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An Update on Machine Learning in Neuro-Oncology Diagnostics

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationBrainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
EditorsAlessandro Crimi, Theo van Walsum, Hugo Kuijf, Mauricio Reyes, Spyridon Bakas, Farahani Keyvan
PublisherSpringer, Cham
Number of pages8
ISBN (Print)9783030117221
Publication statusPublished - 2 Jan 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11383 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


King's Authors


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 imag- ing provides detailed anatomical and pathological information and advanced techniques provide additional physiological detail.
Following image feature extraction, machine learning allows accurate clas- sification 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.

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