Predicting response to somatostatin analogues in acromegaly: machine learning-based high-dimensional quantitative texture analysis on T2-weighted MRI.

Objective: To investigate the value of machine learning (ML)-based high-dimensional quantitative texture analysis (qTA) on T2-weighted magnetic resonance imaging (MRI) in predicting response to somatostatin analogues (SA) in acromegaly patients with growth hormone (GH)-secreting pituitary macroadeno...

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Bibliographic Details

Published in:	European Radiology Vol. 29; no. 6; pp. 2731 - 2740
Main Authors:	Kocak, Burak, Durmaz, Emine Sebnem, Kadioglu, Pinar, Polat Korkmaz, Ozge, Comunoglu, Nil, Tanriover, Necmettin, Kocer, Naci, Islak, Civan, Kizilkilic, Osman
Format:	Journal Article
Published:	Springer Nature Jun2019
Online Access:	View this record in EBSCOhost