Radiogenomic Profiling for Survival Analysis in Gastric Cancer: Integrating CT Imaging, Gene Expression, and Clinical Data.

IF 2.5 4区医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Molecular Imaging and Biology Pub Date : 2025-06-01 Epub Date: 2025-05-15 DOI:10.1007/s11307-025-02019-y

Anju R Nath, Kiruthika Thenmozhi, Jeyakumar Natarajan

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引用次数: 0

Abstract

Purpose: This study aims to integrate CT (Computed Tomography) radiomic features, gene expression profiles, and clinical data to identify radiogenomic biomarkers and improve overall survival prediction in gastric cancer (GC) patients.

Procedures: Quantitative radiomic analysis was performed on 37 GC CT images, alongside gene expression and clinical data, to identify biomarkers associated with overall survival. Tumor segmentation and radiomic feature extraction were followed by Pearson correlation for feature selection. Gene Set Enrichment Analysis (GSEA) identified pathways linking gene expression changes with radiomic features. Regression models were applied to explore the relationships between these pathways, radiomic features, and clinical data in survival prediction.

Results: A total of 107 radiomic features were extracted, with 46 radiomic features, 1,032 genes, and one clinical feature (age) selected for further analysis. GSEA identified 29 significant KEGG pathways, mainly involving immune, signal transduction, and catabolism pathways. In survival analysis, the SVM model performed best, identifying age, genes CSF1R and CXCL12, and image features ShortRunHighGrayLevelEmphasis and Idn (Inverse Difference Normalized) as independent predictors.

Conclusion: This study highlights the potential of integrating imaging, genomics, and clinical data for prognosis in GC patients, with identified genes suggesting new radiogenomic biomarker candidates for future evaluation.

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胃癌生存分析的放射基因组分析：整合CT成像、基因表达和临床数据。

目的：本研究旨在整合CT（计算机断层扫描）放射学特征、基因表达谱和临床数据，以识别放射基因组学生物标志物，提高胃癌（GC）患者的总生存预测。程序：对37张GC CT图像进行定量放射组学分析，并结合基因表达和临床数据，确定与总生存相关的生物标志物。肿瘤分割和放射学特征提取之后，使用Pearson相关性进行特征选择。基因集富集分析（GSEA）确定了将基因表达变化与放射学特征联系起来的途径。应用回归模型探讨这些途径、放射学特征和临床数据在生存预测中的关系。结果：共提取107个放射组学特征，其中46个放射组学特征，1032个基因，1个临床特征（年龄）进行进一步分析。GSEA鉴定出29条重要的KEGG通路，主要涉及免疫、信号转导和分解代谢途径。在生存分析中，SVM模型表现最好，识别出年龄、基因CSF1R和CXCL12，以及图像特征ShortRunHighGrayLevelEmphasis和Idn（逆差归一化）作为独立预测因子。结论：本研究强调了整合影像学、基因组学和临床数据对胃癌患者预后的潜力，已鉴定的基因为未来评估提供了新的放射基因组生物标志物候选物。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Molecular Imaging and Biology 医学-核医学

CiteScore

6.90

自引率

3.20%

发文量

审稿时长

3 months

期刊介绍： Molecular Imaging and Biology (MIB) invites original contributions (research articles, review articles, commentaries, etc.) on the utilization of molecular imaging (i.e., nuclear imaging, optical imaging, autoradiography and pathology, MRI, MPI, ultrasound imaging, radiomics/genomics etc.) to investigate questions related to biology and health. The objective of MIB is to provide a forum to the discovery of molecular mechanisms of disease through the use of imaging techniques. We aim to investigate the biological nature of disease in patients and establish new molecular imaging diagnostic and therapy procedures. Some areas that are covered are: Preclinical and clinical imaging of macromolecular targets (e.g., genes, receptors, enzymes) involved in significant biological processes. The design, characterization, and study of new molecular imaging probes and contrast agents for the functional interrogation of macromolecular targets. Development and evaluation of imaging systems including instrumentation, image reconstruction algorithms, image analysis, and display. Development of molecular assay approaches leading to quantification of the biological information obtained in molecular imaging. Study of in vivo animal models of disease for the development of new molecular diagnostics and therapeutics. Extension of in vitro and in vivo discoveries using disease models, into well designed clinical research investigations. Clinical molecular imaging involving clinical investigations, clinical trials and medical management or cost-effectiveness studies.