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)


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.


Neuro-oncology Machine learning Diagnostic 



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


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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

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