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

  • Burak Kocak
  • Emine Sebnem Durmaz
  • Pinar Kadioglu
  • Ozge Polat Korkmaz
  • Nil Comunoglu
  • Necmettin Tanriover
  • Naci Kocer
  • Civan Islak
  • Osman Kizilkilic
Neuro

Abstract

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 macroadenoma, and to compare the qTA with quantitative and qualitative T2-weighted relative signal intensity (rSI) and immunohistochemical evaluation.

Methods

Forty-seven patients (24 responsive; 23 resistant patients to SA) were eligible for this retrospective study. Coronal T2-weighted images were used for qTA and rSI evaluation. The immunohistochemical evaluation was based on the granulation pattern of the adenomas. Dimension reduction was carried out by reproducibility analysis and wrapper-based algorithm. ML classifiers were k-nearest neighbours (k-NN) and C4.5 algorithm. The reference standard was the biochemical response status. Predictive performance of qTA was compared with those of the quantitative and qualitative rSI and immunohistochemical evaluation.

Results

Five hundred thirty-five out of 828 texture features had excellent reproducibility. For the qTA, k-NN correctly classified 85.1% of the macroadenomas regarding response to SAs with an area under the receiver operating characteristic curve (AUC-ROC) of 0.847. The accuracy and AUC-ROC ranges of the other methods were 57.4–70.2% and 0.575–0.704, respectively. Differences in predictive performance between qTA-based classification and the other methods were significant (p < 0.05).

Conclusions

The ML-based qTA of T2-weighted MRI is a potential non-invasive tool in predicting response to SAs in patients with acromegaly and GH-secreting pituitary macroadenoma. The method performed better than the qualitative and quantitative rSI and immunohistochemical evaluation.

Key Points

• Machine learning-based texture analysis of T2-weighted MRI can correctly classify response to somatostatin analogues in more than four fifths of the patients.

• Machine learning-based texture analysis performs better than qualitative and quantitative evaluation of relative T2 signal intensity and immunohistochemical evaluation.

• About one third of the texture features may not be excellently reproducible, indicating that a reliability analysis is necessary before model development.

Keywords

Acromegaly Growth hormone-secreting pituitary adenoma Machine learning Magnetic resonance imaging Somatostatin 

Abbreviations

2D

Two-dimensional

3D

Three-dimensional

AUC-ROC

Area under the receiver operating characteristic curve

GH

Growth hormone

ICC

Intra-class correlation coefficient

IGF-1

Insulin-like growth factor-1

k-NN

k-nearest neighbours

LoG

Laplacian of Gaussian

ML

Machine learning

MRI

Magnetic resonance imaging

qTA

Quantitative texture analysis

ROI

Region of interest

rSI

Relative signal intensity

SA

Somatostatin analogue

SD

Standard deviation

WEKA

Waikato Environment for Knowledge Analysis

Notes

Funding

The authors state that this work has not received any funding.

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Burak Kocak, MD.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors (Burak Kocak, MD) has significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

Supplementary material

330_2018_5876_MOESM1_ESM.docx (20 kb)
ESM 1 (DOCX 20 kb)

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Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  1. 1.Department of RadiologyIstanbul Training and Research HospitalIstanbulTurkey
  2. 2.Department of Radiology, Cerrahpasa Medical FacultyIstanbul University-CerrahpasaIstanbulTurkey
  3. 3.Department of Endocrinology and Metabolism, Cerrahpasa Medical FacultyIstanbul University-CerrahpasaIstanbulTurkey
  4. 4.Department of Pathology, Cerrahpasa Medical FacultyIstanbul University-CerrahpasaIstanbulTurkey
  5. 5.Department of Neurosurgery, Cerrahpasa Medical FacultyIstanbul University-CerrahpasaIstanbulTurkey

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