AUTHOR=Chen Wufei , Hua Yanqing , Mao Dingbiao , Wu Hao , Tan Mingyu , Ma Weiling , Huang Xuemei , Lu Jinjuan , Li Cheng , Li Ming 

TITLE=A Computed Tomography-Derived Radiomics Approach for Predicting Uncommon EGFR Mutation in Patients With NSCLC

JOURNAL=Frontiers in Oncology

VOLUME=Volume 11 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.722106

DOI=10.3389/fonc.2021.722106

ISSN=2234-943X

ABSTRACT=Purpose: To develop a CT-based radiomics approach for identifying the uncommon EGFR mutation in patients with NSCLC.
Methods: This study involved 223 NSCLC patients (107 with uncommon EGFR mutation-positive and 116 with uncommon EGFR mutation-negative). A total of 1269 radiomics features were extracted from the non-contrast enhanced CT images after image segmentation and preprocessing. SVM algorithm was used for feature selection and model construction. ROC curve analysis was applied to evaluate the performance of the radiomics signature, clinicopathological model and the integrated model. A nomogram was developed and evaluated by using the calibration curve and DCA.
Results: The radiomics signature demonstrated a good performance for predicting the uncommon EGFR mutation in the training cohort (AUC=0.802, 95% CI 0.736-0.858) and verified in the validation cohort (AUC =0.791, 95% CI 0.642–0.899). The integrated model combined radiomics signature with clinicopathological independent predictors exhibited an incremental performance compared with the radiomics signature or the clinicopathological model. A nomogram based on the integrated model was developed and showed good calibration (Hosmer-Lemeshow test, P=0.92 in the training cohort and 0.608 in the validation cohort) and discrimination capacity (AUC of 0.816 in the training cohort and 0.795 in the validation cohort). 
Conclusion: Radiomics signature combined with the clinicopathological features can predict uncommon EGFR mutation in NSCLC patients.