摘要
目的探讨基于放射组学的特征选择和亚组分析对肺腺癌患者预后的预测价值。方法选取293例接受放疗的肺腺癌患者,从胸部CT图像中提取107个放射学特征(14个形状特征、18个一阶统计特征和75个纹理特征)。分析3种不同的特征选择(FS)方法[即重测和多重分割(FS1)、皮尔逊相关分析(FS2)以及FS1和FS2相结合的方法(FS3)]对生存预测性能的影响。对各个T分期进行亚组分析,采用一致性指数(C-index)和Kaplan-Meier法评估预后表现。亚组分析采用5倍交叉验证以确保模型的可靠性。结果在放射学模型的训练和测试数据集中,FS2的C-index是所有选择方法中最高的(分别为0.64、0.61)。同样,FS2在组合模型的训练和测试数据集中的所有选择方法中具有最高的C-index(分别为0.65、0.63)。亚组分析表明,基于T分期的预测模型对测试数据集的C-index高于基于全数据的预测模型。结论特征选择方法在一定程度上提高了生存预测的性能,基于T分期的亚组预测模型可以提高预测性能。
Objective To explore the value of feature selection and subgroup analysis based on radiomics in predicting prognosis of patients with lung adenocarcinoma. Methods A total of 293 lung adenocarcinoma patients with radiotherapy were selected, and 107 radiological features(14 shape features, 18 first-order statistical features and 75 texture features) were extracted from chest CT images. The effects of three different feature selection(FS) methods[retest and multiple segmentation(FS1), Pearson correlation analysis(FS2) and the combination of FS1 and FS2(FS3)]on survival prediction performance were analyzed. Subgroup analysis was performed for each T stage, and the prognostic performance was evaluated by consistency index(C-index) and Kaplan-Meier method. Quintuple cross validation was used in subgroup analysis to ensure the reliability of the model. Results In the training and test data sets of radiology model, the C-index of FS2 was the highest among all the selection methods(values were 0.64 and 0.61 respectively). Similarly, FS2 showed the highest C-index(values were 0.65 and 0.63, respectively) among all the selection methods in the training and test data sets of the combined model. Subgroup analysis showed that the C-index of the prediction model based on T stage was higher than that based on the full data. Conclusion Feature selection method improves the performance of survival prediction to a certain extent, and the subgroup prediction model based on T stage can improve the prediction performance.
作者
焦玉泉
常艳亮
杨春媚
王翔
JIAO Yuquan;CHANG Yanliang;YANG Chunmei;WANG Xiang(Department of Medical Imaging,Yuncheng City Central Hospital of Shanxi Province,Yuncheng,Shanxi,044000)
出处
《实用临床医药杂志》
CAS
2022年第2期108-112,共5页
Journal of Clinical Medicine in Practice
关键词
放射组学
特征选择
预后分析
亚组分析
肺腺癌
radiomics
feature selection
prognosis analysis
subgroup analysis
lung adenocarcinoma
