PET/CT Based EGFR Mutation Status Classification of NSCLC Using Deep Learning Features and Radiomics Features

Frontiers in Pharmacology

Jingyi Wang¹  Fei Kang¹  Xin Cao²  Linzhi Su²  Haolin Wang²  Weicheng Huang²  Yuxiang Zhang²  Kang Li²  Fengjun Zhao² 

[1] Department of Nuclear Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, China;National and Local Joint Engineering Research Center for Cultural Heritage Digitization, Xi'an, China;School of Information Science and Technology, Northwest University, Xi'an, China;

关键词: radiomics;    deep learning;    EGFR;    PET/CT;    lung cancer;   

DOI  :  10.3389/fphar.2022.898529

来源: DOAJ

【 摘 要 】

Purpose: This study aimed to compare the performance of radiomics and deep learning in predicting EGFR mutation status in patients with lung cancer based on PET/CT images, and tried to explore a model with excellent prediction performance to accurately predict EGFR mutation status in patients with non-small cell lung cancer (NSCLC).Method: PET/CT images of 194 NSCLC patients from Xijing Hospital were collected and divided into a training set and a validation set according to the ratio of 7:3. Statistics were made on patients’ clinical characteristics, and a large number of features were extracted based on their PET/CT images (4306 radiomics features and 2048 deep learning features per person) with the pyradiomics toolkit and 3D convolutional neural network. Then a radiomics model (RM), a deep learning model (DLM), and a hybrid model (HM) were established. The performance of the three models was compared by receiver operating characteristic (ROC) curves, sensitivity, specificity, accuracy, calibration curves, and decision curves. In addition, a nomogram based on a deep learning score (DS) and the most significant clinical characteristic was plotted.Result: In the training set composed of 138 patients (64 with EGFR mutation and 74 without EGFR mutation), the area under the ROC curve (AUC) of HM (0.91, 95% CI: 0.86–0.96) was higher than that of RM (0.82, 95% CI: 0.75–0.89) and DLM (0.90, 95% CI: 0.85–0.95). In the validation set composed of 57 patients (32 with EGFR mutation and 25 without EGFR mutation), the AUC of HM (0.85, 95% CI: 0.77–0.93) was also higher than that of RM (0.68, 95% CI: 0.52–0.84) and DLM (0.79, 95% CI: 0.67–0.91). In all, HM achieved better diagnostic performance in predicting EGFR mutation status in NSCLC patients than two other models.Conclusion: Our study showed that the deep learning model based on PET/CT images had better performance than radiomics model in diagnosing EGFR mutation status of NSCLC patients based on PET/CT images. Combined with the most statistically significant clinical characteristic (smoking) and deep learning features, our hybrid model had better performance in predicting EGFR mutation types of patients than two other models, which could enable NSCLC patients to choose more personalized treatment schemes.

【 授权许可】

Unknown