RESEARCH CENTRE

Inria Centre
at the University of Bordeaux

2023
ACTIVITY REPORT

IN PARTNERSHIP WITH:

CNRS, Institut Polytechnique de Bordeaux

Project-Team

MONC

Mathematical modeling for Oncology

IN COLLABORATION WITH: Institut de Mathématiques de Bordeaux (IMB)

DOMAIN

Digital Health, Biology and Earth

THEME

Modeling and Control for Life Sciences

Contents

Project-Team MONC

1 Team members, visitors, external collaborators

2 Overall objectives

2.1 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 General strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1

2

3
3
4

3 Research program

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2 Axis 1: Tumor modeling for patient-specific simulations . . . . . . . . . . . . . . . . . . . . .
3.3 Axis 2: Bio-physical modeling for personalized therapies . . . . . . . . . . . . . . . . . . . . .
3.4 Axis 3: Quantitative cancer modeling for biological studies

6
6
7
9
. . . . . . . . . . . . . . . . . . . 11

4 Application domains

12
4.1 Tumor growth monitoring and therapeutic evaluation . . . . . . . . . . . . . . . . . . . . . . 12
4.2 Biophysical therapies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
4.3 In-vitro and animals experimentations in oncology . . . . . . . . . . . . . . . . . . . . . . . . 12

5 Social and environmental responsibility

13
5.1 Footprint of research activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
5.2 Impact of research results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
5.3 Gender equity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

6 Highlights of the year

13
6.1 Starting projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
6.2 Defenses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
6.3 Software testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
6.4 Awards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

7 New results

14
7.1 AI for medical images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
7.2 Electroporation modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

8 Bilateral contracts and grants with industry

14
8.1 Bilateral contracts with industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

9 Partnerships and cooperations

15
9.1 National initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
9.1.1 Plan Cancer MECI
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
9.1.2 ANR Project MIRE4VTACH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
9.1.3 ANR Project HOLIBRAIN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
9.1.4 French Federation for Cardiology grant DIELECTRIC . . . . . . . . . . . . . . . . . . . 16
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
9.1.5 80Prime CNRS call

10 Dissemination

16
10.1 Promoting scientific activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
10.1.1 Scientific events: organisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
10.1.2 Journal
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
10.1.3 Invited talks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
10.1.4 Scientific expertise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
10.1.5 Research administration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
10.2 Teaching - Supervision - Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
10.2.1 Teaching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
10.2.2 Supervision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

10.2.3 Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
10.3 Popularization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
10.3.1 Interventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

11 Scientific production

18
11.1 Major publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
11.2 Publications of the year

Project MONC

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Project-Team MONC

Creation of the Project-Team: 2016 November 01

Keywords

Computer sciences and digital sciences

A6. – Modeling, simulation and control

A6.1. – Methods in mathematical modeling

A6.1.1. – Continuous Modeling (PDE, ODE)

A6.1.4. – Multiscale modeling

A6.1.5. – Multiphysics modeling

A6.2. – Scientific computing, Numerical Analysis & Optimization

A6.2.1. – Numerical analysis of PDE and ODE

A6.2.4. – Statistical methods

A6.2.6. – Optimization

A6.2.7. – High performance computing

A6.3. – Computation-data interaction

A6.3.1. – Inverse problems

A6.3.2. – Data assimilation

A6.3.3. – Data processing

A6.3.4. – Model reduction

A6.5. – Mathematical modeling for physical sciences

A6.5.2. – Fluid mechanics

A9. – Artificial intelligence

A9.2. – Machine learning

Other research topics and application domains

B1.1.7. – Bioinformatics

B1.1.8. – Mathematical biology

B1.1.10. – Systems and synthetic biology

B2.2.3. – Cancer

B2.4.2. – Drug resistance

B2.6.1. – Brain imaging

B2.6.3. – Biological Imaging

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Inria Annual Report 2023

1 Team members, visitors, external collaborators

Research Scientists

• Clair Poignard [Team leader, INRIA, Senior Researcher, HDR]

• Baudouin Denis de Senneville [CNRS, Researcher, HDR]

• Christele Etchegaray [INRIA, Researcher]

• Nicolas Papadakis [CNRS, Senior Researcher, from Jul 2023, HDR]

