肢端肥大症患者对第一代生长抑素受体配体治疗反应的预测因素。

IF 4.7 3区 医学 Q1 MEDICINE, RESEARCH & EXPERIMENTAL
Montserrat Marques-Pamies , Joan Gil , Mireia Jordà , Manel Puig-Domingo
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引用次数: 0

摘要

背景和目的:第一代生长抑素受体配体(fgsrl)反应的预测因子在肢端肥大症中已经研究了30多年,但它们仍然没有被推荐到临床指南中。难道没有足够的证据支持它们的使用吗?本系统综述旨在描述目前对fgsrl反应的主要预测因素的了解,并讨论其目前的用途以及未来的研究方向。方法:在Scopus和PubMed数据库中系统检索功能、影像学和分子预测因素。结果:共检出282篇,其中纳入64篇。其中大多数是1990年至2023年间进行的回顾性研究,重点关注肢端肥大症患者对fgsrl的预测反应。预测因素的有效性得到证实,大多数重复的因素都有良好的反应,特别是急性奥曲肽试验中GH最低点低,T2 MRI低,生长抑素受体2 (SSTR2)和E-cadherin高表达,以及密集的颗粒状模式。即使这些生物标记物是相互关联的,这种关联也是相当不均匀的。使用经典的统计方法,很难确定值得在临床指南中推荐的可靠和可推广的临界值。涉及组学的机器学习模型是迄今为止实现最高精度值的一种有前途的方法。结论:这项调查证实了一个足够强大的证据水平,以应用预测因素的知识,以提高治疗决策过程的效率。人工智能在这一领域的侵入正在为这些长期存在的问题提供明确的答案,这些问题可能会改变临床指导方针,并使个性化医疗成为现实。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predictors of Response to Treatment with First-Generation Somatostatin Receptor Ligands in Patients with Acromegaly

Background and Aims

Predictors of first-generation somatostatin receptor ligands (fgSRLs) response in acromegaly have been studied for over 30 years, but they are still not recommended in clinical guidelines. Is there not enough evidence to support their use?

This systematic review aims to describe the current knowledge of the main predictors of fgSRLs response and discuss their current usefulness, as well as future research directions.

Methods

A systematic search was performed in the Scopus and PubMed databases for functional, imaging, and molecular predictive factors.

Results

A total of 282 articles were detected, of which 64 were included. Most of them are retrospective studies performed between 1990 and 2023 focused on the predictive response to fgSRLs in acromegaly. The usefulness of the predictive factors is confirmed, with good response identified by the most replicated factors, specifically low GH nadir in the acute octreotide test, T2 MRI hypointensity, high Somatostatin receptor 2 (SSTR2) and E-cadherin expression, and a densely granulated pattern. Even if these biomarkers are interrelated, the association is quite heterogeneous. With classical statistical methods, it is complex to define reliable and generalizable cut-off values worth recommending in clinical guidelines. Machine-learning models involving omics are a promising approach to achieve the highest accuracy values to date.

Conclusions

This survey confirms a sufficiently robust level of evidence to apply knowledge of predictive factors for greater efficiency in the treatment decision process. The irruption of artificial intelligence in this field is providing definitive answers to such long-standing questions that may change clinical guidelines and make personalized medicine a reality.

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来源期刊
Archives of Medical Research
Archives of Medical Research 医学-医学:研究与实验
CiteScore
12.50
自引率
0.00%
发文量
84
审稿时长
28 days
期刊介绍: Archives of Medical Research serves as a platform for publishing original peer-reviewed medical research, aiming to bridge gaps created by medical specialization. The journal covers three main categories - biomedical, clinical, and epidemiological contributions, along with review articles and preliminary communications. With an international scope, it presents the study of diseases from diverse perspectives, offering the medical community original investigations ranging from molecular biology to clinical epidemiology in a single publication.
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