PDF(Pubmed)
Abstract：
UNASSIGNED: Arteriovenous malformations (AVMs) are rare vascular anomalies involving a disorganization of arteries and veins with no intervening capillaries. In the past 10 years, radiomics and machine learning (ML) models became increasingly popular for analyzing diagnostic medical images. The goal of this review was to provide a comprehensive summary of current radiomic models being employed for the diagnostic, therapeutic, prognostic, and predictive outcomes in AVM management.
UNASSIGNED: A systematic literature review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, in which the PubMed and Embase databases were searched using the following terms: (cerebral OR brain OR intracranial OR central nervous system OR spine OR spinal) AND (AVM OR arteriovenous malformation OR arteriovenous malformations) AND (radiomics OR radiogenomics OR machine learning OR artificial intelligence OR deep learning OR computer-aided detection OR computer-aided prediction OR computer-aided treatment decision). A radiomics quality score (RQS) was calculated for all included studies.
UNASSIGNED: Thirteen studies were included, which were all retrospective in nature. Three studies (23%) dealt with AVM diagnosis and grading, 1 study (8%) gauged treatment response, 8 (62%) predicted outcomes, and the last one (8%) addressed prognosis. No radiomics model had undergone external validation. The mean RQS was 15.92 (range: 10-18).
UNASSIGNED: We demonstrated that radiomics is currently being studied in different facets of AVM management. While not ready for clinical use, radiomics is a rapidly emerging field expected to play a significant future role in medical imaging. More prospective studies are warranted to determine the role of radiomics in the diagnosis, prediction of comorbidities, and treatment selection in AVM management.
UNASSIGNED: A systematic literature review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, in which the PubMed and Embase databases were searched using the following terms: (cerebral OR brain OR intracranial OR central nervous system OR spine OR spinal) AND (AVM OR arteriovenous malformation OR arteriovenous malformations) AND (radiomics OR radiogenomics OR machine learning OR artificial intelligence OR deep learning OR computer-aided detection OR computer-aided prediction OR computer-aided treatment decision). A radiomics quality score (RQS) was calculated for all included studies.
UNASSIGNED: Thirteen studies were included, which were all retrospective in nature. Three studies (23%) dealt with AVM diagnosis and grading, 1 study (8%) gauged treatment response, 8 (62%) predicted outcomes, and the last one (8%) addressed prognosis. No radiomics model had undergone external validation. The mean RQS was 15.92 (range: 10-18).
UNASSIGNED: We demonstrated that radiomics is currently being studied in different facets of AVM management. While not ready for clinical use, radiomics is a rapidly emerging field expected to play a significant future role in medical imaging. More prospective studies are warranted to determine the role of radiomics in the diagnosis, prediction of comorbidities, and treatment selection in AVM management.
摘要：
■动静脉畸形（AVM）是罕见的血管异常，涉及动脉和静脉的紊乱，没有毛细血管介入。在过去的10年里,影像组学和机器学习（ML）模型在分析诊断医学图像方面变得越来越流行。这篇综述的目的是提供目前用于诊断的放射学模型的全面总结，治疗性的,预后，以及AVM管理中的预测性结果。
■根据系统评价和荟萃分析（PRISMA）2020指南的首选报告项目进行了系统文献综述，其中使用以下术语搜索PubMed和Embase数据库:(脑或脑或颅内或中枢神经系统或脊柱或脊柱)和(AVM或动静脉畸形或动静脉畸形)和(放射组学或放射基因组学或机器学习或人工智能或深度学习或计算机辅助检测或计算机辅助预测或计算机辅助治疗决策).计算所有纳入研究的影像组学质量评分(RQS)。
■纳入了13项研究，本质上都是回顾性的。三项研究（23％）涉及AVM诊断和分级，1项研究（8%）衡量治疗反应，8（62%）预测结果，最后一个（8％）解决了预后。没有任何影像组学模型经过外部验证。平均RQS为15.92（范围：10-18）。
■我们证明了目前正在AVM管理的不同方面研究影像组学。虽然还没有准备好临床使用，影像组学是一个迅速兴起的领域，有望在未来的医学成像中发挥重要作用。需要更多的前瞻性研究来确定影像组学在诊断中的作用，合并症的预测，以及AVM管理中的治疗选择。
■根据系统评价和荟萃分析（PRISMA）2020指南的首选报告项目进行了系统文献综述，其中使用以下术语搜索PubMed和Embase数据库:(脑或脑或颅内或中枢神经系统或脊柱或脊柱)和(AVM或动静脉畸形或动静脉畸形)和(放射组学或放射基因组学或机器学习或人工智能或深度学习或计算机辅助检测或计算机辅助预测或计算机辅助治疗决策).计算所有纳入研究的影像组学质量评分(RQS)。
■纳入了13项研究，本质上都是回顾性的。三项研究（23％）涉及AVM诊断和分级，1项研究（8%）衡量治疗反应，8（62%）预测结果，最后一个（8％）解决了预后。没有任何影像组学模型经过外部验证。平均RQS为15.92（范围：10-18）。
■我们证明了目前正在AVM管理的不同方面研究影像组学。虽然还没有准备好临床使用，影像组学是一个迅速兴起的领域，有望在未来的医学成像中发挥重要作用。需要更多的前瞻性研究来确定影像组学在诊断中的作用，合并症的预测，以及AVM管理中的治疗选择。