• Olivier Saut [CNRS, Senior Researcher, HDR]

Faculty Members

• Annabelle Collin [BORDEAUX INP, Associate Professor, HDR]

• David Dean [Université de Bordeaux, HDR]

Post-Doctoral Fellows

• Giorgia Ciavolella [INRIA, Post-Doctoral Fellow]

• Van Linh Le [Institut Bergonie]

• Emma Leschiera [Inria, until Oct 2023]

PhD Students

• Simon Bihoreau [INRIA]

• Khaoula Chahdi [INRIA]

• Kylian Desier [UNIV BORDEAUX]

• Audrey Gossard [INRIA, from Nov 2023]

• Julien Granet [CNRS]

• Eloise Inacio [INRIA]

• Pedro Jaramillo Aguayo [BORDEAUX INP, ATER]

• Guillaume Martinroche [UNIV BORDEAUX]

• Virginie Montalibet [UNIV BORDEAUX]

• Simone Nati Poltri [BORDEAUX INP]

• Yannis Petitpas [UNIV BORDEAUX, from Oct 2023]

• Florian Robert [UNIV BORDEAUX]

• Olivier Sutter [CHU AVICIENNE AP-HP]

Technical Staff

• Jerome Faure [Inria, until Jul 2023]

• Luc Lafitte [INRIA, Engineer]

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Interns and Apprentices

• Victor Bousch [Inria, from Mar 2023 until May 2023]

• Maria Correa Abendano [Inria]

• Mouna Naim [Inria]

• Camilla Paleari [Inria, from Oct 2023]

Administrative Assistant

• Fabienne Cuyollaa [BORDEAUX INP, from Aug 2023]

External Collaborator

• Damien Voyer [EIGSI]

2 Overall objectives

2.1 Objectives

The MONC project-team aims at developing new mathematical models from partial differential equations
and statistical methods and based on biological and medical knowledge. Our goal is ultimately to be
able to help clinicians and/or biologists to better understand, predict or control the evolution of the
disease and possibly evaluate the therapeutic response, in a clinical context or for pre-clinical studies.
We develop patient-specific approaches (mainly based on medical images) as well as population-type
approaches in order to take advantage of large databases.

In vivo modeling of tumors is limited by the amount of information available. However, recently,
there have been dramatic increases in the scope and quality of patient-specific data from non-invasive
imaging methods, so that several potentially valuable measurements are now available to quantitatively
measure tumor evolution, assess tumor status as well as anatomical or functional details. Using different
techniques from biology or imaging - such as CT scan, magnetic resonance imaging (MRI), or positron
emission tomography (PET) - it is now possible to evaluate and define tumor status at different levels or
scales: physiological, molecular and cellular.

In the meantime, the understanding of the biological mechanisms of tumor growth, including the
influence of the micro-environment, has greatly increased. Medical doctors now have access to a wide
spectrum of therapies (surgery, mini-invasive techniques, radiotherapies, chemotherapies, targeted
therapies, immunotherapies. . . ).

Our project aims at helping oncologists in their followup of patients via the development of novel
quantitative methods for evaluation cancer progression. The idea is to build phenomenological mathem-
atical models based on data obtained in the clinical imaging routine like CT scans, MRIs and PET scans.
We therefore want to offer medical doctors patient-specific tumor evolution models, which are able to
evaluate – on the basis of previously collected data and within the limits of phenomenological models –
the time evolution of the pathology at subsequent times and the response to therapies. More precisely,
our goal is to help clinicians answer the following questions thanks to our numerical tools:

1. When is it necessary to start a treatment?

2. What is the best time to change a treatment?

3. When to stop a treatment?

We also intend to incorporate real-time model information for improving the accuracy and efficacy of
non invasive or micro-invasive tumor ablation techniques like acoustic hyperthermia, electroporation,
radio-frequency, cryo-ablation and of course radiotherapies.

There is therefore a dire need of integrating biological knowledge into mathematical models based on
clinical or experimental data. A major purpose of our project is also to create new mathematical models

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Inria Annual Report 2023

Figure 1: 3D numerical simulation of a meningioma. The tumor is shown in red.

