Affiliation:
1. School of Health Management China Medical University Shenyang Liaoning China
2. Department of Radiology The First Affiliated Hospital of China Medical University Liaoning Shenyang China
3. Department of Radiology Shengjing Hospital of China Medical University Shenyang Liaoning China
4. Department of Radiology Liaoning Cancer Hospital & Institute Shenyang Liaoning China
5. Library of China Medical University Shenyang Liaoning China
Abstract
AbstractBackgroundEpidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene homolog (KRAS) are mutually exclusive, and they are two important genes that are most prone to mutation in patients with non‐small cell lung cancer.PurposeThis retrospective study investigated the ability of radiomics to predict the mutation status of EGFR and KRAS in patients with non‐small cell lung cancer (NSCLC) and guide precision medicine.MethodsComputed tomography images of 1045 NSCLC patients from five different institutions were collected, and 1204 imaging features were extracted. In the training set (EGFR: 678, KRAS: 246), Max‐Relevance and Min‐Redundancy and least absolute shrinkage and selection operator logistic regression were used to screen radiomics features. The combination of selected radiomics features and clinical factors was used to establish the combined models in identifying EGFR and KRAS mutation status, respectively, through stepwise logistic regression. Then, on two independent external validation sets (EGFR: 203/164, KRAS: 123/95), the performance of each model was evaluated separately, and then the overall performance of predicting the two mutation states was calculated.ResultsIn the EGFR and KRAS groups, radiomics signatures comprised 14 and 10 radiomics features, respectively. They were mutually exclusive between the tumors with positive EGFR mutation and those with positive KRAS mutation in imaging phenotype. For the EGFR group, the area under the curve (AUC) of the combined model in the two validation sets was 0.871 (95% CI: 0.821–0.926) and 0.861 (95% CI: 0.802–0.911), respectively, whereas the AUC of the combined model in the two validation sets was 0.798 (95% CI: 0.739–0.850) and 0.778 (95% CI: 0.735–0.821), respectively, for the KRAS group. Considering both EGFR and KRAS, the overall precision, recall, and F1‐score of the combined model in the two validation sets were 0.704, 0.844, and 0.768, as well as 0.754, 0.693, and 0.722, respectively.ConclusionsOur study demonstrates the potential of radiomics in the non‐invasive identification of EGFR and KRAS mutation status, which may guide patients with non‐small cell lung cancer to choose the most appropriate personalized treatment. This method can be used when biopsy will bring unacceptable risk to patients with NSCLC.