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

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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.

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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

References

  1. Lambin P, Rios-Velazquez E, Leijenaar R et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441–446. https://doi.org/10.1016/j.ejca.2011.11.036

  2. Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577. https://doi.org/10.1148/radiol.2015151169

    Article  PubMed  Google Scholar 

  3. Koçak B, Durmaz EŞ, Ateş E, Kılıçkesmez Ö (2019) Radiomics with artificial intelligence: a practical guide for beginners. Diagn Interv Radiol. https://doi.org/10.5152/dir.2019.19321

  4. Cuccarini V, Erbetta A, Farinotti M et al (2016) Advanced MRI may complement histological diagnosis of lower grade gliomas and help in predicting survival. J Neurooncol 126:279–288. https://doi.org/10.1007/s11060-015-1960-5

    Article  CAS  PubMed  Google Scholar 

  5. Cancer Genome Atlas Research Network, Brat DJ, Verhaak RG et al (2015) Comprehensive, integrative genomic analysis of diffuse lower-grade gliomas. N Engl J Med 372:2481–2498. https://doi.org/10.1056/NEJMoa1402121

  6. Chen B, Liang T, Yang P et al (2016) Classifying lower grade glioma cases according to whole genome gene expression. Oncotarget 7:74031–74042. https://doi.org/10.18632/oncotarget.12188

    Article  PubMed  PubMed Central  Google Scholar 

  7. Louis DN, Perry A, Reifenberger G et al (2016) The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol 131:803–820. https://doi.org/10.1007/s00401-016-1545-1

  8. Eckel-Passow JE, Lachance DH, Molinaro AM et al (2015) Glioma groups based on 1p/19q, IDH, and TERT promoter mutations in tumors. N Engl J Med 372:2499–2508. https://doi.org/10.1056/NEJMoa1407279

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Ino Y, Betensky RA, Zlatescu MC et al (2001) Molecular subtypes of anaplastic oligodendroglioma: implications for patient management at diagnosis. Clin Cancer Res 7:839–845

  10. Kaloshi G, Benouaich-Amiel A, Diakite F et al (2007) Temozolomide for low-grade gliomas: predictive impact of 1p/19q loss on response and outcome. Neurology 68:1831–1836. https://doi.org/10.1212/01.wnl.0000262034.26310.a2

    Article  CAS  PubMed  Google Scholar 

  11. Reifenberger J, Reifenberger G, Liu L, James CD, Wechsler W, Collins VP (1994) Molecular genetic analysis of oligodendroglial tumors shows preferential allelic deletions on 19q and 1p. Am J Pathol 145:1175–1190

  12. Theeler BJ, Yung WKA, Fuller GN, De Groot JF (2012) Moving toward molecular classification of diffuse gliomas in adults. Neurology 79:1917–1926. https://doi.org/10.1212/WNL.0b013e318271f7cb

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Woehrer A, Hainfellner JA (2015) Molecular diagnostics: techniques and recommendations for 1p/19q assessment. CNS Oncol 4:295–306. https://doi.org/10.2217/cns.15.28

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Sanai N, Martino J, Berger MS (2012) Morbidity profile following aggressive resection of parietal lobe gliomas. J Neurosurg 116:1182–1186. https://doi.org/10.3171/2012.2.JNS111228

    Article  PubMed  Google Scholar 

  15. Tate MC, Kim CY, Chang EF, Polley MY, Berger MS (2011) Assessment of morbidity following resection of cingulate gyrus gliomas. Clinical article. J Neurosurg 114:640–647. https://doi.org/10.3171/2010.9.JNS10709

  16. Megyesi JF, Kachur E, Lee DH et al (2004) Imaging correlates of molecular signatures in oligodendrogliomas. Clin Cancer Res 10:4303–4306. https://doi.org/10.1158/1078-0432.CCR-04-0209

  17. Patel SH, Poisson LM, Brat DJ et al (2017) T2-FLAIR mismatch, an imaging biomarker for IDH and 1p/19q status in lower-grade gliomas: a TCGA/TCIA project. Clin Cancer Res 23:6078–6085. https://doi.org/10.1158/1078-0432.CCR-17-0560

  18. Broen MPG, Smits M, Wijnenga MMJ et al (2018) The T2-FLAIR mismatch sign as an imaging marker for non-enhancing IDH-mutant, 1p/19q-intact lower-grade glioma: a validation study. Neuro Oncol 20:1393–1399. https://doi.org/10.1093/neuonc/noy048

