对比MRI平扫、增强图像的影像组学标签对直肠癌生存期的预测价值  

To compare the predictive value of the radiomics siganature extrated from MRI plain or enhancement imaging for the survival of rectal cancer

作　　者：孟闫凯 张雨晨 张翀达 万丽娟 张红梅 董迪[3] 赵心明 徐凯 李纯明 周纯武 Meng Yankai, Zhang Yuchen, Zhang Chongda, Wan Lijuan, Zhang Hongmei, Dong Di, Zhao Xinming, Xu Kai, Li Chunming, Zhou Chunwu.(Department of Diagnostic Radiology, National Cancer Center, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, Chin)

机构地区：[1]国家癌症中心中国医学科学院北京协和医学院肿瘤医院影像诊断科,北京100021 [2]电子科技大学中国科学院自动化研究所 [3]中国科学院自动化研究所 [4]徐州医科大学附属医院影像科 [5]电子科技大学

出　　处：《中华放射学杂志》2018年第5期349-355,共7页Chinese Journal of Radiology

基　　金：公益性行业科研专项（201402019）;北京市科技计划课题（Z161100000516101）;中国癌症基金会北京希望马拉松专项基金（LC2016A05）;中国科技部国家重点研究与发展项目（2016YFC0905303.2016YFC0904600）

摘　　要：目的 对比MRI平扫、增强图像的影像组学标签对直肠癌生存期的预测价值.方法 回顾性分析2010年10月至2013年12月中国医学科学院肿瘤医院活检组织病理证实为直肠腺癌的51例患者,所有患者均在全直肠系膜切除术前行新辅助放化疗（nCRT）,且在nCRT前行盆腔MRI平扫和增强序列检查.所有存活患者的随访时间均大于3年.分别在平扫轴面小FOV的T2WI序列及多期动态增强序列静脉期上进行图像分割.采用LASSO-Cox回归分析提取影像组学特征,构建影像组学标签.根据每例患者的影像组学评分,将患者分为生存期较短的高风险组和生存期较长的低风险组.采用Kaplan-Meier生存曲线分析,分别比较训练集、验证集中影像组学标签的高、低风险组间生存期的差异,同时进行log-rank假设检验,采用一致性指数（C-index）评价模型的预测能力.结果 51例中,训练集36例、验证集15例.复发、转移的患者32例,其中局部复发3例、远处转移26例,同时合并复发、转移患者3例;其余19例删失.在增强序列上,筛选得到12个影像组学特征;训练集影像组学标签和无进展生存期有联系（P=0.0002）,模型的一致性指数为0.904;验证集影像组学标签和DFS也具有联系（P=0.0091）,模型在验证集上的一致性指数为0.700,模型具有较好的预测生存期的能力.平扫序列筛选得到2个影像组学特征;训练集影像组学标签和DFS有联系（P=0.0050）,模型的一致性指数为0.711;验证集影像组学标签和DFS无联系（P=0.7670）,模型在验证集上的一致性指数仅为0.500.结论 直肠癌nCRT前增强序列静脉期影像组学标签预测生存期优于平扫序列.Objective To compare the predictive value of radiomics signature extracted from MRI plain and enhancement sequence for the disease-free survival （DFS） of rectal cancer. Methods We retrospectively analyzed fifty-one patients with rectal adenocarcinoma confirmed by biopsy from October 2010 to December 2013 in Cancer Hospital Chinese Academy of Medical Sciences.All patients underwent neoadjuvant chemotherapy（nCRT）followed total mesorectal excision（TME）,and MRI scans were performed before nCRT.Follow-up time for the survival patients were more than 3 years.The image segmentation was performed on the T2WI sequence of the small FOV and the multi-phase enhancement sequence venous phase,respectively.Least absolute shrinkage and selection operator（LASSO）Cox regression was applied to extract radiomics features and the imaging signature was constructed. According to the radiomics score of each patient,the patients were divided into the high risk group with shorter DFS and the low risk group with longer DFS. A 3-year DFS was calculated for radiomics signature using the Kaplan-Meier product limit method with univariate log-rank analysis testing for differences in the training and validation cohort, respectively. And the predictive ability of the model was evaluated by concordance index （C-index）. Results The training set and the validation set were 36 and 15 cases, respectively. During follow-up 32 patients experienced relapse（26 distant,3 local and 3 both）,and 19 cases were censored.Twelve features were extracted in the enhanced sequence.The radiomics signatures were significant for DFS in the training set and the validation set（P=0.000 2 and 0.009 1,respectively）.The C-index of the model were 0.904 and 0.700 in the training set and the validation set, respectively. The model has the better ability to predict survival.Two features were extracted in the plain sequence.The radiomic signature was significant for DFS in the training set（P=0.005 0）,while the radiomics signature was not significant

关 键 词：磁共振成像 影像组学 图像分割 直肠肿瘤 新辅助放化疗 

分 类 号：R445.2[医药卫生—影像医学与核医学]