The resistance to treatment prompted us to perform the national PEPPI study (Pediatric
Ependymoma Photons Protons and Imaging). PEPPI collated clinical, imaging and dosimetry
data from children with intracranial ependymoma treated in France between 2000 and 2013.
Since then, patients are treated in the current prospective SIOP II Ependymoma program.
Our clinical results confirmed the classical clinical prognostic factors including
radiotherapy dose, and we showed that imaging biomarkers from T2/FLAIR, perfusion and
diffusion MRI were novel prognostic factors. We reported that relapse after RT occurs
mainly locally within the high-dose region. The prognostic value of dose and the high
rate of relapse with standard dose prompted us to perform an in silico dosimetry study of
dose escalation comparing photons and protons, confirming the feasibility of this
approach. In PEPPI series, the prediction of the site of relapse with advanced MR imaging
was not possible due to small sample of imaging data and the recent biomolecular
classification was not available. Recently, DNA methylation profiling provided a novel
classification of ependymoma in molecular subgroups harbouring a strong prognostic value.
EPENDYMOMICS project aims to determine the prognostic role of multimodal imaging, to
identify radioresistant clusters predictive of relapse and to analyze the link between
radiomics features and genomic markers, hence leading to a radiogenomics study. It will
build on a database with a larger number of patients called NETSPARE (Network to
Structure and Share Pediatric data to Accelerate Research on Ependymoma) to develop a
radiomics approach, i.e. predict clinical endpoints by relying on quantitative features
extracted from medical images using either handcrafted or automated features extraction
algorithms. These features will be exploited and combined with other available variables
(clinical, genetic, etc.) as improved decision support.
1. Data collection MRI: Diagnostic, FU until relapse (DICOM format)
- - Sequences: T1W, T2W, FLAIR, T1W with & without contrast enhancement,
diffusion-weighted imaging (DWI), perfusion-weighted imaging (PWI)
Radiotherapy: CT scan, RTDOSE, RTSS, RTPLAN (DICOM RT format) Clinical: Age,
surgery, chemotherapy, doses, late effects, relapse date Histology: Hematoxylin
and Eosin (H&E)-stained histopathology slides Molecular Biology: Methylation
groups RELA, YAP, PFA, and PFB.
2. Data quality check and delineation of volume of interest to curate data before
post-processing and analysis.
To ensure preliminary robust image segmentation the Radiotherapy Structure Set will
be checked for each patient. The referring radiologist will confirm all imaging
changes after RT. An expert radiation oncologist will segment volumes of
post-treatment abnormalities in T1WI post contrast, in T2W/FLAIR imaging, as well as
site of relapse and missing OARs.
3. Imaging data post-processing and analysis of changes after radiotherapy We will
process the DWI and PWI with the Olea Sphere®3.0 software, a post-processing
solution for MRI and CT scanners. We will extract and generate Apparent Diffusion
Coefficient maps from DWI data calculated on a voxel-by-voxel basis and relative
cerebral blood volume maps calculated from PWI data with an oscillation-index
singular value decomposition routine and correction for T1-weighted leakage effects.
All the MRI data will be strictly co-registered with the T1WI-PC and planning CT.
4. Radiomics harmonization We will perform the increase of the level of harmonization
of images and/or extracted features on highly heterogeneous dataset due to its
multiple sources.
We will develop a GAN-based framework, translating heterogeneous images to match the
properties of a standard dataset, such as a template reference image, or
alternatively, one set of images chosen as a reference. We need to determine the
relevant properties within images (local or global metrics, texture, edges,
contrast, signal-to-noise ratio, etc.) and to ensure the ability of the framework to
harmonize images without losing their clinically relevant informative and content.
In the features space, numerous statistical approaches can be applied, such as
normalization or batch effect compensation. ComBat has been shown to outperform
other similar approaches and we will rely on Monte Carlo estimation for small
samples. We will investigate the combination of the batch-correction methods with
unsupervised clustering to deal with data presenting very high heterogeneity and a
very small number of samples per batch.
It might be beneficial and complementary to combine image-based and feature-based
harmonization methodologies for improving the results of multicenter radiomics
studies. We will evaluate the potential added benefit of both previously developed
approaches in improving the results. The goal of this task will be to establish
whether the first or the second approach (or the combination of both) is the most
efficient, taking into account not only the absolute improvement observed in the
results, but also the computing time and effort required to implement each approach
in practice.
5. Biomolecular analysis Biological resources from PEPPI and SIOPII ependymoma cohorts
of patients will be included. First, the diagnosis of intracranial ependymomas will
be confirmed and then pathological features will be investigated.
WHO 2020 classification of ependymomas: the cIMPACT-NOW (Consortium to Inform
Molecular and Practical Approaches to CNS Tumor Taxonomy
- - Not Official WHO) decided
to classify ependymomas according to location and the underlying genetic alteration.
The current WHO classification is under revision.
Biological studies required to classify intracranial ependymoma of the project:
Immunohistochemistry: p65-RelA, H3K27Me3. FISH analysis (classification and Copy
Number Variations). DNA-methylation profiling. RNAseq analysis.