Figure 2: 3D numerical simulation of a lung tumor. The tumor is shown in yellow.

and new paradigms for data assimilation that are adapted to the biological nature of the disease and to
the amount of multi-modal data available.

2.2 General strategy

Our general strategy may be described with the following sequence:

• Stage 1: Derivation of mechanistic models based on the biological knowledge and the available
observations. The construction of such models relies on the up-to-date biological knowledge at
the cellular level including description of the cell-cycle, interaction with the microenvironement
(angiogenesis, interaction with the stroma). Such models also include a "macroscopic" description
of specific molecular pathways that are known to have a critical role in carcinogenesis or that
are targeted by new drugs. We emphasize that for this purpose, close interactions with biologists
are crucial. Lots of works devoted to modeling at the cellular level are available in the literature.
However, in order to be able to use these models in a clinical context, the tumor is also to be
described at the tissue level. The in vitro mechanical characterization of tumor tissues has been
widely studied. Yet no description that could be patient specific or even tumor specific is available.
It is therefore necessary to build adapted phenomenological models, according to the biological
and clinical reality.

• Stage 2: Data collection. In the clinical context, data may come from medical imaging (MRI, CT-
Scan, PET scan) at different time points. We need longitudinal data in time in order to be able to
understand or describe the evolution of the disease. Data may also be obtained from analyses of
blood samples, biopsies or other quantitative biomarkers. A close collaboration with clinicians
is required for selecting the specific cases to focus on, the understanding of the key points and

Project MONC

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data, the classification of the grades of the tumors, the understanding of the treatment,. . . In the
preclinical context, data may for instance be macroscopic measurements of the tumor volume for
subcutaneous cases, green fluorescence protein (GFP) quantifications for total number of living
cells, non-invasive bioluminescence signals or even imaging obtained with devices adapted to
small animals.

– Data processing: Besides selection of representative cases by our collaborators, most of the
time, data has to be processed before being used in our models. We develop novel methods
for semi-automatic (implemented in SegmentIt) as well as supervized approaches (machine
learning or deep learning) for segmentation, non-rigid registration and extraction of image
texture information (radiomics, deep learning).

• Stage 3: Adaptation of the model to data. The model has to be adapted to data: it is useless to have
a model considering many biological features of the disease if it cannot be reliably parameterized
with available data. For example, very detailed descriptions of the angiogenesis process found in
the literature cannot be used, as they have too much parameters to determine for the information
available. A pragmatic approach has to be developed for this purpose. On the other hand, one
has to try to model any element that can be useful to exploit the image. Parameterizing must be
performed carefully in order to achieve an optimal trade-off between the accuracy of the model,
its complexity, identifiability and predictive power. Parameter estimation is a critical issue in
mathematical biology: if there are too many parameters, it will be impossible to estimate them but
if the model is too simple, it will be too far from reality.

• Stage 4: Data assimilation. Because of data complexity and scarcity - for example multimodal,
longitudinal medical imaging - data assimilation is a major challenge. Such a process is a combina-
tion of methods for solving inverse problems and statistical methods including machine learning
strategies.

– Personalized models: Currently, most of the inverse problems developed in the team are solved
using a gradient method coupled with some MCMC type algorithm. We are now trying to use
more efficient methods as Kalman type filters or so-called Luenberger filter (nudging). Using
sequential methods could also simplify Stage 3 because they can be used even with complex
models. Of course, the strategy used by the team depends on the quantity and the quality
of data. It is not the same if we have an homogeneous population of cases or if it is a very
specific isolated case.

– Statistical learning: In some clinical cases, there is no longitudinal data available to build
a mathematical model describing the evolution of the disease. In these cases (e.g. in our
collaboration with Humanitas Research Hospital on low grade gliomas or Institut Bergonié on
soft-tissue sarcoma), we use machine learning techniques to correlate clinical and imaging
features with clinical outcome of patients (radiomics). When longitudinal data and a sufficient
number of patients are available, we combine this approach and mathematical modeling
by adding the personalized model parameters for each patient as features in the statistical
algorithm. Our goal is then to have a better description of the evolution of the disease over
time (as compared to only taking temporal variations of features into account as in delta-
radiomics approaches). We also plan to use statistical algorithms to build reduced-order
models, more efficient to run or calibrate than the original models.

