# A complete pipeline for glioma grading using intelligent AI on multimodal MRI data,

Paul Dequidt, Pascal Bourdon, Benoit Tremblais, Mathieu Naudin, Pierre Fayolle, Clément Giraud, Carole Guillevin, Christine Fernandez-Maloigne, Rémy Guillevin

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Paul Dequidt, Pascal Bourdon, Benoit Tremblais, Mathieu Naudin, Pierre Fayolle, et al.. A complete pipeline for glioma grading using intelligent AI on multimodal MRI data,. Medical Research Archives, 2023, 11 (5), 10.18103/mra.v11i5.3793. hal-05469553

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**Citation:** Dequidt P, Bourdon P, Tremblais B, et al., 2023. A complete pipeline for glioma grading using intelligible AI on multimodal MRI data. Medical Research Archives, [online] 11(5).

https://doi.org/10.18103/mra.v11i5.3793

**Copyright:** © 2023 European Society of Medicine. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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https://doi.org/10.18103/mra.v11i5.3793

ISSN: 2375-1924

# A complete pipeline for glioma grading using intelligible AI on multimodal MRI data

Paul Dequidt<fnref n="4" />, Pascal Bourdon<fnref n="1" />,<fnref n="5" />, Benoit Tremblais<fnref n="1" />,<fnref n="5" />, Mathieu Naudin<fnref n="2" />,<fnref n="3" />,<fnref n="5" />, Pierre Fayolle<fnref n="3" />,<fnref n="5" />, Clément Giraud<fnref n="2" />,<fnref n="3" />,<fnref n="5" />, Carole Guillevin<fnref n="2" />,<fnref n="3" />,<fnref n="5" />, Christine Fernandez-Maloigne<fnref n="1" />,<fnref n="5" /> and Rémy Guillevin<fnref n="2" />,<fnref n="3" />,<fnref n="5" />*

## Abstract

1) **Objectives:** Machine learning for binary glioma grading have been extensively used on anatomical MRI, especially using the BraTS dataset. The relevance of radiomic criteria based on multimodal imaging, including diffusion, perfusion and spectroscopy data is to be explored, as multimodal datasets are scarce, and there is no common benchmark for performance comparison.

2) **Material and methods:** Poitiers University Hospital provides 123 multimodal patient data. We computed 124 features and let a recursive feature elimination algorithm (RFE) yield a relevant, reduced subset of features. We trained a SVM classifier on this subset. We proposed a method to adapt the BraTS dataset to allow performance comparison with the literature. We got a performance reference point by training on anatomical data only, and showed improvements when multimodalities were added. We explored the feature relevance through the RFE subset. The RFE subset is not constant and induce variability in the performances. To smooth the variability, we applied the RFE algorithm 100 times and incremented the selected features, resulting in a global feature ranking. We also show the best classifier reached on these 100 trainings and its feature subset.

3) **Results:** The best classifier reached 86.5% accuracy, with a mean accuracy on 100 trainings of 78.6%. The rankings shows that anatomical and perfusion sequences are the most relevant for glioma grading, especially T1 post-gadolinium, cerebral blood volume and flow. Intensity and texture features are frequently selected, while anisotropic diffusion coefficient, time to peak and mean time transit mappings seem irrelevant.

4) **Conclusion:** Multimodal radiomics improve the classification and are consistent with the radiological analysis.

<footnote n="1">¹XLIM Laboratory, UMR CNRS 7252, University of Poitiers, Poitiers, France</footnote>

<footnote n="2">²DACTIM-MIS, LMA, UMR CNRS 7348, University of Poitiers, Poitiers, France</footnote>

<footnote n="3">³CHU de Poitiers, 86000 Poitiers, France</footnote>

<footnote n="4">⁴COMETE, UMRS-S 1075, INSERM</footnote>

<footnote n="5">⁵I3M, Common Laboratory CNRS University Hospital and University of Poitiers</footnote>

<footnote>*remy.guillevin@chu-poitiers.fr</footnote>
# 1. Introduction

Gliomas are the most common type of brain tumor, linked to a high mortality within a short survival range. Gliomas are highly heterogeneous tumors, and optimal treatment depends on identifying and locating the highest-grade disease present. This is why the quantitative analysis of images acquired for the diagnosis and treatment of patients with brain tumors has increased significantly in recent years, particularly using machine learning methods and, more specifically, deep learning algorithms<sup>[1-3]</sup>. The value of imaging in patients with brain tumor can be enhanced if pathologic data can be estimated from imaging input using predictive models, knowing imaging techniques for diagnosis and treatment of patients are often not validated against the histopathologic criterion standard.

