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Radiogenomics of lower-grade gliomas: machine learning–based MRI texture analysis for predicting 1p/19q codeletion status

  • Burak KocakEmail author
  • Emine Sebnem Durmaz
  • Ece Ates
  • Ipek Sel
  • Saime Turgut Gunes
  • Ozlem Korkmaz Kaya
  • Amalya Zeynalova
  • Ozgur Kilickesmez
Neuro

Abstract

Objective

To evaluate the potential value of the machine learning (ML)–based MRI texture analysis for predicting 1p/19q codeletion status of lower-grade gliomas (LGG), using various state-of-the-art ML algorithms.

Materials and methods

For this retrospective study, 107 patients with LGG were included from a public database. Texture features were extracted from conventional T2-weighted and contrast-enhanced T1-weighted MRI images, using LIFEx software. Training and unseen validation splits were created using stratified 10-fold cross-validation technique along with minority over-sampling. Dimension reduction was done using collinearity analysis and feature selection (ReliefF). Classifications were done using adaptive boosting, k-nearest neighbours, naive Bayes, neural network, random forest, stochastic gradient descent, and support vector machine. Friedman test and pairwise post hoc analyses were used for comparison of classification performances based on the area under the curve (AUC).

Results

Overall, the predictive performance of the ML algorithms were statistically significantly different, χ2(6) = 26.7, p < 0.001. There was no statistically significant difference among the performance of the neural network, naive Bayes, support vector machine, random forest, and stochastic gradient descent, adjusted p > 0.05. The mean AUC and accuracy values of these five algorithms ranged from 0.769 to 0.869 and from 80.1 to 84%, respectively. The neural network had the highest mean rank with mean AUC and accuracy values of 0.869 and 83.8%, respectively.

Conclusions

The ML-based MRI texture analysis might be a promising non-invasive technique for predicting the 1p/19q codeletion status of LGGs. Using this technique along with various ML algorithms, more than four-fifths of the LGGs can be correctly classified.

Key Points

• More than four-fifths of the lower-grade gliomas can be correctly classified with machine learning–based MRI texture analysis. Satisfying classification outcomes are not limited to a single algorithm.

• A few-slice-based volumetric segmentation technique would be a valid approach, providing satisfactory predictive textural information and avoiding excessive segmentation duration in clinical practice.

• Feature selection is sensitive to different patient data set samples so that each sampling leads to the selection of different feature subsets, which needs to be considered in future works.

Keywords

Artificial intelligence Machine learning Radiomics Glioma Mutation 

Abbreviations

AUC

Area under the curve

GLCM

Grey-level co-occurrence matrix

GLRLM

Grey-level run-length matrix

GLZLM

Grey-level zone length matrix

LGG

Lower-grade glioma

ML

Machine learning

MRI

Magnetic resonance imaging

NGLDM

Neighbourhood grey-level difference matrix

SD

Standard deviation

T1W

T1-weighted

T2W

T2-weighted

TCIA

The Cancer Imaging Archive

WHO

World Health Organisation

Notes

Funding information

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 not required for this study because all patients included in this study are publicly and freely available for scientific purposes.

Ethical approval

Institutional Review Board approval was not required because all patients included in this study are publicly and freely available for scientific purposes.

Study subjects or cohorts overlap

Imaging data of 25 patients were partially used in the authors’ previous work in a completely different context. Previous work has been submitted to another journal.

Methodology

• Retrospective

• Diagnostic or prognostic study

• Based on public data

Supplementary material

330_2019_6492_MOESM1_ESM.docx (512 kb)
ESM 1 Part A: Acquisition parameters. Part B: Details of extracted radiomic features. Part C: Data handling. Part D: Receiver operating characteristic (ROC) curves for each model and sample (DOCX 512 kb)

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Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  1. 1.Department of RadiologyIstanbul Training and Research HospitalIstanbulTurkey
  2. 2.Department of RadiologyBuyukcekmece Mimar Sinan State HospitalIstanbulTurkey
  3. 3.Department of Radiology, Koc University School of MedicineKoc University HospitalIstanbulTurkey
  4. 4.Department of Radiology, Cerrahpasa Medical FacultyIstanbul University-CerrahpasaIstanbulTurkey

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