6. Deep learning (DL) for molecular biomarkers of ependymoma For this purpose, the
Zimmer team has designed and trained deep convolutional neural networks based on the
Inception V3 architecture to distinguish ependymoma from glioma grades III and
- IV.
Preliminary results based on an imaging data set indicate a high accuracy of
discrimination and suggest the possibility to predict clinically relevant mutations
from H&E images alone (manuscript in preparation).
We will extend these DL approaches to predict resistance to therapy and metastasis.
This will be done by using the histopathology images alone either as input or in
combination with classifications based on molecular assays. At a technical level, we
will use state-of-the-art deep neural network architectures based on convolutional
layers and skip connections. To compensate for the moderate size of the training
data set, we will make heavy use of transfer learning. Here, we will make specific
use of the TGCA database, which contains images from very different cancers such as
breast cancer, and adapt successful models by retraining on a subset of the
ependymoma data acquired in this study. We also will adopt multi-scale image
representations for a better exploitation of the image information at cellular and
tissue levels, use H&E specific color normalization and methods for automated
elimination of regions subject to imaging artefacts. We also will incorporate the
developed methods in Imjoy. By decoupling the graphical user interface from the
computational back end, it enables users to access high-end computing capabilities
from any workstation or laptop.
7. Statistical analysis Several signatures will be developed and validated : a radiomic
signature (RAD-Score), a radiomic and clinical signature (RADClin-Score), a
molecular signature (MOL-score) and a global signature (Radiomic, Molecular and
clinical signature).
- - RAD-Score associated with progression-free survival (PFS).
To avoid biases,
NETSPARE built from retrospective multicenter study including independent
cohorts is a training cohort. The validation cohort to test the performance of
the radiomic signature corresponds to the UK cohort of SIOP II Program (n=220).
This division allows external validation.
Training Cohort: the median PFS is estimated to be 5 years in literature. This
corresponds to a 3.5-year PFS of 62%. With 370 patients and a median follow-up of 3.5
years, we expect at least 120 events. According to the rule of thumb recommending 10
events per variable of interest, a multiparametric model combining 10 variables may be
trained. An alternative modeling strategy based on a penalized approach will be used to
study the association between imaging parameters and PFS. As imaging biomarker data far
exceeds sample size and are intercorrelated, penalized methods (previously used in
different situations with spatial data) will be used for the regularization and selection
of variables, by encouraging the grouping effect and select explanatory variables.
Validation cohort: 66 events are expected. With this number of events, it is possible to
detect with 80% power a hazard ratio of 0.5 between the low- and high-risk group (Logrank
test two-sided 0.05).
Demographic data: Continuous variables will be summarized by cohort using median,
minimum, maximum and number of available observations. Qualitative variables will be
summarized using counts, percentages and number of missing data. Comparison between
cohort will be performed using the chi-square test (or Fisher exact test if applicable)
for qualitative variables and the Mann-Whitney test for quantitative variables.
- - Radiomics signature development and validation: To study the association between
imaging features and PFS, we will use an alternative modeling strategy based on the
penalized Cox regression model.
Elastic Net method encourages grouping effect and
selects explanatory variables. We will perform a 10-fold cross-validation to select
the best penalty parameter lambda. The mixing parameter α, other parameter of the
Elastic net method, will be set to a default value of 0.5. The regression
coefficients associated with each state of the different variables will be used to
determine the RAD score. As the outcome is time-dependent, the risk score will be
dichotomized using a time-dependent ROC curve to obtain a classifier (risk groups:
poor vs.#46;intermediate vs.#46;good prognosis). The optimal threshold has been determined
to obtain at 3 years a sensitivity of at least 80% and the best specificity.
An interval validation will be performed using a leave k-out cross-validation. We will
then apply the RAD-score signature to the validation cohort. Performances of the score
will be evaluated using different criteria: monotonicity of the prognostic stratification
assessed using Kaplan-Meier survival curves and Hazard Ratio (HR) estimation,
discrimination evaluated by Harrell's C-Index and the D statistics, quality of the model
and the proportion of explained variation estimated by the Akaike Information Criterion
(AIC) and the RD² statistic, respectively. The ability of prognostic score to identify
patients at high risk of relapse during the first 3 years will be evaluated by
determining the sensitivity, specificity and predictive value using a time-dependent ROC
curve.
- - RADClin-Score development and validation: To incorporate clinical and imaging
informations, we will combine both low-dimensional clinical and high-dimensional
imaging data in a global prediction model.
First, we will completely ignore the
imaging features. A Cox model will be fitted to identify clinical covariates
associated with PFS. We will build a clinical linear predictor using a regression
coefficient. Next, we will use Elastic Net with the clinical linear predictor in
offset to identify imaging features. Finally, we will compute the RADClin-Score
using a regression coefficient. We will plot a Venn diagram to compare feature
selection with the RADClin-Score and the RAD-Score. We will compare the model using
the different criteria previously presented.
