PO.CL12.04 · 临床研究
前列腺癌放射基因组学应用的系统评价
A systematic review of radiogenomic applications in prostate cancer
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
引言：前列腺癌（PCa）研究的一个优先事项是开发精确的风险分层工具，以便早期识别侵袭性肿瘤的男性，同时最大限度地减少对其他惰性疾病男性的过度治疗。由于影像学在PCa的诊断和管理中发挥着核心作用，放射基因组学作为一种个性化医疗方法已被探索用于改善风险分层。本研究旨在回顾并描述文献中报道的放射基因组学应用。
方法：使用检索词的各种变体对Medline、Embase和Cochrane数据库进行系统检索，纳入2010年后报道PCa放射基因组学应用的文章。如果报道了以下内容则纳入文章：（1）所用的基因组平台；（2）确定用于放射组学特征提取的感兴趣区域（ROI）的方法；（3）放射组学与基因组学之间的相关性分析。
结果：共筛查了267篇文章，经两位作者独立审查后，13篇符合纳入标准。大多数（n=10/13）报道了基于MRI的应用，涉及715名患者。其余模态包括超声（n=1）和PET扫描（n=2）。在评估MRI成像模态的研究中，大多数（7/10）将批量RNA测序与放射组学特征相关联。纹理放射组学特征（n=6）最常被报道与基因表达相关，其次是直方图（n=2）和体积特征（n=1）。在4项研究中，MRI放射组学与缺氧相关基因显著相关。在3项研究中观察到纹理特征（灰度共生矩阵）与ANGPTL4表达相关。放射基因组学模型预测存在临床显著PCa的中位AUC为0.746。仅有4项研究（基于MRI n=3，基于超声 n=1）对所开发的模型进行了外部验证。大多数（9/13）研究采用人工定性方法将成像位点与基因组分析的组织采集部位进行配准。
结论：前列腺癌放射基因组学研究在报告和设计上存在显著异质性。多项研究一致观察到提示MRI纹理放射组学特征存在关联的信号，但缺乏验证。
查看英文原文 English abstract
Introduction: A priority in prostate cancer (PCa) research is development of precise risk stratification tools to enable early identification of men with aggressive tumours while minimizing overtreatment of others with indolent disease. With imaging playing a central role in the diagnosis and management of PCa, radiogenomics has been explored as a personalised medicine approach to improve risk stratification. This study aims to review and describe radiogenomic applications reported in the literature.
Methods: Medline, Embase and Cochrane libraries were systematically searched using variations of search terms for articles reporting radiogenomic applications in PCa after 2010. Articles were included if the following were reported (1) genomic platform used; (2) method of determining region of interest (ROI) for radiomic feature extraction; (3) correlation analyses between radiomics and genomics.
Results: A total of 267 articles were screened and 13 met the inclusion criteria following independent review by two authors. Majority (n=10/13) reported MRI-based applications involving 715 patients. Remaining modalities included ultrasound (n=1) and PET scan (n=2). Most (7/10) studies evaluating an MRI imaging modality, correlated bulk RNA-sequencing with radiomic features. Textural radiomics (n=6) features were most commonly reported to correlate with gene expression followed by histogram (n=2) and volumetric features (n=1). MRI radiomics significantly correlated with hypoxia related genes in 4 studies. The textural feature (Gray Level Co-occurrence Matrix) was seen to correlate with ANGPTL4 expression in 3 studies. Median AUC for a radiogenomic model to predict presence of clinically significant PCa was 0.746. Only 4 studies (MRI-based n=3, ultrasound-based n=1) externally validated the developed model. Most (9/13) studies used a manual qualitative approach to register imaging loci with site of tissue acquisition for genomic analysis.
Conclusion: There is significant heterogeneity in the reporting and design of prostate cancer radiogenomic studies. A signal suggesting and association of MRI textural radiomic features have been consistently observed in several studies but lack validation
利益披露 Disclosure
T. Anbarasan, None..
M. Dichmont, None..
S. Figiel, None..
B. Papiez, None..
A. Lamb, None..
R. Bryant, None..
I. Mills, None.