Gliomas are the most aggressive primary brain tumors,
presenting poor survival rates,
while the accurate preoperative grade classification is of main clinical importance,
related to early prognosis and precise selection of the therapeutic approach.
According to the World Health Organization (WHO) grading system [1],
gliomas are subdivided into four categories considering their malignancy status,
i.e.
grades I,
II (low grade) and grades III,
IV (high grade).
To date,
several studies have reported that MRI may supportively contribute in tumor heterogeneity assessment,
overcoming sample-biopsy limitations,
towards glioma grading.
Most of these studies consider either conventional [2] or advanced MRI sequences [3] individually,
which provide different perspectives of gliomas pathophysiology.
Usually,
the proposed methods are complemented by advanced image analysis techniques,
such as shape and texture analysis,
for increasing diagnostic accuracy through the quantitative assessment of the spatial information provided by MRI.
Even though the specific findings seem promising,
the increased methodological variability of the current MRI unilateral evaluation approaches,
consequently resulting into conflicting sensitivity and specificity reports,
could lead to a misinterpretation of gliomas’ biological heterogeneity mechanisms.
As it has been mentioned from certain research studies exploiting multiparametric MRI (mp-MRI) data [4-5],
the combination of several MRI parameters representative of the underlying pathophysiology,
may lead to a better understanding of tumor characteristics,
and a more accurate grade classification.
Furthermore,
the recent advent of Radiomics,
considering novel approaches including advanced quantification and classification methodologies,
which facilitate the manipulation and evaluation of multidimensional imaging feature data,
may serve as a sophisticated analysis framework [6],
in performing various clinical data associations (e.g.
imaging,
genomics) [7].
Hence,
it is evident that in the precision medicine era,
a plethora of quantitative parameters should be taken into consideration for an accurate tumor characterization.
However,
there is still a demand for further investigation on the validation and utility of combining such techniques,
in order to establish a powerful non-invasive tool in clinical practice.
The aim of this study was to comprehensively evaluate 3T multiparametric glioma MRI data utilizing radiomic analysis,
to provide imaging biomarkers of increased prognostic value for glioma grading.
To our knowledge,
this study is one of the very few [8-9] to incorporate conventional MR data accompanied by all the advanced MR neuroimaging techniques used in brain tumor evaluation,
within a robust radiomic analysis pipeline.