Advertisement

An Update on Machine Learning in Neuro-Oncology Diagnostics

  • Thomas C. BoothEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11383)

Abstract

Imaging biomarkers in neuro-oncology are used for diagnosis, prognosis and treatment response monitoring. Magnetic resonance imaging is typically used throughout the patient pathway because routine structural imaging provides detailed anatomical and pathological information and advanced techniques provide additional physiological detail.

Following image feature extraction, machine learning allows accurate classification in a variety of scenarios. Machine learning also enables image feature extraction de novo although the low prevalence of brain tumours makes such approaches challenging.

Much research is applied to determining molecular profiles, histological tumour grade and prognosis at the time that patients first present with a brain tumour. Following treatment, differentiating a treatment response from a post-treatment related effect is clinically important and also an area of study. Most of the evidence is low level having been obtained retrospectively and in single centres.

Keywords

Neuro-oncology Machine learning Diagnostic 

Notes

Acknowledgments

This work was supported by the Wellcome/EPSRC Centre for Medical Engineering [WT 203148/Z/16/Z].

References

  1. 1.
    FDA-NIH Biomarker Working Group: BEST (Biomarkers, EndpointS, and other Tools) Resource. Food and Drug Administration (US), Silver Spring. Co-published by National Institutes of Health (US), Bethesda (2016)Google Scholar
  2. 2.
    MacDonald, D., Cascino, T.L., Schold, S.C., Cairncross, J.G.: Response criteria for phase II studies of supratentorial malignant glioma. J. Clin. Oncol. 8, 1277–1280 (2010).  https://doi.org/10.1200/JCO.1990.8.7.1277CrossRefGoogle Scholar
  3. 3.
    Wen, P.Y., Macdonald, D.R., Reardon, D.A., Cloughesy, T.F., Sorensen, A.G., Galanis, E.: Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J. Clin. Oncol. 28, 1963–1972 (2010).  https://doi.org/10.1200/JCO.2009.26.3541CrossRefGoogle Scholar
  4. 4.
    Kassner, A., Thornhill, R.E.: Texture analysis: a review of neurologic MR imaging applications. Am. J. Neuroradiol. 31(5), 809–816 (2010).  https://doi.org/10.3174/ajnr.A2061CrossRefGoogle Scholar
  5. 5.
    Cagney, D.N., Sul, J., Huang, R.Y., Ligon, K.L., Wen, P.Y., Alexander, B.M.: The FDA NIH biomarkers, endpoints, and other tools (BEST) resource in neuro-oncology. Neuro. Oncol. 20(9), 1162–1172 (2017).  https://doi.org/10.1093/neuonc/nox242CrossRefGoogle Scholar
  6. 6.
    Howick, J., et al.: OCEBM Table of Evidence Working Group: The Oxford 2011 Levels of Evidence (2011). http://www.cebm.net/index.aspx?o=5653. Oxford Centre for Evidence-Based Medicine, Oxford (2016)
  7. 7.
    Louis, D.N., et al.: The 2016 world health organization classification of tumors of the central nervous system: a summary. Acta Neuropathol. 131(6), 803–820 (2016).  https://doi.org/10.1007/s00401-016-1545-1CrossRefGoogle Scholar
  8. 8.
    Zhang, B., et al.: Multimodel MRI features predict isocitrate dehydrogenase genotype in high grade gliomas. Neuro. Oncol. 19, 109–117 (2017)CrossRefGoogle Scholar
  9. 9.
    Zhou, H., et al.: MRI features predict survival and molecular markers in diffuse lower-grade gliomas. Neuro. Oncol. 19, 862–870 (2017)CrossRefGoogle Scholar
  10. 10.
    Inano, R., et al.: Visualization of heterogeneity and regional grading of gliomas by multiple features using magneteic resonance-based clustered images. Sci. Rep. 6, 30344 (2016)CrossRefGoogle Scholar
  11. 11.
    Booth, T.C., et al.: Analysis of heterogeneity in T2-weighted MR images can differentiate pseudoprogression from progression in glioblastoma. PLoS One 12(5), e0176528 (2017).  https://doi.org/10.1371/journal.pone.0176528CrossRefGoogle Scholar
  12. 12.
    Kebir, S., et al.: Unsupervised consensus cluster analysis of [18F]-fluoroethyl-L-tyrosine positron emission tomography identified textural features for the diagnosis of pseudoprogression in high grade glioma. Oncotarget 8(5), 8294–8304 (2016)Google Scholar
  13. 13.
    Macyszyn, L., et al.: Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques. Neuro. Oncol. 18, 417–425 (2016)CrossRefGoogle Scholar
  14. 14.
    Chato, L., Latifi, S.: Machine learning and deep learning techniques to predict overall survival of brain tumor patients using MRI images. In: 17th IEEE International Conference on Bioinformatics and Engineering. IEEE Press, New York (2017).  https://doi.org/10.1109/bibe.2017.00009

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Biomedical Engineering and Imaging SciencesKing’s College London, St. Thomas’ HospitalLondonUK
  2. 2.Department of NeuroradiologyKing’s College Hospital NHS Foundation TrustLondonUK

Personalised recommendations