改进晚期经典霍奇金淋巴瘤的风险分层:临床预测模型的关键分析。

IF 3.8 2区 医学 Q1 HEMATOLOGY
Oguzhan Koca, Ahmet Emre Eskazan
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引用次数: 0

摘要

经典霍奇金淋巴瘤(cHL)是一种高治愈率的血液系统恶性肿瘤;然而,根据临床和生物学因素,预后差异很大。为了加强风险分层,随着时间的推移,人们开发了几种临床预测模型,特别是晚期cHL。国际预后评分(IPS)于1998年推出,是第一个被广泛采用的模型,后来在2012年进行了改进(IPS更新),并在2015年进一步简化(IPS-3)。尽管这些模型具有预测功能,但由于cHL治疗的进步,这些模型的预测性能有所下降。作为回应,整体性联盟最近在2023年引入了晚期霍奇金淋巴瘤国际预后指数(A-HIPI)。与以前的模型不同,a - hipi包含连续变量,旨在提供更精确的风险评估。然而,其对老年患者的适用性仍不确定,需要进一步的验证研究。此外,现有的模型都没有纳入动态治疗反应标记,如中期正电子发射断层扫描/计算机断层扫描(PET/CT),这些标记显示出很强的预后价值。这篇综述全面讨论了这些预测模型的发展、优势和局限性、它们的临床意义以及整合动态生物标志物和治疗反应指标的未来改进的必要性。机器学习和多组学方法的结合可以进一步加强cHL的风险分层,提高治疗个性化,优化患者预后。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Refining the risk stratification in advanced-stage classical Hodgkin lymphoma: A critical analysis of clinical prediction models.

Classical Hodgkin lymphoma (cHL) is a haematological malignancy with high curability; however, prognosis varies significantly based on clinical and biological factors. To enhance risk stratification, several clinical prediction models have been developed over time, particularly for advanced-stage cHL. The International Prognostic Score (IPS), introduced in 1998, was the first widely adopted model, later refined in 2012 (updated IPS) and further simplified in 2015 (IPS-3). Despite their prognostic utility, these models have demonstrated declining predictive performance due to advancements in cHL treatment. In response, the HoLISTIC consortium recently introduced the Advanced-Stage Hodgkin Lymphoma International Prognostic Index (A-HIPI) in 2023. Unlike previous models, A-HIPI incorporates continuous variables, aiming to provide a more precise risk assessment. However, its applicability to older patients remains uncertain, necessitating further validation studies. Additionally, none of the existing models incorporate dynamic treatment response markers such as interim positron emission tomography/computed tomography (PET/CT), which have shown strong prognostic value. This review comprehensively discusses the evolution, strengths and limitations of these prediction models, their clinical implications and the necessity for future refinements integrating dynamic biomarkers and treatment response indicators. The integration of machine learning and multi-omics approaches could further enhance risk stratification, improve treatment personalization and optimize patient outcomes in cHL.

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来源期刊
CiteScore
8.60
自引率
4.60%
发文量
565
审稿时长
1 months
期刊介绍: The British Journal of Haematology publishes original research papers in clinical, laboratory and experimental haematology. The Journal also features annotations, reviews, short reports, images in haematology and Letters to the Editor.
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