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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.
摘要：
从非小细胞肺癌（NSCLC）的18F-氟代脱氧葡萄糖正电子发射断层扫描/计算机断层扫描（18F-FDGPET/CT）图像获得的图像纹理特征揭示了肿瘤异质性。基因组数据和影像组学的结合可以改善肿瘤预后的预测。本研究旨在使用基于基因表达数据和图像纹理特征的蛋白质-蛋白质相互作用（PPI）网络获得的图神经网络（GNN）来预测NSCLC转移。从癌症成像档案获得93例NSCLC患者的18F-FDGPET/CT图像和RNA测序数据。从18F-FDGPET/CT图像中提取图像纹理特征，并计算每个图像特征的曲线下面积（AUC）。加权基因共表达网络分析(WGCNA)构建基因模块,然后进行功能富集分析和差异表达基因的鉴定。每个基因模块的PPI和属于转移相关过程的基因通过图形注意力网络进行转换。图像和基因组特征连接在一起。使用来自WGCNA的PPI模块和结合图像纹理特征的转移相关函数对GNN模型进行了定量评估。从18F-FDGPET/CT中提取55个图像纹理特征，和基于AUC选择影像组学特征(n=10)。通过WGCNA对86个基因模块进行聚类。使用DEG分析过滤在转移相关途径中富集的基因(n=19)。PPI网络的准确性，来自WGCNA模块和转移相关基因，从0.4795提高到0.5830（p<2.75×10-12）。在GNN模型中整合四个转移相关基因的PPI与18F-FDGPET/CT图像特征，将其准确性高于无图像特征模型的0.8545（95％CI=0.8401-0.8689，p值<0.02）。与使用源自WGCNA的PPI和18F-FDGPET/CT（p值<0.02）的模型相比，该模型显示出显着增强，强调转移相关基因在预测模型中的关键作用。淋巴结转移预测GNN模型对NSCLC的预测能力增强，通过将综合图像特征与基因组数据集成来实现，证明了临床实施的希望。
