| 王寻,葛双,郗会珍,等.基于标准化增强CT影像组学列线图预测肺腺癌表皮生长因子受体突变状态的研究[J].中华放射医学与防护杂志,2024,44(3):194-201.Wang Xun,Ge Shuang,Xi Huizhen,et al.Prediction of EGFR mutation status in lung adenocarcinoma based on standardized enhanced CT radiomics nomogram[J].Chin J Radiol Med Prot,2024,44(3):194-201 |
| 基于标准化增强CT影像组学列线图预测肺腺癌表皮生长因子受体突变状态的研究 |
| Prediction of EGFR mutation status in lung adenocarcinoma based on standardized enhanced CT radiomics nomogram |
| 投稿时间：2023-08-23 |
| DOI：10.3760/cma.j.cn112271-20230823-00059 |
| 中文关键词: 肺腺癌 表皮生长因子受体 影像组学 预测 突变 |
| 英文关键词:Lung adenocarcinoma Epidermal growth factor receptor Radiomics Predition Mutation |
| 基金项目:济宁市重点研发计划项目(2022YXNS029) |
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| 中文摘要: |
| 目的 探讨基于治疗前标准化胸部增强CT的影像组学列线图预测肺腺癌患者表皮生长因子受体(EGFR)突变状态的价值。方法 回顾性分析济宁医学院附属医院2017年7月至2023年6月262例经病理证实且接受EGFR基因检测的原发性肺腺癌患者的治疗前胸部增强CT影像及临床资料,EGFR野生型(n=122)和突变型(n=140),采用分层抽样方式按照7∶3比例分为训练组(n=183)和测试组(n=79)。对CT图像进行标准化预处理、勾画感兴趣区(ROI)并提取影像组学特征,采用最小绝对收缩和选择算子(LASSO)算法进行降维和关键特征筛选,通过Logistic Regression(LR)机器学习方法建立标准化影像组学模型、临床模型及两者相结合的联合模型,计算影像组学分数(Rad-score)并绘制列线图。采用ROC曲线及Delong检验评估并比较不同模型的预测性能。结果 最终筛选出23个标准化增强CT影像组学特征和4个临床特征。标准化影像组学模型预测性能好于未标准化影像组学模型[曲线下面积(AUC):0.863 vs. 0.805,t=2.19,P<0.05]。联合模型和标准化影像组学的AUC高于临床模型(训练组:0.885,0.863 vs. 0.774,t=3.57、2.17,P<0.05;测试组:0.873,0.829 vs. 0.763,t=2.19、2.02,P<0.05)。根据Rad-score、年龄、性别、吸烟史及BMI绘制了影像组学列线图。结论 联合模型及标准化影像组学模型可有效预测肺腺癌患者治疗前EGFR突变状态,具有一定的临床价值。 |
| 英文摘要: |
| Objective To investigate the value of radiomics nomogram based on standardized pre-treatment chest enhanced CT in predicting the mutation status of epidermal growth factor receptor (EGFR) for patients with lung adenocarcinoma. Methods A retrospective analysis was conducted on pre-treatment chest enhanced CT images and clinical data of 262 patients from the affiliated hospital of Jining Medical University with pathologically proven primary lung adenocarcinoma who received EGFR gene testing, including EGFR wild type (n=122) and mutant type (n=140). The patients were divided into training group (n=183) and testing group (n=79) according to a ratio of 7∶3 by stratified sampling method. Standardized pre-processed the images, delineated the ROI and extracted the radiomics features. Least absolute shrinkage and selection operator (LASSO) algorithm was used to reduce the dimension and select key features. The standardized radiomics model, clinical model and the combined model were established by Logistic Regression (LR) machine learning method. Calculated the Rad-score and drew the nomogram. ROC curve and Delong were used to evaluate and compare the predictive performance of different models. Results 23 standardized enhanced CT radiomics features and 4 clinical features were selected. The predictive performance of standardized radiomics model was better than that of non-standardized radiomics model [area under curve (AUC):0.863 vs. 0.805,t=2.19,P<0.05]. The AUCs of the combined model and standardized radiomics model were higher than that of the clinical model (training group:0.885,0.863 vs. 0.774,t=3.57,2.17,P<0.05;testing group: 0.873,0.829 vs. 0.763,t=2.19,2.02, P<0.05). The radiomics nomogram was built based on Rad-score,age,sex, smoking history and BMI. Conclusions The combined model and standardized radiomics model could effectively predict the mutation status of EGFR gene in lung adenocarcinoma patients before treatment, providing valuable clinical insights. |
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