According to the World Health Organization (WHO), glioma development is described by their grade, ranging from I to IV, with low grade gliomas (LGG) being grades I-II and high-grade gliomas (HGG) being grades III-IV<sup>[4]</sup>. But if glioma grading using machine learning is an active field, only a few studies use multimodal Magnetic Resonance (MR) data, including diffusion and perfusion imaging, and magnetic resonance spectroscopy (MRS)<sup>[5]</sup>. The reference dataset for this task is the MICCAI Brain Tumor Segmentation (BraTS) challenge dataset<sup>[6-8]</sup>, which provides 285 patients with 4 anatomical sequences: T1, T1 contrast-enhanced (T1c), T2 and T2 FLuid Attenuated Inversion Recovery (FLAIR). However, this dataset is not divided into HGG and LGG according to the

WHO, but into High Grade Gliomas (glioblastoma multiform, WHO-IV) and Lower Grade Gliomas (WHO-I, II, III). Consequently, most works on this task are not aligned with a WHO-defined system, despite claiming otherwise<sup>[9,10]</sup>. In a previous study, we proposed a new ground truth to align the BraTS dataset on the WHO definition, to use it as a reference dataset for a WHO-defined binary grading task<sup>[11]</sup>.

But anatomical data lack specificity for a robust classification<sup>[12]</sup>. Multimodal MRI, including perfusion, diffusion and MRS data, gives important information for binary grading<sup>[13]</sup>. In order to create an alternative to the BraTS dataset, some authors use proprietary datasets, with their own acquisition protocol. They provide more sequences and more MR modalities, but with a lack of patients. For example, Citak-Er et al. use a dataset of 43 patients<sup>[14]</sup> and Vamvakas et al. use only 40 patients<sup>[15]</sup>. The lack of a large and open multimodal dataset prevents creating a performance benchmark to compare different publications.

Poitiers University Hospital, France, has led a 3T multimodal pre-operative acquisition protocol with 123 histologically confirmed glioma patients, 46 LGG and 77 HGG. Using these data, we created a complete processing pipeline from the acquisition to the automatic classification. This pipeline involves multimodal registration, brain and tumor segmentation, intensity normalization, feature extraction, feature selection and classification using Support Vector Machines (SVM)<sup>[16-17]</sup>.

In this paper, we will review the current performances in binary glioma grading. We
will describe our acquisition protocol and
processing pipeline. We will train and
evaluate our classifier on the Poitiers
University Hospital multimodal dataset,
analyze the feature ranking produced by the
feature selection and link this ranking to the
radiologic analysis. The conclusion will
present some perspectives.

## 2. Material and methods

### 2.1. Multimodal imaging

Radiologists can reach a binary estimation of
glioma grades from anatomical data[¹⁸,¹⁹].
Some visual features, such as gadolinium
enhancement, necrosis, mass effect, or FLAIR
inhomogeneity can be read as clues for a
high-grade label. Questions can be raised
about the robustness of these features, as it is
known that LGG, which are usually non-
enhancing after gadolinium injection, can
show enhancement patterns[²⁰]. Therefore,
multimodal MR data can improve the
diagnosis with complementary information[²<fnref n="1" />-
²⁶].

Fitting the arterial input function on perfusion
imaging give parametric maps such as
cerebral blood volume (CBV) or cerebral
blood flow (CBF), mean transit time (MTT) and
time to peak (TTP). The ratio between the
tumor region of interest (ROI) and contro-
lateral healthy tissues give the relative CBV
(rCBV) and CBF (rCBF) values, showing hypo
or hyper-perfusion in the lesion. Diffusion
tensor imaging (DTI) generates maps of the

anisotropic diffusion coefficient (ADC),
fractional anisotropy (FA) and the b1000 trace
image. Proton MRS at short (35 ms) and long
(135 ms) echo time (TE) give metabolic
concentrations such as choline (Cho), creatine
(Cr), N-acetylaspartic acid (NAA), lipids (Lip) or
lactates (Lac), which can be compared
between patients by calculating metabolic
ratios.

