Montserrat Marques-Pamies , Joan Gil , Mireia Jordà , Manel Puig-Domingo
{"title":"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。","authors":"Montserrat Marques-Pamies , Joan Gil , Mireia Jordà , Manel Puig-Domingo","doi":"10.1016/j.arcmed.2023.102924","DOIUrl":null,"url":null,"abstract":"<div><h3>Background and Aims</h3><p>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?</p><p>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.</p></div><div><h3>Methods</h3><p>A systematic search was performed in the Scopus and PubMed databases for functional, imaging, and molecular predictive factors.</p></div><div><h3>Results</h3><p>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.</p></div><div><h3>Conclusions</h3><p>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.</p></div>","PeriodicalId":8318,"journal":{"name":"Archives of Medical Research","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predictors of Response to Treatment with First-Generation Somatostatin Receptor Ligands in Patients with Acromegaly\",\"authors\":\"Montserrat Marques-Pamies , Joan Gil , Mireia Jordà , Manel Puig-Domingo\",\"doi\":\"10.1016/j.arcmed.2023.102924\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background and Aims</h3><p>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?</p><p>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.</p></div><div><h3>Methods</h3><p>A systematic search was performed in the Scopus and PubMed databases for functional, imaging, and molecular predictive factors.</p></div><div><h3>Results</h3><p>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.</p></div><div><h3>Conclusions</h3><p>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.</p></div>\",\"PeriodicalId\":8318,\"journal\":{\"name\":\"Archives of Medical Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2023-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Archives of Medical Research\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0188440923001625\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MEDICINE, RESEARCH & EXPERIMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Archives of Medical Research","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0188440923001625","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
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.
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.