Machine learning and glioma imaging biomarkers

Clinical Radiology
Thomas C BoothH Shuaib

Abstract

To review how machine learning (ML) is applied to imaging biomarkers in neuro-oncology, in particular for diagnosis, prognosis, and treatment response monitoring. The PubMed and MEDLINE databases were searched for articles published before September 2018 using relevant search terms. The search strategy focused on articles applying ML to high-grade glioma biomarkers for treatment response monitoring, prognosis, and prediction. Magnetic resonance imaging (MRI) is typically used throughout the patient pathway because routine structural imaging provides detailed anatomical and pathological information and advanced techniques provide additional physiological detail. Using carefully chosen image features, ML is frequently used to allow accurate classification in a variety of scenarios. Rather than being chosen by human selection, ML also enables image features to be identified by an algorithm. Much research is applied to determining molecular profiles, histological tumour grade, and prognosis using MRI images acquired at the time that patients first present with a brain tumour. Differentiating a treatment response from a post-treatment-related effect using imaging is clinically important and also an area of active study (described he...Continue Reading

Citations

Jul 18, 2020·International Journal of Computer Assisted Radiology and Surgery·Hari McGrathTom Vercauteren
Jan 26, 2021·Health Information Science and Systems·Kamel A SamaraAmani Abusafia
Mar 25, 2021·Mathematical Biosciences and Engineering : MBE·Annalisa VitaleRenato Cuocolo
Apr 28, 2021·Clinical Radiology·H M C Cheung, D Rubin
May 1, 2021·Diagnostics·Rima HajjoAlexander Tropsha
Oct 1, 2021·The Protein Journal·Preantha PoonanMahmoud E S Soliman

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Methods Mentioned

BETA
biopsy
imaging techniques
imaging technique
X-ray

Software Mentioned

LASSO
DTA
PRISMA

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