基于CT的放射组学：肺腺癌中KRAS突变的潜在指标。

CT-based radiomics: A potential indicator of KRAS mutation in pulmonary adenocarcinoma.

作者信息

Mahmoud Menna Allah, Wu Sijun, Su Ruihua, Liufu Yuling, Wen Yanhua, Pan Xiaohuan, Guan Yubao

机构信息

Department of Radiology, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.

Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.

出版信息

Tumori. 2025 Apr;111(2):147-157. doi: 10.1177/03008916251314659. Epub 2025 Feb 2.

DOI:10.1177/03008916251314659

PMID:39894961

Abstract

PURPOSE

This study aimed to validate a CT-based radiomics signature for predicting Kirsten rat sarcoma (KRAS) mutation status in lung adenocarcinoma (LADC).

MATERIALS AND METHODS

A total of 815 LADC patients were included. Radiomics features were extracted from non-contrast-enhanced CT (NECT) and contrast-enhanced CT (CECT) images using Pyradiomics. CT-based radiomics were combined with clinical features to distinguish KRAS mutation status. Four feature selection methods and four deep learning classifiers were employed. Data was split into 70% training and 30% test sets, with SMOTE addressing imbalance in the training set. Model performance was evaluated using AUC, accuracy, precision, F1 score, and recall.

RESULTS

The analysis revealed that 10.4% of patients showed KRAS mutations. The study extracted 1061 radiomics features and combined them with 17 clinical features. After feature selection, two signatures were constructed using top 10, 20, and 50 features. The best performance was achieved using Multilayer Perceptron with 20 features. CECT, it showed 66% precision, 76% recall, 69% F1-score, 84% accuracy, and AUC of 93.3% and 87.4% for train and test sets, respectively. For NECT, accuracy was 85% and 82%, with AUC of 90.7% and 87.6% for train and test sets, respectively.

CONCLUSIONS

CT-based radiomics signature is a noninvasive method that can predict KRAS mutation status of LADC when mutational profiling is unavailable.

摘要

目的

本研究旨在验证一种基于CT的放射组学特征，用于预测肺腺癌（LADC）中的 Kirsten 大鼠肉瘤（KRAS）突变状态。

材料与方法

共纳入815例LADC患者。使用 Pyradiomics 从非增强CT（NECT）和增强CT（CECT）图像中提取放射组学特征。将基于CT的放射组学与临床特征相结合，以区分KRAS突变状态。采用了四种特征选择方法和四种深度学习分类器。数据被分为70%的训练集和30%的测试集，通过合成少数过采样技术（SMOTE）解决训练集中的不平衡问题。使用AUC、准确率、精确率、F1分数和召回率评估模型性能。

结果

分析显示，10.4%的患者存在KRAS突变。该研究提取了1061个放射组学特征，并将其与17个临床特征相结合。经过特征选择后，使用排名前10、20和50的特征构建了两个特征集。使用具有20个特征的多层感知器实现了最佳性能。对于CECT，其精确率为66%，召回率为76%，F1分数为69%，准确率为84%，训练集和测试集的AUC分别为93.3%和87.4%。对于NECT，准确率分别为85%和82%，训练集和测试集的AUC分别为90.7%和87.6%。