CT-based radiomics for predicting the response of non-small cell lung cancer to EGFR-TKIs

Li Xiaoying; Yang Chaofeng; Li Yang

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CT-based radiomics for predicting the response of non-small cell lung cancer to EGFR-TKIs

更新时间：2025-07-31

- CT-based radiomics for predicting the response of non-small cell lung cancer to EGFR-TKIs
- Journal of Chongqing Medical University Pages: 1-8(2025)
- 作者机构：
  
  川北医学院附属医院放射科，南充 637000
- 作者简介：
- 基金信息：
- DOI：
  CLC： R730.7
- Received：05 March 2025，
  
  Published Online：31 July 2025，
- 稿件说明：
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李小英,杨朝凤,李杨.CT影像组学预测非小细胞肺癌EGFR-TKIs疗效评估的研究[J].重庆医科大学学报,

Li Xiaoying,Yang Chaofeng,Li Yang.CT-based radiomics for predicting the response of non-small cell lung cancer to EGFR-TKIs[J].Journal of Chongqing Medical University,
李小英,杨朝凤,李杨.CT影像组学预测非小细胞肺癌EGFR-TKIs疗效评估的研究[J].重庆医科大学学报, DOI：10.13406/j.cnki.cyxb..

Li Xiaoying,Yang Chaofeng,Li Yang.CT-based radiomics for predicting the response of non-small cell lung cancer to EGFR-TKIs[J].Journal of Chongqing Medical University, DOI：10.13406/j.cnki.cyxb..

摘要

目的

探究基于X线计算机体层摄影（computed tomography，CT）的影像组学预测非小细胞肺癌（non-small cell lung cancer，NSCLC）经表皮生长因子受体酪氨酸激酶抑制剂（epidermal growth factor receptor tyrosine kinase inhibitors，EGFR-TKIs）治疗后无进展生存期（progression-free survival，PFS）的价值。

方法

回顾性收集经EGFR-TKIs治疗的NSCLC患者135例，按照7：3的比例随机分为训练组（96例）及测试组（39例）。首先，从肿瘤影像中提取影像组学特征，在训练组中采用观察者内观察者间相关性、单因素COX回归及最小绝对收缩与选择算子的COX回归分析筛选最具预测价值的影像组学特征。同时，采用单因素及多因素COX回归分析筛选具有独立预测价值的临床特征。然后，基于筛选的特征建立影像组学模型、临床模型及联合模型。评估并比较各模型的区分度及临床获益，最后进行风险分层生存分析。

结果

基于训练组最终筛选出12个与PFS最为相关的影像组学特征。年龄、平均红细胞体积、中性粒细胞与淋巴细胞比率是PFS的临床独立危险因素。在训练组中，影像组学模型、临床模型、联合模型的C-index分别是0.690（95%CI=0.616~0.763）、0.649（95%CI=0.582~0.716）、0.752（95%CI=0.688~0.815）；在测试组中，C-index分别是0.711（95%CI=0.593~0.830）、0.574（95%CI=0.428~0.719）、0.739（95%CI=0.633~0.845）；联合模型性能最高。临床决策曲线分析（decision curve analysis，DCA）显示联合模型与影像组学模型的临床净获益接近，但高于临床模型。Kaplan-Meier曲线和Log-rank 检验显示联合模型具备对患者风险分层生存分析的能力。

结论

CT影像组学对接受EGFR-TKIs治疗的NSCLC患者的PFS具有良好的预测性能，对临床医师筛选快速进展人群、改善患者预后具有重要意义。

Abstract

Objective

To explore the value of radiomics based on X-ray computed tomography（CT） in predicting the progression-free survival（PFS） of patients with non-small cell lung cancer（NSCLC） treated with epidermal growth factor receptor tyrosine kinase inhibitors （EGFR-TKIs）.

Methods

A retrospective study was conducted on 135 NSCLC patients who were treated with EGFR-TKIs. The patients were randomly divided into a training set（96 cases） and a testing set（39 cases） at a 7：3 ratio. Firstly，radiomics features were extracted from tumor medical images. In the training set，optimal predictive radiomics features were selected by intra-class and inter-class correlation coefficient，univariate and least absolute shrinkage and selection operator COX regression analyses. Meanwhile，clinical risk factors with independent predictive value were selected by univariate and multivariate COX regression analyses. Then，radiomics，clinical，and combined models were constructed based on the selected features. The discrimination performance and clinical benefit of each model were evaluated and compared. Finally，risk stratification based on survival analysis was performed.

Results

Based on the training set，12 radiomic features significantly associated with PFS were selected. Age，mean corpuscular volume，and neutrophil-to-lymphocyte ratio were independent clinical risk factors for PFS. In the training set，the C-index of the radiomics model，clinical model，and combined model was 0.690（95%CI=0.616-0.763），0.649（95%CI=0.582-0.716），and 0.752（95%CI=0.688-0.815），respectively. In the testing set，the C-index values were 0.711（95%CI=0.593-0.830），0.574（95%CI=0.428-0.719），and 0.739（95%CI=0.633-0.845），respectively. The combined model had the highest performance. The decision curve analysis showed that the net clinical benefit of the combined model was similar to radiomics model，but higher than clinical model. The Kaplan-Meier curve and Log-rank test showed that the combined model had the ability of risk stratification based on survival analysis.

Conclusion

CT-based radiomics can effectively predict the PFS of NSCLC patients treated with EGFR-TKIs，which is significant for clinicians to identify rapid progressors and improve the prognosis of patients.

关键词

Keywords

references

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