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Review
. 2023 Jul 28;15(15):3845.
doi: 10.3390/cancers15153845.

Radiomics and Machine Learning in Brain Tumors and Their Habitat: A Systematic Review

Mehnaz Tabassum  1   2 , Abdulla Al Suman  2 , Eric Suero Molina  2   3 , Elizabeth Pan  2   4 , Antonio Di Ieva  2 , Sidong Liu  1   2
Affiliations
Review

Radiomics and Machine Learning in Brain Tumors and Their Habitat: A Systematic Review

Mehnaz Tabassum et al. Cancers (Basel). .

Abstract

Radiomics is a rapidly evolving field that involves extracting and analysing quantitative features from medical images, such as computed tomography or magnetic resonance images. Radiomics has shown promise in brain tumor diagnosis and patient-prognosis prediction by providing more detailed and objective information about tumors' features than can be obtained from the visual inspection of the images alone. Radiomics data can be analyzed to determine their correlation with a tumor's genetic status and grade, as well as in the assessment of its recurrence vs. therapeutic response, among other features. In consideration of the multi-parametric and high-dimensional space of features extracted by radiomics, machine learning can further improve tumor diagnosis, treatment response, and patients' prognoses. There is a growing recognition that tumors and their microenvironments (habitats) mutually influence each other-tumor cells can alter the microenvironment to increase their growth and survival. At the same time, habitats can also influence the behavior of tumor cells. In this systematic review, we investigate the current limitations and future developments in radiomics and machine learning in analysing brain tumors and their habitats.

Keywords: brain tumor; machine learning; neuro-oncology; peritumoral region; radiomics; tumor habitat.

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Conflict of interest statement

All the authors state that they have no conflict of interest.

Figures

Figure 1
Figure 1
Radiomics workflow: A diagram illustrating the various steps involved in the radiomics workflow, starting with image acquisition for MRI imaging and ending with evaluation, after passing through segmentation, feature extraction, and selection.
Figure 2
Figure 2
The PRISMA diagram shows the screening and selection of relevant papers.
Figure 3
Figure 3
(a) Bar chart of the number of articles included in this review according to their publication year. Three pie charts (b–d) are presented, depicting the number of articles and focusing on types of study, application area, and subject.
Figure 4
Figure 4
Summary of QUADAS-2 assessments of included studies.
See this image and copyright information in PMC

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