### 2.2. Data

We used two datasets: Poitiers University
Hospital multimodal dataset and the
anatomical BraTS 2018 dataset<fnref n="1" />.

Using Poitiers University Hospital Magnetom
Skyra 3 Tesla (Siemens Healthineers,
Erlangen, Germany), we created a dataset of
123 patients, divided in 46 LGG and 77 HGG,
confirmed by histo-molecular analysis. For
each patient, we measured the following
modalities and sequences: anatomical
imaging with T1, T1 post-gadolinium (T1c)
and T2 FLAIR; diffusion imaging with ADC, FA
and b1000; perfusion imaging, with CBF,
CBV, MTT and TTP maps; and proton MRS for
short and long TE.

In the existing literature, most authors use the
BraTS 2018 dataset. This dataset provides 285
patients with 4 anatomical sequences: T1,
T1c, T2 and T2 FLAIR. But this dataset is not
divided according to the WHO definition of
LGG and HGG. We proposed in a previous
study a radiological ground truth to align this
dataset on a WHO-based system and showed

<footnote n="1">1
https://www.med.upenn.edu/sbia/brats2018/data.html</footnote>
that our proposed ground truth has little
impact on the performances, despite an
important class imbalance[¹¹].

We propose to adapt the BraTS dataset to get
a performance reference point for our study
on our multimodal data. We will train and
evaluate our classifier on the BraTS dataset
with 4 and 3 anatomical sequences, removing
T2 data that we do not have at Poitier
University Hospital. We will then use our
radiological ground truth to train on a 3
sequence WHO-aligned BraTS dataset. This
training provides a reference point to evaluate
our performances on Poitiers University
Hospital's anatomical data. Finally, we will
then train our classifier on the multimodal data
and see how multimodality impacts the
classification.

## 2.3. Features

We model the radiologic analysis through two
types of features: image computed features
and manual measurements. We used
Pyradiomics[27] to extract image features
aligned with the imaging biomarkers
standardization initiative (ISBI)[28].

For each patient, we computed 7 shape
features, 6 histogram-based intensity features
and 5 texture features. A ROI of tumor
segmentation is needed for feature
extraction. The shape features are only
computed once per patient while the intensity
and texture features are computed on each
MR sequence. The shape features include
length of major and minor axis, maximum 3D
diameter, elongation, flatness, sphericity, and
surface area. The histogram-based intensity
features are mean, skewness, kurtosis,

contrast, energy, and entropy. For texture
analysis we used the correlation of the gray
level co-occurrence matrix (GLCM),
coarseness, inverse difference moment (IDM),
complexity and strength. This gives us 117
automated image features.

The manual measurements are the rCBV and
rCBF values and 5 metabolic ratios: Cho/Cr,
Cho/NAA, NAA/Cr, Lac/Cr at long echo time
(TE, 135ms) and Lip/Cr at short TE (35 ms).
Therefore, each patient is described by 124
features.

These acquisitions are time and resource
consuming. Some sequences were missing for
some patients, creating sparse data. We fix
these sparse data by assigning the mean
value of the corresponding feature.

## 2.4. Binary classification, current performances

Common evaluation metrics used for this task
are precision, sensitivity, and specificity. For
binary glioma grading, the accuracy ranges
from 84% to 95.5% depending on the method
and dataset used[5]. Deep learning methods
gives interesting results but lacks intelligibility.
We want to use a machine learning methods
to have an intelligible classifier. Computing a
large set of features increases the
dimensionality. This is why many authors use
a feature selection algorithm and train on the
resulting set.

Computing a large set of features increases
the dimensionality. Most authors use a feature
selection algorithm, like the SVM Recursive
Feature Elimination (SVM-RFE) algorithm, and
train on the resulting set[26]. The SVM-RFE
returns a subset of features with high
informational value and helps reduce
dimensionality. This algorithm gives good
results associated with a linear kernel.

