Value of 18F-FDG PET/CT-Based Radiomics Nomogram to Predict Survival Outcomes and Guide Personalized Targeted Therapy in Lung Adenocarcinoma With EGFR Mutations

Bin Yang; Hengshan Ji; Jing Zhong; Lu Ma; Jian Zhong; Hao Dong; Changsheng Zhou; Shaofeng Duan; Chaohui Zhu; Jiahe Tian; Longjiang Zhang; Feng Wang; Hong Zhu; Guangming Lu

doi:10.3389/fonc.2020.567160

Value of ¹⁸F-FDG PET/CT-Based Radiomics Nomogram to Predict Survival Outcomes and Guide Personalized Targeted Therapy in Lung Adenocarcinoma With EGFR Mutations

Front Oncol. 2020 Nov 11:10:567160. doi: 10.3389/fonc.2020.567160. eCollection 2020.

Authors

Bin Yang¹, Hengshan Ji², Jing Zhong¹, Lu Ma¹, Jian Zhong¹, Hao Dong³, Changsheng Zhou¹, Shaofeng Duan⁴, Chaohui Zhu⁵, Jiahe Tian⁶, Longjiang Zhang¹, Feng Wang⁷, Hong Zhu², Guangming Lu¹

Affiliations

¹ Department of Medical Imaging, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China.
² Department of Nuclear Medicine, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China.
³ College of Medical Imaging, Xuzhou Medical University, Xuzhou, China.
⁴ Institute of Precision Medicine, GE Healthcare China, Shanghai, China.
⁵ Department of Nuclear Medicine, Peking Union Medical College Hospital, Beijing, China.
⁶ Department of Nuclear Medicine, The Chinese People's Liberation Army (PLA) General Hospital, Beijing, China.
⁷ Department of Nuclear Medicine, First People's Hospital of Nanjing, Nanjing, China.

Abstract

Objectives: To investigate the development and validation of a radiomics nomogram based on PET/CT for guiding personalized targeted therapy in patients with lung adenocarcinoma mutation(s) in the EGFR gene.

Methods: A cohort of 109 (77/32 in training/validation cohort) consecutive lung adenocarcinoma patients with an EGFR mutation was enrolled in this study. A total of 1672 radiomic features were extracted from PET and CT images, respectively. The least absolute shrinkage and selection operator (LASSO) Cox regression was used to select the radiomic features and construct the radiomics nomogram for the estimation of overall survival (OS), which was then assessed with respect to calibration and clinical usefulness. Patients with an EGFR mutation were divided into high- and low- risk groups according to their nomogram score. The treatment strategy for high- and low-risk groups was analyzed using Kaplan-Meier analysis and a log-rank test.

Results: The C-index of the radiomics nomogram for the prediction of OS in lung adenocarcinoma in patients with an EGFR mutation was 0.840 and 0.803 in the training and validation cohorts, respectively. Distant metastasis [(Hazard ratio, HR),1.80], metabolic tumor volume (MTV, HR, 1.62), and rad score (HR, 17.23) were the independent risk factors for patients with an EGFR mutation. The calibration curve showed that the predicted survival time was remarkably close to the actual time. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful. Targeted therapy for patients with high-risk EGFR mutations attained a greater benefit than other therapies (p < 0.0001), whereas the prognoses of the two therapies were similar in the low-risk group (p = 0.85).

Conclusions: Development and validation of a radiomics nomogram based on PET/CT radiomic features combined with clinicopathological factors may guide targeted therapy for patients with lung adenocarcinoma with EGFR mutations. This is conducive to the advancement of precision medicine.

Keywords: lung adenocarcinoma; nomogram; positron emission tomography/computed tomography; radiomics; targeted therapy.