Aims and objectives
Glioma imaging has been chosen as the exampleclinical context as it is one of most well researched topics in machine learning.
This literature review appraisesthe current evidence supporting the current use of machine learning inglioma imaging to date. We aim to provide readers with a clear,
concise overview of the capabilities and shortcomings of machine learning in glioma imaging,
such that they can better understand the current state of the art of artificial intelligence and where it might go.
Methods and materials
Embase and Medline databases were searched to identify relevant studiespublished between January 2014and March 2018.
The search produced 18 studieswhichmet the inclusion criteria and related to both machine learning and glioma imaging.
This review appraises studies which use machine learning algorithms in order to generate prognostic information from medical images.
The field of radiomics involves extracting and analysing quantitative imaging features which can then be correlated with other clinical and pathological parameters in order to generate predictive and diagnostic models of disease.
A traditional model of radiomic analysis,
as described by Kumar et al.(2012),
typically involves the following steps: image acquisition,
and informatic analyses (1).
The majority of papers appraised in...
We hope to havedemonstrated the utility of machine learning and deep learning in the creation of a unified workflow for the non-invasive prognostication of glioma.
From automated tumour segmentation,
to the generation of clinically useful quantitative radiomic biomarkers,
and finally to the creation of prognostic models,
machine learning has demonstrated its role in the provision of personalised medicine.
The early work in this field is promising,
but the work is still in its infancy.
The numbers of patients used for machine learningtraining is relatively low,...
Radiomics: the process and the challenges.
Magn Reson Imaging.
2012 Nov 1;30(9):1234–48.
Fathi Kazerooni A.,
Saligheh Rad H.,
Zeinali Zadeh M.,
Characterization of active and infiltrative tumorous subregions from normal tissue in brain gliomas using multiparametric MRI.
J Magn Reson Imaging.
MRI features predict p53...