Suppr超能文献

使用肾脏评分系统预测术后并发症。

Predicting post-surgical complications using renal scoring systems.

作者信息

Golagha Mahshid, Hesswani Charles, Singh Shiva, Dehghani Firouzabadi Fatemeh, Sheikhy Ali, Koller Christopher, Linehan W Marston, Ball Mark W, Malayeri Ashkan A

机构信息

National Cancer Institute, Bethesda, USA.

National Institutes of Health, Bethesda, USA.

出版信息

Abdom Radiol (NY). 2025 Mar;50(3):1273-1284. doi: 10.1007/s00261-024-04627-8. Epub 2024 Oct 12.

Abstract

Current surgical approaches for renal malignancies primarily rely on qualitative factors such as patient preferences, surgeon experience, and hospital capabilities. Applying a quantitative method for consistent and reliable assessment of renal lesions would significantly enhance surgical decision-making and facilitate data comparison. Nephrometry scoring (NS) systems systematically evaluate and describe renal tumors based on their anatomical features. These scoring systems, including R.E.N.A.L., PADUA, MAP scores, C-index, CSA, and T-index, aim to predict surgical complications by evaluating anatomical and patient-specific factors. In this review paper, we explore the components and methodologies of these scoring systems, compare their effectiveness and limitations, and discuss their application in advancing patient care and optimizing surgical outcomes.

摘要

目前肾恶性肿瘤的手术方法主要依赖于患者偏好、外科医生经验和医院能力等定性因素。应用定量方法对肾病变进行一致且可靠的评估将显著提高手术决策水平并便于数据比较。肾计量评分(NS)系统基于肾肿瘤的解剖特征对其进行系统评估和描述。这些评分系统,包括R.E.N.A.L.、帕多瓦(PADUA)、MAP评分、C指数、CSA和T指数,旨在通过评估解剖因素和患者特定因素来预测手术并发症。在这篇综述论文中,我们探讨了这些评分系统的组成部分和方法,比较了它们的有效性和局限性,并讨论了它们在改善患者护理和优化手术结果方面的应用。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验