## 2.5. Preprocessing pipeline

We designed a preprocessing pipeline for
multimodal sequences, including registration,
brain, and tumor segmentation.

Using multimodal MRI data requires the
implementation of a complete processing
pipeline. These steps include multimodal
registration (rigid transformations and
oversampling to a common resolution), brain
extraction, intensity normalization and tumor
segmentation. The Image Tool-Kit[27-30] and
FMRIB Software Library (FSL)[31] give tools for
registration, brain extraction, oversampling,
and intensity normalization. Each step of our
pipeline has been visually validated by an
expert radiologist.

The rigid multimodal registration groups a
translation followed by an affine transform,
with a Powell optimizer[32]. We also resampled
each image on a common resolution, aligned
with the BraTS 2018 acquisitions.

Brain segmentation is performed using the
Brain Extraction Tool (BET)[33]. We got the best
segmentation on T1c images and applied the
segmentation mask on the other registered
sequences to get multimodal brain
segmentation.

The intensity normalization is computed
through histogram matching. We chose the
patient with the highest intensity amplitude of
the BraTS dataset as the reference histogram.

The features are extracted from the tumor
ROI. Good segmentation results are reached
using deep learning networks[34], especially

with U-net based methods like Isensee et
al.[35]. This method is among the best results in
the BraTS 2017 challenge with an average
Dice score of 0.896.

We have trained the U-net proposed by
Isensee et al. on the BraTS data, using only
the 3 anatomical sequences available at the
Hospital of Poitiers, i.e., T1, T1c and T2 FLAIR.
We use the 3D patches extraction proposed
by Fenneteau et al.[36] for better memory
allocation. With one sequence less than the
original implementation by Isensee et al., we
reached an average Dice score of 0.804. Our
network gives good visual results, and these
segmentations has been validated by an
expert radiologist.

## 2.6. Classifier

We want to use an intelligible classifier and
analyze its performances through feature
selection and feature ranking. We used a
20\% test set and a 5-fold cross-validated
SVM-RFE for feature selection, followed by a
SVM with grid search to optimize
hyperparameters. We tested two kernels
(linear and radial) and 5 values of the
regularization hyperparameter ranging from
0.1 to 2.

We let the SVM-RFE algorithm choose the
best number of features for training. This
exploration phase led to a high variability in
the classifier performances. We propose to
smooth this variability by doing 100
independent training and average the
performances (precision, sensitivity, and
specificity). We also show the best precision
reached on these 100 independent trainings.
Each training gets its own subset of features by the SVM-RFE. We will analyze the selection frequency of each feature to see which feature is relevant for our problem in a global feature ranking.

# 3. Results

## 3.1. Performance Analysis

Training on the BraTS 2018 dataset gives a precision of 85.8 ± 4.1%, within performances in the state of the art[8]. Training on 3 sequences gives similar performances, showing that the T2 sequence is not important for binary grading. Using our WHO-aligned ground truth drops slightly the accuracy. As this ground truth has an important class imbalance, these results show that our learning is robust.

We get our performance reference point by training on the anatomical sequences available at Poitiers Hospital. This gives a lower precision with only 77.5 ± 6.8%. The average sensitivity stays stable with 88.5 ± 6.2%, and the specificity drops to 60.2 ± 14.0%. The best classifier on 100 trainings gets an accuracy of 81.1%. We want to see how these values change when we add multimodality.

Adding multimodal data leads to a 1.1% gain in averaged accuracy, going from 77.5 ± 6.8% to 78.6 ± 6.6%. The best classifier on 100 trainings reaches 86.5% accuracy; a strong improvement compared to anatomical data only. We observe a slight decrease in sensitivity, from 88.5% ± 6.2% to 85.9% ± 7.9%. The largest improvement is reached for the average specificity, reaching 67.3 ± 15.0%. Multimodality improves the

classification, but these improvements are not as important as we would expect from the state of art, where multimodal SVM often reached 90% accuracy. Extended results are shown in Table 1.

