Casado-Suela Miguel Ángel, Torres-Macho Juan, Prada-Alonso Jesús, Pastorín-Salis Rodrigo, Martínez de la Casa-Muñoz Ana, Ruiz-Navío Eva, Bustamante-Fermosel Ana, Franco-Moreno Anabel
Department of Internal Medicine, Hospital Universitario Infanta Leonor, Gran Via del Este Avenue, 80, 28031 Madrid, Spain.
Department of Medicine, Universidad Complutense de Madrid, 28040 Madrid, Spain.
Diagnostics (Basel). 2025 Aug 12;15(16):2022. doi: 10.3390/diagnostics15162022.
Inherent to the challenge of acute pulmonary embolism (APE), the breadth of presentation ranges from asymptomatic pulmonary emboli to sudden death. Risk stratification of patients with APE is mandatory for determining the appropriate therapeutic management approach. However, the optimal clinically most relevant combination of predictors of death remains to be determined. Radiomics is an emerging discipline in medicine that extracts and analyzes quantitative data from medical images using mathematical algorithms. In APE, these data can reveal thrombus characteristics that are not visible to the naked eye, which may help to more accurately identify patients at higher risk of early clinical deterioration or mortality. We conducted a scoping review to explore the current evidence on the prognostic performance of radiomic models in patients with APE. PubMed, Web of Science, EMBASE, and Scopus were searched for studies published between January 2010 and April 2025. Eligible studies evaluated the use of radiomics to predict adverse outcomes in patients with APE. The PROSPERO registration number is CRD420251083318. Nine studies were included in this review. There was significant heterogeneity in the methodology for feature selection and model development. Radiomic models demonstrated variable performance across studies. Models that combined radiomic features with clinical data tended to show better predictive accuracy. This scoping review underscores the potential of radiomic models, particularly when combined with clinical data, to improve risk stratification in patients with APE.
急性肺栓塞（APE）的挑战在于其临床表现范围广泛，从无症状性肺栓塞到猝死。对APE患者进行风险分层对于确定合适的治疗管理方法至关重要。然而，死亡预测指标的最佳临床最相关组合仍有待确定。放射组学是医学中一门新兴学科，它使用数学算法从医学图像中提取和分析定量数据。在APE中，这些数据可以揭示肉眼不可见的血栓特征，这可能有助于更准确地识别早期临床恶化或死亡风险较高的患者。我们进行了一项范围综述，以探索关于放射组学模型在APE患者中预后性能的现有证据。在PubMed、科学网、EMBASE和Scopus中检索了2010年1月至2025年4月发表的研究。符合条件的研究评估了放射组学在预测APE患者不良结局中的应用。PROSPERO注册号为CRD420251083318。本综述纳入了9项研究。在特征选择和模型开发方法上存在显著异质性。放射组学模型在各项研究中的表现各不相同。将放射组学特征与临床数据相结合的模型往往显示出更好的预测准确性。这项范围综述强调了放射组学模型的潜力，特别是与临床数据相结合时，可改善APE患者的风险分层。
