Radiomics for Detection of the EGFR Mutation in Liver Metastatic NSCLC

Rationale and Objectives: The research aims to investigate whether MRI radiomics on hepatic metastasis from primary nonsmall cell lung cancer (NSCLC) can be used to differentiate patients with epidermal growth factor receptor (EGFR) mutations from those with EGFR wild-type, and develop a prediction model based on combination of primary tumor and the metastasis. Materials and Methods: A total of 130 patients were enrolled between Aug. 2017 and Dec. 2021, all pathologically confirmed harboring hepatic metastasis from primary NSCLC. The pyradiomics was used to extract radiomics features from intra-and peritumoral areas of both primary tumor and metastasis. The least absolute shrinkage and selection operator (LASSO) regression was applied to identify most predictive features and to develop radiomics signatures (RSs) for prediction of the EGFR mutation status. The receiver operating charac-teristic (ROC) curve analysis was performed to assess the prediction capability of the developed RSs. Results: A RS-Primary and a RS-Metastasis were derived from the primary tumor and metastasis, respectively. The RS-Combine by com-bination of the primary tumor and metastasis achieved the highest prediction performance in the training (AUCs, RS-Primary vs. RS-Metastasis vs. RS-Combine, 0.826 vs. 0.821 vs. 0.908) and testing (AUCs, RS-Primary vs. RS-Metastasis vs. RS-Combine, 0.760 vs. 0.791 vs. 0.884) set. The smoking status showed significant difference between EGFR mutant and wild-type groups (p < 0.05) in the train-ing set. Conclusion: The study indicates that hepatic metastasis-based radiomics can be used to detect the EGFR mutation. The developed mul-tiorgan combined radiomics signature may be helpful to guide individual treatment strategies for patients with metastatic NSCLC.

Automated summary

The study aims to investigate the use of MRI radiomics on hepatic metastasis from primary NSCLC to differentiate patients with EGFR mutations from those with EGFR wild-type and develop a prediction model. Radiomics features were extracted from the primary tumor and metastasis, and a radiomics signature was developed to predict the EGFR mutation status. The study concludes that radiomics based on hepatic metastasis can be used to detect EGFR mutation, and a multiorgan combined radiomics signature may guide individual treatment strategies for patients with metastatic NSCLC.