# 4. Discussion

## 4.1. Feature ranking

We want to analyze the ranking produced by the SVM-RFE. The best classifier on 100 trainings used a linear kernel and a subset of 23 features, Table 2. This ranking corresponds to the weights of the normal vector of the SVM hyperplane, as described by Guyon et al.[26]. We will also analyze the selection frequency of each feature on 100 trainings. This second ranking gives us a global ranking of all 124 features. Both rankings give information about which features and sequences are relevant in the classification. We will compare the top 23 features of this frequency ranking, Table 3, with the 23 features of Table 2.

These rankings share 14 common features, with homogeneous ranks. Therefore, we will read the ranking on 100 independent trainings as robust global alternative ranking.

Half of the features in Table 2 are anatomical sequences, mostly T1c. In the top ranked features, 3 out of 5 are intensity features computed on anatomical sequences. These results are coherent with the radiological analysis: the T1c contrast is the top feature, and HGG are contrasted lesions with gadolinium enhancement next to necrotic regions, while LGG are homogeneous.
<table><thead><tr><th>Groundtruth</th><th>Accuracy<sup>1</sup></th><th>Sensitivity<sup>1</sup></th><th>Specificity<sup>1</sup></th><th>Best accuracy<sup>2</sup></th></tr></thead><tbody><tr><td>BraTS 2018<br>4 sequences</td><td>85,8 ± 4,1%</td><td>89,5 ± 4,5%</td><td>75,4 ± 9,7%</td><td>89,5%</td></tr><tr><td>BraTS 2018<br>3 sequences</td><td>86,0 ± 4,2%</td><td>89,4 ± 4,5%</td><td>75,9 ± 9,6%</td><td>87,7%</td></tr><tr><td>WHO-aligned<br>BraTS 2018 [10]<br>3 sequences</td><td>82,1 ± 4,1%</td><td>84,0 ± 5,5%</td><td>67,1 ± 9,7%</td><td>84,2%</td></tr><tr><td>Poitiers Hospital<br>3 sequences</td><td>77,5 ± 6,8%</td><td>88,5 ± 6,2%</td><td>60,2 ± 14,0%</td><td>81,1%</td></tr><tr><td>Poitiers Hospital<br>Multimodal data</td><td>78,6 ± 6,6%</td><td>85,9 ± 7,9%</td><td>67,3% ± 15,0%</td><td>86,5%</td></tr></tbody></table>

<caption>Table 1: Performances results. Each value reached on 100 independent training is given with its standard deviation.</caption>

