CT影像组学特征预测晚期肺腺癌表皮生长因子受体突变状态及表皮生长因子受体酪氨酸激酶抑制剂治疗敏感性的效能
目的 应用CT影像组学特征预测晚期肺腺癌表皮生长因子受体(EGFR)的突变状态,筛选EGFR酪氨酸激酶抑制剂(EGFR?TKIs)治疗的优势人群.方法 回顾性分析行EGFR突变检测的253例晚期肺腺癌患者的临床资料.选取患者治疗前平扫期、动脉期和静脉期CT图像,每组CT提取715个影像组学特征.应用Lasso?logistic回归模型和10折交叉验证方法,分析预测EGFR突变的状态,筛选EGFR?TKIs治疗敏感人群的影像组学特征.结果 EGFR突变状态验证组单时相(平扫期、动脉期和静脉期)的受试者工作特征曲线下面积( AUC)分别为0.763、0.807和0.808,三时相所提取的影像组学特...
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| Published in | 中华肿瘤杂志 Vol. 41; no. 4; pp. 282 - 287 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
济宁市第一人民医院肿瘤科272000%山东省肿瘤医院放射物理技术科,济南,250117
01.04.2019
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0253-3766 |
| DOI | 10.3760/cma.j.issn.0253-3766.2019.04.007 |
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| Abstract | 目的 应用CT影像组学特征预测晚期肺腺癌表皮生长因子受体(EGFR)的突变状态,筛选EGFR酪氨酸激酶抑制剂(EGFR?TKIs)治疗的优势人群.方法 回顾性分析行EGFR突变检测的253例晚期肺腺癌患者的临床资料.选取患者治疗前平扫期、动脉期和静脉期CT图像,每组CT提取715个影像组学特征.应用Lasso?logistic回归模型和10折交叉验证方法,分析预测EGFR突变的状态,筛选EGFR?TKIs治疗敏感人群的影像组学特征.结果 EGFR突变状态验证组单时相(平扫期、动脉期和静脉期)的受试者工作特征曲线下面积( AUC)分别为0.763、0.807和0.808,三时相所提取的影像组学特征分别为5、18、23个,可区分EGFR突变状态. EGFR?TKIs敏感验证组单时相(平扫期、动脉期和静脉期)的AUC值分别为0.730、0.833、0.895,三时相所提取的影像组学特征分别为3、7、22个,可筛选EGFR?TKIs治疗敏感的优势人群.静脉期CT图像特征在预测EGFR突变状态、EGFR?TKIs治疗敏感性方面的效能显著高于平扫期和动脉期.结论 不同时相的CT扫描影像组学特征均可作为预测肺腺癌EGFR突变状态、筛选EGFR?TKIs治疗优势人群的无创手段,有利于提高中晚期肺腺癌患者靶向治疗的效能. |
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| AbstractList | 目的 应用CT影像组学特征预测晚期肺腺癌表皮生长因子受体(EGFR)的突变状态,筛选EGFR酪氨酸激酶抑制剂(EGFR?TKIs)治疗的优势人群.方法 回顾性分析行EGFR突变检测的253例晚期肺腺癌患者的临床资料.选取患者治疗前平扫期、动脉期和静脉期CT图像,每组CT提取715个影像组学特征.应用Lasso?logistic回归模型和10折交叉验证方法,分析预测EGFR突变的状态,筛选EGFR?TKIs治疗敏感人群的影像组学特征.结果 EGFR突变状态验证组单时相(平扫期、动脉期和静脉期)的受试者工作特征曲线下面积( AUC)分别为0.763、0.807和0.808,三时相所提取的影像组学特征分别为5、18、23个,可区分EGFR突变状态. EGFR?TKIs敏感验证组单时相(平扫期、动脉期和静脉期)的AUC值分别为0.730、0.833、0.895,三时相所提取的影像组学特征分别为3、7、22个,可筛选EGFR?TKIs治疗敏感的优势人群.静脉期CT图像特征在预测EGFR突变状态、EGFR?TKIs治疗敏感性方面的效能显著高于平扫期和动脉期.结论 不同时相的CT扫描影像组学特征均可作为预测肺腺癌EGFR突变状态、筛选EGFR?TKIs治疗优势人群的无创手段,有利于提高中晚期肺腺癌患者靶向治疗的效能. |
| Abstract_FL | Objective To explore the ability of computed?tomography ( CT) radiomic features to predict the Epidermal growth factor receptor ( EGFR ) mutation status and the therapeutic response of advanced lung adenocarcinoma to EGFR? Tyrosine kinase inhibitors ( TKIs ) treatment. Methods A retrospective analysis was performed on 253 patients diagnosed as advanced lung adenocarcinoma, who underwent EGFR mutation detection, and those with EGFR sensitive mutation were treated with TKIs. Using the Lasso regression model and the 10 fold cross?validation method, the radiomic features of predicted EGFR mutation status and the screening of TKIs for sensitive populations were obtained.715 radiomic features were extracted from unenhanced, arterial phase and venous phase, respectively. Results The area under curve (AUC) values of the multi?phases including unenhanced, arterial phase and venous phase of the EGFR mutation status validation group were 0.763, 0.807 and 0.808, respectively. The number of radiomic features extracted from the multi?phases were 5, 18 and 23, respectively, which could distinguish the EGFR mutation status. The AUC values of the multi?phases of the EGFR?TKIs sensitive validation group were 0.730, 0.833 and 0.895, respectively. The number of radiomic features extracted from the multi?phases were 3, 7 and 22, respectively, which can be used to screen the superior population for TKIs treatment. The efficiency of radiomic features extracted from venous phase in predicting EGFR mutant status and EGFR?TKIs sensitivity was significantly superior than those of unenhanced and arterial phase. Conclusions The radiomic features of CT scanning can be used as the radiomics biomarker to predict the EGFR mutation status of lung adenocarcinoma and to further screen the dominant population in TKIs therapy, which provides the basis for targeted therapy. |
| Author | 巩贯忠 仇清涛 杨春生 尹勇 李振江 陈卫东 |
| AuthorAffiliation | 济宁市第一人民医院肿瘤科272000%山东省肿瘤医院放射物理技术科,济南,250117 |
| AuthorAffiliation_xml | – name: 济宁市第一人民医院肿瘤科272000%山东省肿瘤医院放射物理技术科,济南,250117 |
| Author_FL | Yin Yong Chen Weidong Yang Chunsheng Li Zhenjiang Qiu Qingtao Gong Guanzhong |
| Author_FL_xml | – sequence: 1 fullname: Yang Chunsheng – sequence: 2 fullname: Chen Weidong – sequence: 3 fullname: Gong Guanzhong – sequence: 4 fullname: Li Zhenjiang – sequence: 5 fullname: Qiu Qingtao – sequence: 6 fullname: Yin Yong |
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| Keywords | [Subject words] Lung neoplasms [主题词] 肺肿瘤 影像组学 表皮生长因子受体酪氨酸激酶抑制剂 Epidermal growth factor receptor?tyrosine kinase inhibitors 表皮生长因子受体 Epidermal growth factor receptor Radiomics |
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| Title | CT影像组学特征预测晚期肺腺癌表皮生长因子受体突变状态及表皮生长因子受体酪氨酸激酶抑制剂治疗敏感性的效能 |
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