[1]陈 彦,姜立朋.基于CT的影像组学特征对非小细胞肺癌放射治疗效果的评估价值[J].陕西医学杂志,2021,50(9):1094-1097.[doi:DOI:10.3969/j.issn.1000-7377.2021.09.013]
　CHEN Yan,JIANG Lipeng.Evaluation value of CT-based imaging omics features for radiotherapy effect in treatment of non-small cell lung cancer[J].,2021,50(9):1094-1097.[doi:DOI:10.3969/j.issn.1000-7377.2021.09.013]
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基于CT的影像组学特征对非小细胞肺癌放射治疗效果的评估价值
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《陕西医学杂志》[ISSN:1000-7377/CN:61-1281/TN]

卷:
50
期数:
2021年9期
页码:
1094-1097
栏目:
临床研究
出版日期:
2021-09-05

文章信息/Info

Title:
Evaluation value of CT-based imaging omics features for radiotherapy effect in treatment of non-small cell lung cancer
作者:
陈 彦1; 2; 姜立朋3
(1.锦州医科大学,辽宁 锦州 121001; 2.赤峰学院附属医院放疗科,内蒙古 赤峰 024000; 3.锦州医科大学附属第一医院放疗科,辽宁 锦州 121001)
Author(s):
CHEN Yan; JIANG Lipeng
(Jinzhou Medical University,Jinzhou 121001,China)
关键词:
非小细胞肺癌;  影像组学;  立体定向放射治疗;  感兴趣区域;  评估价值
Keywords:
Non-small cell lung cancer;  Imaging omics;  Stereotactic body radiation therapy;  Region of interest;  Evaluation value
分类号:
R 734.2
DOI:
DOI:10.3969/j.issn.1000-7377.2021.09.013
文献标志码:
A
摘要:
目的:探究基于CT的影像组学特征对非小细胞肺癌(NSCLC)放射治疗效果的评估价值。方法:选取90例NSCLC患者,随机分为训练组(45例)和验证组(45例)。所有患者均接受立体定向放射治疗(SBRT)。收集患者治疗前CT图像,勾画感兴趣区域(ROI)并提取影像组学特征。应用最小绝对收缩和选择运算符(LASSO)筛选最佳组学特征,分析影像组学特征与SBRT疗效的关系。结果:LASSO回归分析筛选出平扫期、动脉期和静脉期最佳影像特征分别为4、12和18个,训练组影像组学标签为-0.853±0.276,验证组影像组学标签为-0.781±0.248。基于平扫期、动脉期和静脉期CT的影像组学特征建立的模型预测SBRT疗效的曲线下面积(AUC)为分别为0.768、0.802和0.806,灵敏度分别为75.33%、80.02%和80.00%,特异度分别为68.71%、81.48%和81.22%。Logistic回归分析显示,影像组学标签、IDO和CEA水平是影响SBRT疗效的独立危险因素。结论:基于不同时相CT的影像组学特征建立的模型均可作为评估NSCLC放射治疗效果的有效检查手段,对影像组学标签和实验室指标的动态变化进行追踪分析有利于放射治疗效果的预测。
Abstract:
Objective:To explore the evaluation value of CT-based imaging omics features for radiotherapy effect in the treatment of non-small cell lung cancer(NSCLC).Methods:90 patients with NSCLC were selected and randomly divided into training group(45 cases)and verification group(45 cases).All patients received stereotactic body radiation therapy(SBRT).CT images before treatment were collected to delineate the region of interest(ROI)and extract the imaging omics features.Least absolute shrinkage and selection operator(LASSO)was used to screen the best omic features.The relationship between the imaging features and the efficacy of SBRT was analyzed.Results:LASSO regression analysis showed that there were 4,12 and 18 best imaging features in plain scan,arterial phase and venous phase,respectively.The imaging omics labels in training group and verification group were -0.853±0.276 and -0.781±0.248,respectively.The AUC of the model based on CT imaging features in plain scan,arterial phase and venous phase was 0.768,0.802 and 0.806,the sensitivity was 75.33%,80.02% and 80.00%,and the specificity was 68.71%,81.48% and 81.22%,respectively.Logistic regression analysis showed that imaging omics label,IDO and CEA were independent risk factors for the efficacy of SBRT.Conclusion:The established models based on CT imaging omics features in different phases can be applied as effective examination methods to evaluate radiotherapy effect in NSCLC patients.Tracking and analyzing the dynamic changes of imaging omics labels and laboratory indexes are beneficial to predict radiotherapy effect.

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备注/Memo

备注/Memo:
基金项目:辽宁省自然科学基金指导计划项目(2018225032)
更新日期/Last Update: 2021-09-06