| 胡江,贺睿敏,程品晶,刘小敏,伍海彪,刘霖霏,王柏琦,成浩,杨骏辉.治疗前CT影像组学结合机器学习预测非小细胞肺癌患者EGFR突变亚型[J].中华放射医学与防护杂志,2023,43(5):386-392 |
| 治疗前CT影像组学结合机器学习预测非小细胞肺癌患者EGFR突变亚型 |
| Prediction of EGFR mutant subtypes in patients with non-small cell lung cancer by pre-treatment CT radiomics and machine learning |
| 投稿时间：2022-12-23 |
| DOI：10.3760/cma.j.cn112271-20221223-00495 |
| 中文关键词: 非小细胞肺癌 表皮生长因子受体 计算机断层扫描 影像组学 机器学习 |
| 英文关键词:Non-small cell lung cancer Epidermal growth factor receptor Computed tomography Radiomics Machine learning |
| 基金项目:湖南省高校创新平台开放基金项目(20K110) |
|
| 摘要点击次数: 1094 |
| 全文下载次数: 313 |
| 中文摘要: |
| 目的 探讨基于治疗前胸部平扫CT影像组学特征和临床特征结合机器学习算法预测非小细胞肺癌(NSCLC)患者表皮生长因子受体(EGFR)突变状态和突变亚型(19Del/21L858R)的可行性和价值。方法 回顾性分析南华大学附属第一医院和附属第二医院经活检病理证实和接受EGFR基因检测的280例NSCLC患者的治疗前胸部平扫CT和临床特征数据,其中EFGR突变患者为136例。由两位高年资影像和肿瘤医师勾画原发肺部大体肿瘤区域(GTV),然后提取851个影像组学特征,采用Spearman相关分析和RELIEFF算法筛选具有预测性的特征,两家医院分别为训练组和验证组。经特征选择的影像组学特征和临床特征构建临床-影像组学模型,并与单独采用影像组学特征和临床特征模型进行比较。采用序贯建模流程,使用支持向量机(SVM)建立机器学习模型预测EGFR突变状态和突变亚型。受试者工作曲线下面积(AUC-ROC)评估预测模型的诊断效能。结果 经特征筛选各有21个影像组学特征在预测EGFR突变和突变亚型时具有预测效能并用于建立影像组学模型。临床-影像组学模型表现出最好的预测效能,预测EGFR突变状态的模型AUC在训练组为0.956(95%CI:0.952~1.000)、验证组为0.961(95%CI:0.924~0.998),预测19Del/21L858R突变亚型的AUC在训练组为0.926(95%CI:0.893~0.959)、验证组为0.938(95%CI:0.876~1.000)。结论 基于治疗前CT影像组学和临床特征结合机器学习的序贯模型能够精准预测EGFR的突变状态和突变亚型。 |
| 英文摘要: |
| Objective To evaluate the feasibility and clinical value of pre-treatment non-enhanced chest CT radiomics features and machine learning algorithm to predict the mutation status and subtype (19Del/21L858R) of epidermal growth factor receptor (EGFR) for patients with non-small cell lung cancer (NSCLC). Methods This retrospective study enrolled 280 NSCLC patients from first and second affiliated hospital of University of South China who were confirmed by biopsy pathology, gene examination, and have pre-treatment non-enhanced CT scans. There are 136 patients were confirmed EGFR mutation. Primary lung gross tumor volume was contoured by two experienced radiologists and oncologists, and 851 radiomics features were subsequently extracted. Then, spearman correlation analysis and RELIEFF algorithm were used to screen predictive features. The two hospitals were training and validation cohort, respectively. Clinical-radiomics model was constructed using selected radiomics and clinical features, and compared with models built by radiomics features or clinical features respectively. In this study, machine learning models were established using support vector machine (SVM) and a sequential modeling procedure to predict the mutation status and subtype of EGFR. The area under receiver operating curve (AUC-ROC) was employed to evaluate the performances of established models. Results After feature selection, 21 radiomics features were found to be efffective in predicting EGFR mutation status and subtype and were used to establish radiomics models. Three types models were established, including clinical model, radiomics model, and clinical-radiomics model. The clinical-radiomics model showed the best predictive efficacy, AUCs of predicting EGFR mutation status for training dataset and validation dataset were 0.956 (95%CI: 0.952-1.000) and 0.961 (95%CI: 0.924-0.998), respectively. The AUCs of predicting 19Del/L858R mutation subtype for training dataset and validation dataset were 0.926 (95%CI: 0.893-0.959), 0.938 (95%CI: 0.876-1.000), respectively. Conclusions The constructed sequential models based on integration of CT radiomics, clinical features and machine learning can accurately predict the mutation status and subtype of EGFR. |
| HTML 查看全文 查看/发表评论 下载PDF阅读器 |
| 关闭 |
|
|
|