Prediction of EGFR mutations in lung cancer based on multi-sequence spectral CT using radiomics and deep learning

Shen Jingjing; Zhang Rui; Li Qiong; Gao Xin

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Prediction of EGFR mutations in lung cancer based on multi-sequence spectral CT using radiomics and deep learning

更新时间：2025-09-03

- Prediction of EGFR mutations in lung cancer based on multi-sequence spectral CT using radiomics and deep learning
- Journal of Chongqing Medical University Pages: 1-8(2025)
- 作者机构：
  
  1.中国科学技术大学生物医学工程学院（苏州）/生命科学与医学部，合肥　230026
  2.中国科学院苏州生物医学工程技术研究所/生医工人工智能研究室，苏州　215163
  3.中山大学肿瘤防治中心影像科，广州　510060
- 作者简介：
- 基金信息：
- DOI：
  CLC： R734.2
- Received：16 April 2025，
  
  Published Online：03 September 2025，
- 稿件说明：
移动端阅览
沈菁菁,张睿,李琼等.基于多序列光谱CT影像组学与深度学习的肺癌EGFR突变预测[J].重庆医科大学学报,

Shen Jingjing,Zhang Rui,Li Qiong,et al.Prediction of EGFR mutations in lung cancer based on multi-sequence spectral CT using radiomics and deep learning[J].Journal of Chongqing Medical University,
沈菁菁,张睿,李琼等.基于多序列光谱CT影像组学与深度学习的肺癌EGFR突变预测[J].重庆医科大学学报, DOI：10.13406/j.cnki.cyxb..

Shen Jingjing,Zhang Rui,Li Qiong,et al.Prediction of EGFR mutations in lung cancer based on multi-sequence spectral CT using radiomics and deep learning[J].Journal of Chongqing Medical University, DOI：10.13406/j.cnki.cyxb..

摘要

目的

探究多序列光谱计算机断层扫描（computed tomography，CT）对于术前无创预测肺癌患者表皮生长因子受体（epidermal growth factor receptor，EGFR）突变状态的价值。

方法

回顾性收集2021年4月至2022年5月通过光谱CT增强扫描的236例肺癌患者的数据，按照8∶2的比例，随机划分训练集（188例）和验证集（48例）。基于常规CT图像及光谱CT的6种序列图像提取并筛选肿瘤的影像组学和深度学习特征，采用最小绝对收缩和选择算子以及决策融合策略建立了多个预测模型，并通过融合临床特征构建了临床-影像模型。采用受试者工作特征曲线评价模型预测效能，DeLong检验比较曲线下面积（area under the curve，AUC）。

结果

光谱CT模型（AUC=0.801）相比常规CT模型（AUC=0.705）的预测效能有显著提升（

=0.034）。在所有构建的模型中，临床-影像模型（AUC=0.804）预测效能最高。

结论

相比常规CT，多序列的光谱CT在EGFR突变预测方面更具优势，其联合常规CT和临床特征构建的模型，有望为无创预测EGFR突变提供新思路，提升诊断效率。

Abstract

Objective

To explore the value of multi-sequence spectral computed tomography（CT） in non-invasive preoperative prediction of epidermal growth factor receptor（EGFR） mutation status in patients with lung cancer.

Methods

The data of 236 patients with lung cancer who underwent enhanced spectral CT scan from April 2021 to May 2022 were retrospectively collected，and the patients were randomly divided into a training set（

=188） and a validation set（

=48） at a ratio of 8∶2. Radiomic and deep learning features of tumors were extracted and selected based on conventional CT images and six sequences of spectral CT images. Multiple prediction models were established using the least absolute shrinkage and selection operator and decision fusion strategy，and the clinical-imaging model was constructed by combining clinical features. Receiver operating characteristic curve was used to evaluate the prediction performance of each model. DeLong test was used to compare the differences in area under the curve（AUC）.

Results

The prediction performance of the spectral CT model（AUC=0.801） was significantly improved compared with the conventional CT model（AUC=0.705）（

=0.034）. Among all the constructed models，the clinical-imaging model（AUC=0.804） had the highest prediction performance.

Conclusion

Compared with conventional CT，multi-sequence spectral CT has an advantage in EGFR mutation prediction. The model constructed by combining multi-sequence spectral CT with conventional CT and clinical features is expected to provide a new idea for non-invasive prediction of EGFR mutations and improve diagnostic efficiency.

关键词

Keywords

references

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