<footnote n="1">¹Mean performances on 100 trainings</footnote>

<footnote n="2">²Best accuracy reached on 100 trainings</footnote>
<table><caption>Table 2: Ranking from the best classifier reached on 100 independent training</caption><thead><tr><td>Rank</td><td>Feature</td><td>Sequence</td><td>Modality</td></tr></thead><tbody><tr><td>1</td><td>Contrast</td><td>T1c</td><td>Anatomical</td></tr><tr><td>2</td><td>Entropy</td><td>T1c</td><td>Anatomical</td></tr><tr><td>3</td><td>rCBV</td><td>CBV</td><td>Perfusion</td></tr><tr><td>4</td><td>Skewness</td><td>T1</td><td>Anatomical</td></tr><tr><td>5</td><td>Complexity</td><td>CBV</td><td>Perfusion</td></tr><tr><td>6</td><td>GLCM correlation</td><td>CBV</td><td>Perfusion</td></tr><tr><td>7</td><td>Mean</td><td>T2 FLAIR</td><td>Anatomical</td></tr><tr><td>8</td><td>IDM</td><td>CBV</td><td>Perfusion</td></tr><tr><td>9</td><td>IDM</td><td>T1c</td><td>Anatomical</td></tr><tr><td>10</td><td>Complexity</td><td>CBF</td><td>Perfusion</td></tr><tr><td>11</td><td>GLCM correlation</td><td>T2 FLAIR</td><td>Anatomical</td></tr><tr><td>12</td><td>IDM</td><td>T1</td><td>Anatomical</td></tr><tr><td>13</td><td>Skewness</td><td>b1000</td><td>Diffusion</td></tr><tr><td>14</td><td>Mean</td><td>T1c</td><td>Anatomical</td></tr><tr><td>15</td><td>Skewness</td><td>CBV</td><td>Perfusion</td></tr><tr><td>16</td><td>Lac/Cr</td><td>CSI</td><td>MRS</td></tr><tr><td>17</td><td>Energy</td><td>CBV</td><td>Perfusion</td></tr><tr><td>18</td><td>GLCM correlation</td><td>FA</td><td>Diffusion</td></tr><tr><td>19</td><td>Kurtosis</td><td>FA</td><td>Diffusion</td></tr><tr><td>20</td><td>Kurtosis</td><td>T2 FLAIR</td><td>Anatomical</td></tr><tr><td>21</td><td>GLCM correlation</td><td>TTP</td><td>Perfusion</td></tr><tr><td>22</td><td>Entropy</td><td>b1000</td><td>Diffusion</td></tr><tr><td>23</td><td>Entropy</td><td>T1</td><td>Anatomical</td></tr></tbody></table>
<table><thead><tr><td>Rank</td><td>Feature</td><td>Sequence</td><td>Modality</td><td>Frequency<sup>1</sup></td></tr></thead><tbody><tr><td>1</td><td>Contrast</td><td>T1c</td><td>Anatomical</td><td>99</td></tr><tr><td>2</td><td>rCBV</td><td>CBV</td><td>Perfusion</td><td>94</td></tr><tr><td>3</td><td>Entropy</td><td>T1c</td><td>Anatomical</td><td>86</td></tr><tr><td>4</td><td>GLCM correlation</td><td>CBV</td><td>Perfusion</td><td>82</td></tr><tr><td>5</td><td>IDM</td><td>T1</td><td>Anatomical</td><td>81</td></tr><tr><td>6</td><td>Lac/Cr</td><td>CSI</td><td>MRS</td><td>78</td></tr><tr><td>7</td><td>IDM</td><td>T1c</td><td>Anatomical</td><td>69</td></tr><tr><td>8</td><td>IDM</td><td>CBV</td><td>Perfusion</td><td>67</td></tr><tr><td>9</td><td>Skewness</td><td>T1</td><td>Anatomical</td><td>63</td></tr><tr><td>10</td><td>Kurtosis</td><td>T1c</td><td>Anatomical</td><td>62</td></tr><tr><td>11</td><td>Kurtosis</td><td>CBV</td><td>Perfusion</td><td>57</td></tr><tr><td>12</td><td>rCBF</td><td>CBF</td><td>Perfusion</td><td>55</td></tr><tr><td>13</td><td>GLCM correlation</td><td>FA</td><td>Diffusion</td><td>54</td></tr><tr><td>14</td><td>GLCM correlation</td><td>CBF</td><td>Perfusion</td><td>54</td></tr><tr><td>15</td><td>Mean</td><td>T1c</td><td>Anatomical</td><td>52</td></tr><tr><td>16</td><td>IDM</td><td>FA</td><td>Diffusion</td><td>46</td></tr><tr><td>17</td><td>GLCM correlation</td><td>MTT</td><td>Perfusion</td><td>43</td></tr><tr><td>18</td><td>Entropy</td><td>T1</td><td>Anatomical</td><td>42</td></tr><tr><td>19</td><td>Skewness</td><td>T1c</td><td>Anatomical</td><td>41</td></tr><tr><td>20</td><td>Strength</td><td>b1000</td><td>Diffusion</td><td>41</td></tr><tr><td>21</td><td>Complexity</td><td>b1000</td><td>Diffusion</td><td>40</td></tr><tr><td>22</td><td>Mean</td><td>T2 FLAIR</td><td>Anatomical</td><td>39</td></tr><tr><td>23</td><td>Kurtosis</td><td>FA</td><td>Diffusion</td><td>32</td></tr></tbody></table>

<caption>Table 3: Ranking from 100 independent training</caption>

Perfusion features are less frequent than anatomical features. Most of them are from CBV and CBF maps. The rCBV value is among the 3 best features in both rankings. The rCBF value appears in Table 3 and has been selected 55 times. These ranking underline

the relevance of these manual measurements for the classification task. TTP and MTT maps are only present once in both rankings, so we can question their relevance for binary grading.

<footnote n="1">¹Number of occurrence in the RFE subset on 100 independent trainings</footnote>
Regarding diffusion imaging, we note that the
ADC is absent from both rankings, which is

surprising as ADC is visually discriminative, as
shown in Figure 1.

