An Update on Machine Learning in Neuro-Oncology Diagnostics

TY - GEN

T1 - An Update on Machine Learning in Neuro-Oncology Diagnostics

AU - Booth, Thomas Calvert

PY - 2019/1/2

Y1 - 2019/1/2

N2 - 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.

AB - 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.

KW - Diagnostic

KW - Machine learning

KW - Neuro-oncology

UR - http://www.scopus.com/inward/record.url?scp=85063514429&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-11723-8_4

DO - 10.1007/978-3-030-11723-8_4

M3 - Conference contribution

SN - 9783030117221

VL - 11383

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 37

EP - 44

BT - Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

A2 - Crimi, Alessandro

A2 - van Walsum, Theo

A2 - Kuijf, Hugo

A2 - Reyes, Mauricio

A2 - Bakas, Spyridon

A2 - Keyvan, Farahani

PB - Springer, Cham

ER -