摘要
目的:采用影像组学方法分析放疗定位CT影像的组学特点,构建预测局限期小细胞肺癌(limit-stage small cell lung cancer,LS-SCLC)患者总生存(overall survival,OS)期、无进展生存(progression-free survival,PFS)期的组学模型,为个体化治疗提供依据。方法:回顾性分析天津医科大学肿瘤医院2013年9月至2019年12月193例LS-SCLC患者的放疗定位CT资料,并将患者按照7∶3分为训练组和测试组,勾画患者肿瘤区域(gross tumor volume,GTV)进行特征分析。随访获得的患者预后数据,以t检验和LASSO筛选特征建立随机森林预测模型,以曲线下面积(area under the receiver operating characteristic curve,AUC)对模型进行验证评估。结果:患者中位OS为29.77个月,中位PFS为19.03个月。每例患者提取了1037个影像特征,包含一阶特征、形状特征和纹理特征。分别以OS≤1年或OS≥3年、OS≤1年或OS≥5年、PFS≤6个月或PFS≥24个月作为标准对患者分组,各测试组模型的AUC均值分别为0.73、0.79、0.70。组学特征中original_ngtdm_Strength、wavelet-HHL_ngtdm_Busyness、wavelet-LLH_glcm_ClusterShade和wavelet-LLH_glcm_Correlation等参数具有预测价值。结论:基于放疗定位CT的影像组学获得的影像特征模型对LS-SCLC患者预后有一定预测价值,纳入临床因素建立融合模型综合分析可能获得更为理想的结果。
Objectives:To evaluate the effectiveness of the radiomic features of positioning computed tomography(CT)in prognostication,including overall survival(OS)and progression-free survival(PFS),among patients with limited-stage small cell lung cancer(LS-SCLC)and to improve individualized treatment for them.Methods:A total of 193 patients with LS-SCLC,who were admitted to the Tianjin Medical University Cancer Institute&Hospital,were enrolled in this retrospective study,conducted from September 2013 to December 2019.The patients were randomly assigned into the training and testing groups in a ratio of 7:3.The gross tumor volume(GTV)was segmented by experienced radiologists to extract features as regions of interest.The random forest classification was used to further analyze possible prognostic factors,selected via t-test and least absolute shrinkage and selection operator(LASSO).The performance of the models was evaluated considering the area under the receiver operating characteristic curve(AUC).Results:The median OS of the whole cohort was 29.77 months,and the median PFS was 19.03 months.A total of 1,037 radiomic features were extracted from the CT location images,including the first-order,shape,and texture features.All patients were selected considering different standards to develop models,including cohorts of OS≤12 months or OS≥36 months,OS≤12 months or OS≥60 months,and PFS≤6 months or PFS≥24 months.The mean AUCs of all models were 0.73,0.79,and 0.70,respectively.The most important features were original_ngtdm_Strength,wavelet-HHL_ngtdm_Busyness,wavelet-LLH_glcm_ClusterShade,and wavelet-LLH_glcm_Correlation.Conclusions:The radiomic model,based on positioning CT,was a viable prognostication tool for LS-SCLC.A combined model that considers clinical factors and radiomic features may help obtain more ideal results.
作者
吴洁菡
宋家伟
徐畅
王伟
杨成文
刘桂芝
刘宁波
Jiehan Wu;Jiawei Song;Chang Xu;Wei Wang;Chengwen Yang;Guizhi Liu;Ningbo Liu(Department of Radiotherapy,Tianjin Medical University Cancer Institute&Hospital,National Clinical Research Center for Cancer,Tianjin Key Laboratory of Cancer Prevention and Therapy,Tianjin 300060,China;Department of Oncology,The People's Hospital of Ganyu District,Lianyungang 222100,China)
出处
《中国肿瘤临床》
CAS
CSCD
北大核心
2023年第1期37-43,共7页
Chinese Journal of Clinical Oncology
关键词
小细胞肺癌
影像组学
预后
纹理分析
CT模拟定位
small cell lung cancer(SCLC)
radiomics
prognosis
texture analysis
CT simulation positioning
作者简介
吴洁菡,专业方向为胸部肿瘤放疗和影像组学基础研究。E-mail:jcheart@163.com;通信作者:刘宁波,liuningbo@tjmuch.com。
