Predicting epidermal growth factor receptor mutation status of lung adenocarcinoma based on PET/CT images using deep learning

Lele Huang; Weifang Kong; Yongjun Luo; Hongjun Xie; Jiangyan Liu; Xin Zhang; Guojin Zhang

doi:10.3389/fonc.2024.1458374

Predicting epidermal growth factor receptor mutation status of lung adenocarcinoma based on PET/CT images using deep learning

Front Oncol. 2024 Dec 13:14:1458374. doi: 10.3389/fonc.2024.1458374. eCollection 2024.

Authors

Lele Huang^#^{1

2

3}, Weifang Kong^#⁴, Yongjun Luo^#^{1

2

3}, Hongjun Xie⁵, Jiangyan Liu^{1

2

3}, Xin Zhang⁶, Guojin Zhang⁴

Affiliations

¹ Department of Nuclear Medicine, The Second Hospital and Clinical Medical School, Lanzhou University, Lanzhou, China.
² Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China.
³ Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.
⁴ Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
⁵ Department of Nuclear Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
⁶ Department of Pharmaceuticals Diagnosis, GE Healthcare, Beijing, China.

^# Contributed equally.

Abstract

Background: The aim of this study is to develop deep learning models based on ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomographic (¹⁸F-FDG PET/CT) images for predicting individual epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma (LUAD).

Methods: We enrolled 430 patients with non-small-cell lung cancer from two institutions in this study. The advanced Inception V3 model to predict EGFR mutations based on PET/CT images and developed CT, PET, and PET + CT models was used. Additionally, each patient's clinical characteristics (age, sex, and smoking history) and 18 CT features were recorded and analyzed. Univariate and multivariate regression analyses identified the independent risk factors for EGFR mutations, and a clinical model was established. The performance using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, recall, and F1-value was evaluated. The DeLong test was used to compare the predictive performance across various models.

Results: Among these four models, deep learning models based on CT and PET + CT exhibit the same predictive performance, followed by PET and the clinical model. The AUC values for CT, PET, PET + CT, and clinical models in the training set are 0.933 (95% CI, 0.922-0.943), 0.895 (95% CI, 0.882-0.907), 0.931 (95% CI, 0.921-0.942), and 0.740 (95% CI, 0.685-0.796), respectively; whereas those in the testing set are:0.921 (95% CI, 0.904-0.938), 0.876 (95% CI, 0.855-0.897), 0.921 (95% CI, 0.904-0.937), and 0.721 (95% CI, 0.629-0.814), respectively. The DeLong test results confirm that all deep learning models are superior to clinical one.

Conclusion: The PET/CT images based on trained CNNs effectively predict EGFR and non-EGFR mutations in LUAD. The deep learning predictive models could guide treatment options.

Keywords: 18F-FDG PET/CT; EGFR; deep learning; lung adenocarcinoma; mutation.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the National Natural Science Foundation of China (grant number 82202147); Research Fund of Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital (grant number 2022QN25); and Cuiying Scientific and Technological Innovation Program of the Second Hospital and Clinical Medical School, Lanzhou University (grant number CY2023-QN-A08).