Intratumoral and peritumoral radiomics based on 18F-FDG PET-CT for predicting epidermal growth factor receptor mutation status in lung adenocarcinoma
10.3760/cma.j.cn112149-20240319-00143
- VernacularTitle:基于 18F-FDG PET-CT的瘤内及瘤周影像组学预测肺腺癌表皮生长因子受体突变状态
- Author:
Jianxiong GAO
1
;
Xinyu GE
;
Rong NIU
;
Yunmei SHI
;
Zhenxing JIANG
;
Yan SUN
;
Jinbao FENG
;
Yuetao WANG
;
Xiaonan SHAO
Author Information
1. 苏州大学附属第三医院 常州市第一人民医院核医学科 苏州大学核医学与分子影像临床转化研究所 常州市分子影像重点实验室，常州 213003
- Keywords:
Lung neoplasms;
Positron emission tomography;
Tomography, X-ray computed;
Radiomics;
Epidermal growth factor receptor
- From:
Chinese Journal of Radiology
2024;58(10):1042-1049
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the value of intratumoral and peritumoral radiomics models based on 18F-FDG PET-CT in predicting epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma and interpret peritumoral radiomics features. Methods:This study was a cross-sectional study. Patients with lung adenocarcinoma who underwent 18F-FDG PET-CT at the Third Affiliated Hospital of Soochow University between January 2018 and April 2022 were retrospectively collected and samplied into a training set (309 cases) and a test set (206 cases) in a 6∶4 ratio randomly. Radiomics features were extracted from the intratumoral and peritumoral regions of interest based on PET and CT images, respectively, and the optimal feature sets were selected. Radiomics models were established using the XGBoost algorithm, and radiomics scores (intratumoral CT label, peritumoral CT label, intratumoral PET label, peritumoral PET label) were calculated. Logistic regression analysis was used to construct a clinical model and a combined model (incorporating PET-CT intratumoral and peritumoral radiomics, clinical features, and CT semantic features). The predictive performance of the models was evaluated using receiver operating characteristic curves and the area under the curve (AUC). Unsupervised clustering, Spearman correlation analysis, and visualization methods were used for the interpretability of peritumoral radiomics features. Results:In both the training and test sets, the AUC value of CT peritumoral labels was greater than that of CT intratumoral labels for predicting EGFR mutation status in lung adenocarcinoma (training set: Z=3.84, P<0.001; test set: Z=1.99, P=0.046). In the test set, the AUC value of PET intratumoral labels (0.684) was slightly higher than that of PET peritumoral labels (0.672) for predicting EGFR mutation status, but the difference was not statistically significant ( P>0.05). The combined model had the highest AUC value for predicting EGFR mutation status of lung adenocarcinoma in both the training and test sets and was significantly better than the clinical model (training set: Z=6.52, P<0.001; test set: Z=2.31, P=0.021). Interpretability analysis revealed that CT peritumoral radiomics features were correlated with CT shape features, and there were significant differences in CT peritumoral features between different EGFR mutation statuses. Conclusions:The value of CT peritumoral labels is superior to that of CT intratumoral labels in predicting EGFR mutation status in lung adenocarcinoma. The predictive performance of the model can be improved by combining PET-CT intratumoral and peritumoral radiomics, clinical features, and CT semantic features.
