Graph Neural Network Model for Prediction of Non-Small Cell Lung Cancer Lymph Node Metastasis Using Protein–Protein Interaction Network and 18F-FDG PET/CT Radiomics

Abstract

The image texture features obtained from 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) images of non-small cell lung cancer (NSCLC) have revealed tumor heterogeneity. A combination of genomic data and radiomics may improve the prediction of tumor prognosis. This study aimed to predict NSCLC metastasis using a graph neural network (GNN) obtained by combining a protein–protein interaction (PPI) network based on gene expression data and image texture features. 18F-FDG PET/CT images and RNA sequencing data of 93 patients with NSCLC were acquired from The Cancer Imaging Archive. Image texture features were extracted from 18F-FDG PET/CT images and area under the curve receiver operating characteristic curve (AUC) of each image feature was calculated. Weighted gene co-expression network analysis (WGCNA) was used to construct gene modules, followed by functional enrichment analysis and identification of differentially expressed genes. The PPI of each gene module and genes belonging to metastasis-related processes were converted via a graph attention network. Images and genomic features were concatenated. The GNN model using PPI modules from WGCNA and metastasis-related functions combined with image texture features was evaluated quantitatively. Fifty-five image texture features were extracted from 18F-FDG PET/CT, and radiomic features were selected based on AUC (n = 10). Eighty-six gene modules were clustered by WGCNA. Genes (n = 19) enriched in the metastasis-related pathways were filtered using DEG analysis. The accuracy of the PPI network, derived from WGCNA modules and metastasis-related genes, improved from 0.4795 to 0.5830 (p < 2.75 × 10−12). Integrating PPI of four metastasis-related genes with 18F-FDG PET/CT image features in a GNN model elevated its accuracy over a without image feature model to 0.8545 (95% CI = 0.8401–0.8689, p-value < 0.02). This model demonstrated significant enhancement compared to the model using PPI and 18F-FDG PET/CT derived from WGCNA (p-value < 0.02), underscoring the critical role of metastasis-related genes in prediction model. The enhanced predictive capability of the lymph node metastasis prediction GNN model for NSCLC, achieved through the integration of comprehensive image features with genomic data, demonstrates promise for clinical implementation.

Application of Chest CT Imaging Feature Model in Distinguishing Squamous Cell Carcinoma and Adenocarcinoma of the Lung

“…For patients who are difficult to undergo surgery or biopsy, or whose pathology cannot be decided after biopsy, distinguishing between lung squamous cell carcinoma and adenocarcinoma is a widespread problem that troubles clinical There have been many literature reports on the application of imaging features for pathological prediction in the past. [5][6][7][8][9][10][11][12][13]17,[23][24][25][26][27][28][29][30][31][32][33] Han et al found that a model for distinguishing lung squamous cell carcinoma and lung adenocarcinoma was constructed using CT texture features, with an AUC of 0.803, 5 Zhang et al established a model based on the variation of iodine concentration in enhanced CT, with an AUC of 0.871, 7 Fukuma et al established a model based on the attenuation rate of enhanced CT, with an AUC of only 0.625, 8 Jiang et al established a model based on the perfusion parameters of brain metastases, with an AUC of 0.845, 9 Tomori et al established a model using a combination of PET-CT…”

Application of 3D, 4D, 5D, and 6D bioprinting in cancer research: what does the future look like?

“…Similarly, in non-small cell lung cancer (NSCLC), the use of data from 18F-fluorodeoxyglucose positron emission tomography (18F-FDG-PET) and a small residual convolutional network (SResCNN) model achieved high accuracy in mutation detection. 194 Regarding tumor classification, a deep learning tool, generative adversarial networks (GAN), was developed to identify mutations in IDH1 and IDH2 (isocitrate dehydrogenase genes) associated with gliomas using magnetic resonance imaging (MRI) scans. 195 Multiple databases are already accessible in repositories and platforms to be used by AI in the cancer field, for example, optum oncology electronic health records include data on genetic mutations.…”