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

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.

Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology

“…In particular, glioblastoma, either on its own or combined with metastasis as a super-class of aggressive tumors, is the subject of many studies, with some others also including other frequent super-classes such as low-grade glioma or meningioma, while minority tumor types and grades are only considered in a limited number of studies. Importantly, and related to our previous comments concerning scarce data availability, most of the studies reported in [32] work with very small sample sizes, often not reaching the barrier of 100 cases. The challenge posed by data scarcity is compounded by the fact that most of the studies extract Radiomic features in the hundreds if not the thousands.…”

“…The former may include first-order statistics, size and shape-based features, image intensity histogram descriptors, image textural information, etc. The use of this method for the pre-processing of brain tumor images prior to the use of ML has been recently and exhaustively reviewed in [32]. From that review, it is clear that the predominant problem under analysis is diagnosis, with only a limited number of studies addressing prognosis, survival, and progression.…”

Radiomics-Based Machine Learning in Differentiation Between Primary Central Nervous System Lymphoma And Solitary Brain Metastasis

“…Unsatisfactory diagnostic accuracy was also revealed according to these previous studies. What's more, research on the differential diagnosis of PCNSL from SBM based on radiomics is scarce [22,23].…”