– Data assimilation of gene expression. "Omics" data become more and more important in
oncology and we aim at developing our models using this information as well. For example,
in our work on GIST, we have taken the effect of a Ckit mutation on resistance to treatment
into account. However, it is still not clear how to use in general gene expression data in our
macroscopic models, and particularly how to connect the genotype to the phenotype and
the macroscopic growth. We expect to use statistical learning techniques on populations
of patients in order to move towards this direction, but we emphasize that this task is very
prospective and is a scientific challenge in itself.

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Inria Annual Report 2023

Figure 3: General strategy of the team to build meaningful models in oncology.

• Stage 5: Patient-specific Simulation and prediction, Stratification. Once the mechanistic models
have been parametrized, they can be used to run patient-specific simulations and predictions. The
statistical models offer new stratifications of patients (i.t. an algorithm that tells from images and
clinical information wheter a patient with soft-tissue sarcoma is more likely to be a good or bad
responder to neoadjuvant chemotherapy). Building robust algorithms (e.g. that can be deployed
over multiple clinical centers) also requires working on quantifying uncertainties.

3 Research program

3.1 Introduction

We are working in the context of data-driven medicine against cancer. We aim at coupling mathemat-
ical models with data to address relevant challenges for biologists and clinicians in order for instance
to improve our understanding in cancer biology and pharmacology, assist the development of novel
therapeutic approaches or develop personalized decision-helping tools for monitoring the disease and
evaluating therapies.

More precisely, our research on mathematical oncology is three-fold:

• Axis 1: Tumor modeling for patient-specific simulations: Clinical monitoring. Numerical markers

from imaging data. Radiomics.

• Axis 2: Bio-physical modeling for personalized therapies: Electroporation from cells to tissue. Ra-

diotherapy.

• Axis 3: Quantitative cancer modeling for biological studies: Biological mechanisms. Metastatic

dissemination. Physical properties of microtumors.

In the first axis, we aim at producing patient-specific simulations of the growth of a tumor or its
response to treatment starting from a series of images. We hope to be able to offer a valuable insight on
the disease to the clinicians in order to improve the decision process. This would be particularly useful in
the cases of relapses or for metastatic diseases.

The second axis aims at modeling biophysical therapies like irreversible electroporation, but also
radiotherapy, thermo-ablations, radio-frequency ablations or electrochemotherapies that play a crucial
role for a local treatment of the disease.

The third axis is essential since it is a way to better understand and model the biological reality of
cancer growth and the (possibly complex) effects of therapeutic intervention. Modeling in this case
also helps to interpret the experimental results and improve the accuracy of the models used in Axis 1.
Technically speaking, some of the computing tools are similar to those of Axis 1.

Biological  and Medical knowledgeSimulationsPredictionsImaging DataData AssimilationScientiﬁc Computing Biological DataLongitudinal Multimodal Data ProcessingStatistical LearningPDE models of cancer and therapiesMonc’s skillsProject MONC

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Figure 4: Research program organisation.

Since our models are higly data driven, a transverse axis dedicated to data assimilation has been

recently added to our research program.

3.2 Axis 1: Tumor modeling for patient-specific simulations

The gold standard treatment for most cancers is surgery. In the case where total resection of the tumor
is possible, the patient often benefits from an adjuvant therapy (radiotherapy, chemotherapy, targeted
therapy or a combination of them) in order to eliminate the potentially remaining cells that may not be
visible. In this case personalized modeling of tumor growth is useless and statistical modeling will be able
to quantify the risk of relapse, the mean progression-free survival time. . . However if total resection is not
possible or if metastases emerge from distant sites, clinicians will try to control the disease for as long as
possible. A wide set of tools are available. Clinicians may treat the disease by physical interventions (radi-
ofrequency ablation, cryoablation, radiotherapy, electroporation, focalized ultrasound,. . . ) or chemical
agents (chemotherapies, targeted therapies, antiangiogenic drugs, immunotherapies, hormonotherapies).
One can also decide to monitor the patient without any treatment (this is the case for slowly growing
tumors like some metastases to the lung, some lymphomas or for some low grade glioma). A reliable
patient-specific model of tumor evolution with or without therapy may have different uses:

