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

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

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.

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.

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