建立影像组学评分，预测致癌信号通路中的基因改变。采用受试者工作特征曲线下面积（AUC）评估性能。
影像组学评分可预测细胞周期、HIPPO、NOTCH、PI3K、RTK RAS 和 TP53 信号通路的改变。训练队列的 AUC 值分别为 0.94、0.91、0.94、0.93、0.87 和 0.93。验证队列的 AUC 值均大于 0.7。
放射组学是一种新的非侵入性获取肿瘤遗传水平分子信息的方法。