• Case without treatment: the evaluation of the growth of the tumor would offer a useful indication
for the time at which the tumor may reach a critical size. For example, radiofrequency ablation
of pulmonary lesion is very efficient as long as the diameter of the lesion is smaller than 3 cm.
Thus, the prediction can help the clinician plan the intervention. For slowly growing tumors,
quantitative modeling can also help to decide at what time interval the patient has to undergo a
CT-scan. CT-scans are irradiative exams and there is a challenge for decreasing their occurrence for
each patient. It has also an economical impact. And if the disease evolution starts to differ from the
prediction, this might mean that some events have occurred at the biological level. For instance,
it could be the rise of an aggressive phenotype or cells that leave a dormancy state. This kind of
events cannot be predicted, but some mismatch with respect to the prediction can be an indirect
proof of their existence. It could be an indication for the clinician to start a treatment.

• Case with treatment: a model can help to understand and to quantify the final outcome of a treat-
ment using the early response. It can help for a redefinition of the treatment planning. Modeling
can also help to anticipate the relapse by analyzing some functional aspects of the tumor. Again, a
deviation with respect to reference curves can mean a lack of efficiency of the therapy or a relapse.

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Inria Annual Report 2023

Moreover, for a long time, the response to a treatment has been quantified by the RECIST criteria
which consists in (roughly speaking) measuring the diameters of the largest tumor of the patient, as
it is seen on a CT-scan. This criteria is still widely used and was quite efficient for chemotherapies
and radiotherapies that induce a decrease of the size of the lesion. However, with the systematic
use of targeted therapies and anti-angiogenic drugs that modify the physiology of the tumor, the
size may remain unchanged even if the drug is efficient and deeply modifies the tumor behavior.
One better way to estimate this effect could be to use functional imaging (Pet-scan, perfusion or
diffusion MRI, . . . ), a model can then be used to exploit the data and to understand in what extent
the therapy is efficient.

• Optimization: currently, we do not believe that we can optimize a particular treatment in terms of
distribution of doses, number, planning with the model that we will develop in a medium term
perspective.

The scientific challenge is therefore as follows: given the history of the patient, the nature of the prim-
itive tumor, its histopathology, knowing the treatments that patients have undergone, some biological
facts on the tumor and having a sequence of images (CT-scan, MRI, PET or a mix of them), are we able to
provide a numerical simulation of the extension of the tumor and of its metabolism that fits as best as
possible with the data (CT-scans or functional data) and that is predictive in order to address the clinical
cases described above?

Our approach relies on the elaboration of PDE models and their parametrization with images by
coupling deterministic and stochastic methods. The PDE models rely on the description of the dynamics
of cell populations. The number of populations depends on the pathology. For example, for glioblastoma,
one needs to use proliferative cells, invasive cells, quiescent cells as well as necrotic tissues to be able to
reproduce realistic behaviors of the disease. In order to describe the relapse for hepatic metastases of
gastro-intestinal stromal tumor (gist), one needs three cell populations: proliferative cells, healthy tissue
and necrotic tissue.

The law of proliferation is often coupled with a model for the angiogenesis. However such models
of angiogenesis involve too many non measurable parameters to be used with real clinical data and
therefore one has to use simplified or even simplistic versions. The law of proliferation often mimics
the existence of an hypoxia threshold, it consists of an ODE. or a PDE that describes the evolution of the
growth rate as a combination of sigmoid functions of nutrients or roughly speaking oxygen concentration.
Usually, several laws are available for a given pathology since at this level, there are no quantitative
argument to choose a particular one.

The velocity of the tumor growth differs depending on the nature of the tumor. For metastases, we
will derive the velocity thanks to Darcy’s law in order to express that the extension of the tumor is basically
due to the increase of volume. This gives a sharp interface between the metastasis and the surrounding
healthy tissues, as observed by anatomopathologists. For primitive tumors like glioma or lung cancer, we
use reaction-diffusion equations in order to describe the invasive aspects of such primitive tumors.

The modeling of the drugs depends on the nature of the drug: for chemotherapies, a death term can
be added into the equations of the population of cells, while antiangiogenic drugs have to be introduced
in a angiogenic model. Resistance to treatment can be described either by several populations of cells or
with non-constant growth or death rates. As said before, it is still currently difficult to model the changes
of phenotype or mutations, we therefore propose to investigate this kind of phenomena by looking at
deviations of the numerical simulations compared to the medical observations.

