Clinical Investigation
Value of 18F-FDG PET/CT radiomics for predicting KRAS gene mutations in non-small cell lung cancer
Wang Jingyi, Huang Weicheng, Cao Xin, Zhang Yuxiang, Yang Weidong, Kang Fei, Wang Jing
Published 2023-07-25
Cite as Chin J Nucl Med Mol Imaging, 2023, 43(7): 391-396. DOI: 10.3760/cma.j.cn321828-20211130-00423
Abstract
ObjectiveTo assess the predictive efficacy of 18F-FDG PET/CT-based radiomics models for the mutation status of Kirsten rats sarcoma viral oncogene homolog (KRAS) in patients with non-small cell lung cancer (NSCLC).
MethodsFrom January 2016 to January 2021, the 18F-FDG PET/CT images and KRAS testing of 258 NSCLC patients (180 males, 78 females; age: 33-91 years) in the First Affiliated Hospital of the Air Force Military Medical University were retrospectively analyzed. Patients were randomly divided into training set (n=180) and validation set (n=78) in the ratio of 7∶3. Tumor lesions on PET and CT images were drawn respectively, and the radiomics features of PET and CT lesions were extracted. The radiomics features were screened by least absolute shrinkage and selection operator (LASSO). CT radiomics score (RS) model, PET/CT RS model and composite models of PET/CT RS combined with screened clinical information were eventually developed. ROC curves were used to assess the predictive efficacy of these models.
ResultsThe CT RS model included 4 radiomics features and the PET/CT RS model included 4 CT radiomics features and 8 PET radiomics features. The CT RS model and the PET/CT RS model both had significant differences in RS between KRAS mutant and wild-type patients in the training set and validation set (z values: from -8.30 to -4.10, all P<0.001). In predicting KRAS mutations, the composite model of PET/CT RS combined with age showed AUCs of 0.879 and 0.852 in the training and validation sets respectively, which were higher than those of the CT RS model (0.813 and 0.770) and the PET/CT RS model (0.858 and 0.834). The accuracy of the composite model of PET/CT RS combined with age were 81.67%(147/180) and 79.49%(62/78) in the training set and validation set respectively, which were also higher than those of the CT RS model (75.00%(135/180) and 74.36%(58/78)) and the PET/CT RS model (78.89%(142/180) and 78.21%(61/78)).
ConclusionModels based on radiomics features can predict KRAS gene mutation status, and the composite model combining PET/CT RS and age can improve the prediction performance.
Key words:
Carcinoma, non-small-cell lung; Mutation; Genes, ras; Positron-emission tomography; Tomography, X-ray computed; Fluorodeoxyglucose F18; Forecasting; Radiomics
Contributor Information
Wang Jingyi
Department of Nuclear Medicine, the First Affiliated Hospital of the Air Force Military Medical University, Xi′an 710032, China
Huang Weicheng
School of Information Science and Technology, Northwest University, Xi′an 710127, China
Cao Xin
School of Information Science and Technology, Northwest University, Xi′an 710127, China
Zhang Yuxiang
School of Information Science and Technology, Northwest University, Xi′an 710127, China
Yang Weidong
Department of Nuclear Medicine, the First Affiliated Hospital of the Air Force Military Medical University, Xi′an 710032, China
Kang Fei
Department of Nuclear Medicine, the First Affiliated Hospital of the Air Force Military Medical University, Xi′an 710032, China
Wang Jing
Department of Nuclear Medicine, the First Affiliated Hospital of the Air Force Military Medical University, Xi′an 710032, China
