Performance of 18F-FDG PET/CT Radiomics for Predicting EGFR Mutation Status in Patients With Non-Small Cell Lung Cancer

Min Zhang; Yiming Bao; Weiwei Rui; Chengfang Shangguan; Jiajun Liu; Jianwei Xu; Xiaozhu Lin; Miao Zhang; Xinyun Huang; Yilei Zhou; Qian Qu; Hongping Meng; Dahong Qian; Biao Li

doi:10.3389/fonc.2020.568857

Performance of ¹⁸F-FDG PET/CT Radiomics for Predicting EGFR Mutation Status in Patients With Non-Small Cell Lung Cancer

Front Oncol. 2020 Oct 8:10:568857. doi: 10.3389/fonc.2020.568857. eCollection 2020.

Authors

Min Zhang¹, Yiming Bao², Weiwei Rui³, Chengfang Shangguan⁴, Jiajun Liu¹, Jianwei Xu², Xiaozhu Lin¹, Miao Zhang¹, Xinyun Huang¹, Yilei Zhou¹, Qian Qu¹, Hongping Meng¹, Dahong Qian^{1

2}, Biao Li¹

Affiliations

¹ Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
² Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China.
³ Department of Pathology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
⁴ Department of Oncology, Rujin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Abstract

Objective: To assess the performance of pretreatment ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) radiomics features for predicting EGFR mutation status in patients with non-small cell lung cancer (NSCLC).

Patients and methods: We enrolled total 173 patients with histologically proven NSCLC who underwent preoperative ¹⁸F-FDG PET/CT. Tumor tissues of all patients were tested for EGFR mutation status. A PET/CT radiomics prediction model was established through multi-step feature selection. The predictive performances of radiomics model, clinical features and conventional PET-derived semi-quantitative parameters were compared using receiver operating curves (ROCs) analysis.

Results: Four CT and two PET radiomics features were finally selected to build the PET/CT radiomics model. Compared with area under the ROC curve (AUC) equal to 0.664, 0.683 and 0.662 for clinical features, maximum standardized uptake values (SUV_max) and total lesion glycolysis (TLG), the PET/CT radiomics model showed better performance to discriminate between EGFR positive and negative mutations with the AUC of 0.769 and the accuracy of 67.06% after 10-fold cross-validation. The combined model, based on the PET/CT radiomics and clinical feature (gender) further improved the AUC to 0.827 and the accuracy to 75.29%. Only one PET radiomics feature demonstrated significant but low predictive ability (AUC = 0.661) for differentiating 19 Del from 21 L858R mutation subtypes.

Conclusions: EGFR mutations status in patients with NSCLC could be well predicted by the combined model based on ¹⁸F-FDG PET/CT radiomics and clinical feature, providing an alternative useful method for the selection of targeted therapy.

Keywords: 18F-fluorodeoxyglucose; epidermal growth factor receptor; lung cancer; positron emission tomography/computed tomography; radiomics.