Dipartimento di Scienze Biomediche Avanzate, Università degli Studi di Napoli "Federico II", Naples, Italy.
Dipartimento delle Scienze Mediche, Motorie e del Benessere, Università Degli Studi di Napoli Parthenope, Naples, Italy.
J Transl Med. 2024 Nov 30;22(1):1088. doi: 10.1186/s12967-024-05896-z.
There is a growing interest on the association of radiomic features with genomic signatures in oncology. Using computational methods, quantitative radiomic data are extracted from various imaging techniques and integrated with genomic information to construct predictive models aimed at advancing diagnostic strategies in cancer patient management. In this context, the aim of this systematic review was to assess the current knowledge on potential application of this association in patients with thyroid cancer (TC).
A comprehensive literature review was conducted by querying three different databases (PubMed, Scopus and Embase) to identify studies published until June 2024, focusing on the potential association of radiomics and genomics in patients with TC. Pertinent data were subsequently extracted, and the methodological quality was evaluated using the A MeaSurement Tool to Assess Systematic Reviews 2 (AMSTAR 2).
From the initial analysis, a total of 853 papers were identified. After removing duplicates and applying eligibility criteria, we ultimately evaluated 7 articles. It was observed that the most commonly utilized imaging technique for TC examination was ultrasound (US), followed by computed tomography and magnetic resonance imaging. Regarding genomic techniques, sequencing and polymerase chain reaction were the most commonly employed methods to validate genetic alterations. The association of radiomic features with genomic signatures demonstrated promising performance in predicting metastasis to the cervical lymph nodes or RET/PTC rearrangements. The effectiveness of models based on US-radiomic features in predicting BRAF mutation in patients with TC requires further investigation.
Although this systematic review has several limitations, primarily related to the limited amount of available literature data, the association of radiomic features with genomic signatures demonstrates a potential as non-invasive tool to enhance the accuracy and efficacy of TC diagnosis and prognosis. PROSPERO registration number: CRD42024572292.
放射组学特征与肿瘤学中的基因组特征的相关性越来越受到关注。使用计算方法，从各种成像技术中提取定量放射组学数据，并与基因组信息集成，构建预测模型，旨在推进癌症患者管理中的诊断策略。在这种情况下，本系统评价的目的是评估这种相关性在甲状腺癌 (TC) 患者中的潜在应用的现有知识。
通过查询三个不同的数据库（PubMed、Scopus 和 Embase）进行全面的文献综述，以确定截至 2024 年 6 月发表的研究，重点关注放射组学和基因组学在 TC 患者中的潜在相关性。随后提取相关数据，并使用评估系统评价的测量工具 2（AMSTAR 2）评估方法学质量。
从最初的分析中，共确定了 853 篇论文。在去除重复项并应用入选标准后，我们最终评估了 7 篇文章。观察到用于 TC 检查的最常用成像技术是超声 (US)，其次是计算机断层扫描和磁共振成像。关于基因组技术，测序和聚合酶链反应是验证遗传改变最常用的方法。放射组学特征与基因组特征的相关性在预测颈部淋巴结转移或 RET/PTC 重排方面表现出有前途的性能。基于 US-放射组学特征的模型在预测 TC 患者 BRAF 突变中的有效性需要进一步研究。
尽管本系统评价存在一些局限性，主要与可用文献数据量有限有关，但放射组学特征与基因组特征的相关性显示出作为一种非侵入性工具的潜力，可提高 TC 诊断和预后的准确性和疗效。PROSPERO 注册号：CRD42024572292。