<figure><img src="image_4.png" /><figcaption>Figure 1: ADC maps of (left) a LGG patient and (right) a HGG patient. The HGG patients shows high intra-tumoral ADC values which are not visible for a LGG patient. Therefore, ADC should appear as a discriminative sequence.</figcaption></figure>

Most features in both rankings are intensity-
based features, followed by texture features.
No shape feature appears in both rankings.
Every type of intensity features appears,
mostly from anatomical T1c and T1 sequences,
which underlines the relevance of such
features. Dissimilarly, coarseness and strength
features are always absent. These texture
features are not interesting for binary grading.

Using 100 independent training allow us to
get a global feature ranking, as seen in Figure
2. This Figure shows the number of
occurrences of each 124 features in the SVM-
RFE subset on 100 independent trainings.
From the shape of this histogram, we can tell
that some features were never selected and

that some others were highly solicited. The
most selected feature, T1c contrast, has been
selected 99 times out of 100. 15 features were
never selected in the SVM-RFE subset. Most
of them were texture features, namely
strength and coarseness. This result shows
very clearly that these texture features are not
interesting for glioma grading.

Two other features were never selected: the
T2 FLAIR contrast and CBV contrast. The
absence of the T2 FLAIR contrast is surprising,
as HGG can have a heterogeneous aspect in
T2 FLAIR, resulting in an increased contrast
within the lesion.
<figure><img src="image_5.png" /><figcaption>Figure 2: Features sorted in decreasing order of appearance</figcaption></figure>

## 5. Conclusion

Using multimodal MRI data from Poitiers University Hospital, we proposed a complete processing pipeline ranging from the acquisition to the automatic classification. This pipeline includes automatic multimodal registration, brain extraction, intensity normalization, tumor segmentation, and features computation. Two types of features are still based on manual measurements: metabolic SRM ratios and relative contralateral perfusion values.

To the best of our knowledge, our work is the only one to use multimodal data and provide a performance reference point with the BraTS database, which is the most popular dataset used in glioma grading. This allows us to compare our classifier result with the current state-of-art and show the impact of multimodal data on our performances.

Adding multimodal data, we observed a small gain in mean intensity compared to learning on anatomical data only. The best impact was on specificity. Therefore, we can say that adding multimodal information has a positive effect on the performances.

We proposed an analysis of our classifier through feature ranking. We analyzed two rankings: one produced by 100 independent trainings and the other by the best classifier reached on these trainings. Both rankings produced comparable results, and we used the analysis over 100 trainings as a global ranking for all 124 features. This global ranking allows us to deepen the feature analysis, showing that strength and coarseness are not interesting features for this classification task. The same result appears for ADC, MTT and TTP sequences.
Our ranking also produced interesting result for radiologists, as it highlights anatomical intensity-based features, such as T1c contrast, as relevant for binary grading. Therefore, we showed how machine learning can be used as an exploration tool and produce a consistent analysis of the radiological process.

To extend this study, our current work deals with comparison of this approach with convolutional neural networks classification, and by considering the explainability of the algorithms, essential point for health data[37]. Moreover, we now use imaging coming from

a Siemens 7Tesla MRI Scanner, intalled in Poitiers since December 2019. Our Siemens Magnetom 7Tesla Ultra High Field 7 Tesla Magnetic Resonance Imaging scanner is one of only three in France, and the lonely one for clinical use as well as for research. Indeed, MAGNETOM Terra is the first 7T scanner released for clinical use in Europe and the US and the Universitary Hospital in Poitiers, was the first in the world, in december 2021, to accommodate over 2000 patients for clinical use. This is a huge opportunity to develop our AI systems for the benefit of patients and physicians.
**Corresponding Author**

Rémy Guillevin

DACTIM-MIS, LMA, UMR CNRS 7348,
University of Poitiers, Poitiers, France.

CHU de Poitiers, 86000 Poitiers, France.

I3M, Common Laboratory CNRS Universitary
Hospital and University of Poitiers.

Email: [remy.guillevin@chu-poitiers.fr](mailto:remy.guillevin@chu-poitiers.fr)

**Conflicts of Interest statement**

None.

**Funding information**

None.

**Acknowledgements**

None.
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