Imaging diagnosis is crucial for early detection and monitoring of brain tumors. Radiomics
enable the extraction of a large mass of quantitative features from complex clinical imaging …

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 …

The purpose of this study was to establish a high-performing radiomics strategy with
machine learning from conventional and diffusion MRI to differentiate recurrent glioblastoma …

Objective Hyperintensity signal in T2‐weighted magnetic resonance imaging (MRI) has
been related to better therapeutic response during pasireotide treatment in acromegaly. The …

Context Early identification of the response of prolactinoma patients to dopamine agonists
(DA) is crucial in treatment planning. Objective To develop a radiomics model using an …

Simple Summary Machine learning in radiology of the central nervous system has seen
many interesting publications in the past few years. Since the focus has largely been on …

A pituitary adenoma (PA) is a common intracranial neoplasm, and is a complex, chronic, and
whole‐body disease with multicausing factors, multiprocesses, and multiconsequences. It is …

High-risk pituitary adenomas are aggressive. They show clinical and imaging features
similar to those of carcinomas, including infiltration of the surrounding brain structures, but …

Radiomic analysis has emerged as a valuable tool for extracting quantitative features from
medical imaging data, providing in-depth insights into various contexts and diseases. By …

Introduction Sparsely granulated somatotroph adenoma/tumor (SGST) is thought to be more
clinically aggressive than densely granulated somatotroph adenoma/tumor (DGST) …