Predictors of Response to Treatment with First-Generation Somatostatin Receptor Ligands in Patients with Acromegaly.

Arch Med Res

Endocrine Research Unit, Germans Trias i Pujol Research Institute, Badalona, Spain; Network Research Center for Rare Diseases, CIBERER, Unit 747, Instituto de Salud Carlos III, Madrid, Spain; Department of Endocrinology and Nutrition, Germans Trias i Pujol University Hospital, Badalona, Spain; Department of Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain. Electronic address:

Published: December 2023

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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Source
http://dx.doi.org/10.1016/j.arcmed.2023.102924DOI Listing

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