  19. Han Y, Xie Z, Zang Y et al (2018) Non-invasive genotype prediction of chromosome 1p/19q co-deletion by development and validation of an MRI-based radiomics signature in lower-grade gliomas. J Neurooncol 140:297–306. https://doi.org/10.1007/s11060-018-2953-y

    Article  CAS  PubMed  Google Scholar 

  20. Lu CF, Hsu FT, Hsieh KL et al (2018) Machine learning-based radiomics for molecular subtyping of gliomas. Clin Cancer Res 24:4429–4436. https://doi.org/10.1158/1078-0432.CCR-17-3445

  21. Akkus Z, Ali I, Sedlář J et al (2017) Predicting deletion of chromosomal arms 1p/19q in low-grade gliomas from MR images using machine intelligence. J Digit Imaging 30:469–476. https://doi.org/10.1007/s10278-017-9984-3

    Article  PubMed  PubMed Central  Google Scholar 

  22. Clark K, Vendt B, Smith K et al (2013) The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 26:1045–1057. https://doi.org/10.1007/s10278-013-9622-7

    Article  PubMed  PubMed Central  Google Scholar 

  23. Erickson B, Akkus Z, Sedlar J, Korfiatis P (2017) Data from LGG-1p19qDeletion. Cancer Imaging Arch. https://doi.org/10.7937/K9/TCIA.2017.dwehtz9v

  24. Collewet G, Strzelecki M, Mariette F (2004) Influence of MRI acquisition protocols and image intensity normalization methods on texture classification. Magn Reson Imaging 22:81–91. https://doi.org/10.1016/j.mri.2003.09.001

    Article  CAS  PubMed  Google Scholar 

  25. Shafiq-Ul-Hassan M, Zhang GG, Latifi K et al (2017) Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels. Med Phys 44:1050–1062. https://doi.org/10.1002/mp.12123

    Article  CAS  PubMed  Google Scholar 

  26. Nioche C, Orlhac F, Boughdad S et al (2018) LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res 78:4786–4789. https://doi.org/10.1158/0008-5472.CAN-18-0125

    Article  CAS  PubMed  Google Scholar 

  27. Demšar J, Curk T, Erjavec A et al (2013) Orange: data mining toolbox in Python. J Mach Learn Res 14:2349–2353

    Google Scholar 

  28. Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30

    Google Scholar 

  29. Zhou H, Chang K, Bai HX et al (2019) Machine learning reveals multimodal MRI patterns predictive of isocitrate dehydrogenase and 1p/19q status in diffuse low- and high-grade gliomas. J Neurooncol 142:299–307. https://doi.org/10.1007/s11060-019-03096-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Zhou H, Vallières M, Bai HX et al (2017) MRI features predict survival and molecular markers in diffuse lower-grade gliomas. Neuro Oncol 19:862–870. https://doi.org/10.1093/neuonc/now256

  31. Lewis MA, Ganeshan B, Barnes A et al (2019) Filtration-histogram based magnetic resonance texture analysis (MRTA) for glioma IDH and 1p19q genotyping. Eur J Radiol 113:116–123. https://doi.org/10.1016/j.ejrad.2019.02.014

    Article  PubMed  Google Scholar 

  32. Kuthuru S, Deaderick W, Bai H et al (2018) A visually interpretable, dictionary-based approach to imaging-genomic modeling, with low-grade glioma as a case study. Cancer Inform 17:1176935118802796. https://doi.org/10.1177/1176935118802796

    Article  PubMed  PubMed Central  Google Scholar 

  33. Bahrami N, Hartman SJ, Chang YH et al (2018) Molecular classification of patients with grade II/III glioma using quantitative MRI characteristics. J Neurooncol 139:633–642. https://doi.org/10.1007/s11060-018-2908-3

  34. Zwanenburg A, Leger S, Vallières M, Löck S (2016) Image biomarker standardisation initiative - feature definitions. arXiv:1612.07003

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Funding

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

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Authors

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Correspondence to Burak Kocak.

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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

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Authors submitted an abstract of this work to European Congress of Radiology 2020 (ECR 2020) as an oral research presentation. The control number for the presentation is #0645.

Electronic supplementary material

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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Cite this article

Kocak, B., Durmaz, E.S., Ates, E. et al. Radiogenomics of lower-grade gliomas: machine learning–based MRI texture analysis for predicting 1p/19q codeletion status. Eur Radiol 30, 877–886 (2020). https://doi.org/10.1007/s00330-019-06492-2

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