The calibration of the model is achieved by using a series (at least 2) of images of the same patient
and by minimizing a cost function. The cost function contains at least the difference between the volume
of the tumor that is measured on the images with the computed one. It also contains elements on the
geometry, on the necrosis and any information that can be obtained through the medical images. We
will pay special attention to functional imaging (PET, perfusion and diffusion MRI). The inverse problem
is solved using a gradient method coupled with some Monte-Carlo type algorithm. If a large number
of similar cases is available, one can imagine to use statistical algorithms like random forests to use
some non quantitative data like the gender, the age, the origin of the primitive tumor. . . for example for
choosing the model for the growth rate for a patient using this population knowledge (and then to fully
adapt the model to the patient by calibrating this particular model on patient data) or for having a better

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initial estimation of the modeling parameters. We have obtained several preliminary results concerning
lung metastases including treatments and for metastases to the liver.

3.3 Axis 2: Bio-physical modeling for personalized therapies

In this axis, we investigate locoregional therapies such as radiotherapy, irreversible electroporation.
Electroporation consists in increasing the membrane permeability of cells by the delivery of high voltage
pulses. This non-thermal phenomenon can be transient (reversible) or irreversible (IRE). IRE or electro-
chemotherapy – which is a combination of reversible electroporation with a cytotoxic drug – are essential
tools for the treatment of a metastatic disease. Numerical modeling of these therapies is a clear scientific
challenge. Clinical applications of the modeling are the main target, which thus drives the scientific ap-
proach, even though theoretical studies in order to improve the knowledge of the biological phenomena,
in particular for electroporation, should also be addressed. However, this subject is quite wide and we
focus on two particular approaches: some aspects of radiotherapies and electro-chemotherapy. This
choice is motivated partly by pragmatic reasons: we already have collaborations with physicians on these
therapies. Other treatments could be probably treated with the same approach, but we do not plan to
work on this subject on a medium term.

• Radiotherapy (RT) is a common therapy for cancer. Typically, using a CT scan of the patient with
the structures of interest (tumor, organs at risk) delineated, the clinicians optimize the dose delivery
to treat the tumor while preserving healthy tissues. The RT is then delivered every day using low
resolution scans (CBCT) to position the beams. Under treatment the patient may lose weight and
the tumor shrinks. These changes may affect the propagation of the beams and subsequently
change the dose that is effectively delivered. It could be harmful for the patient especially if
sensitive organs are concerned. In such cases, a replanification of the RT could be done to adjust
the therapeutical protocol. Unfortunately, this process takes too much time to be performed
routinely. The challenges faced by clinicians are numerous, we focus on two of them:

– Detecting the need of replanification: we are using the positioning scans to evaluate the move-
ment and deformation of the various structures of interest. Thus we can detect whether or not
a structure has moved out of the safe margins (fixed by clinicians) and thus if a replanification
may be necessary. In a retrospective study, our work can also be used to determine RT margins
when there are no standard ones. A collaboration with the RT department of Institut Bergonié
is underway on the treatment of retroperitoneal sarcoma and ENT tumors (head and neck
cancers). A retrospective study was performed on 11 patients with retro-peritoneal sarcoma.
The results have shown that the safety margins (on the RT) that clinicians are currently using
are probably not large enough. The tool used in this study was developed by an engineer

0500010000150002000005000100001500020000Determination of the tumoral volumePredicted volume (in mm3) Observed volume (in mm3)VPredict = 2.47 + 1.04 * VObserved(R = 0.9823)+15%-15%10

Inria Annual Report 2023

funded by INRIA (Cynthia Périer, ADT Sesar). We used well validated methods from a level-set
approach and segmentation / registration methods. The originality and difficulty lie in the
fact that we are dealing with real data in a clinical setup. Clinicians have currently no way to
perform complex measurements with their clinical tools. This prevents them from investigat-
ing the replanification. Our work and the tools developed pave the way for easier studies on
evaluation of RT plans in collaboration with Institut Bergonié. There was no modeling involved
in this work that arose during discussions with our collaborators. The main purpose of the
team is to have meaningful outcomes of our research for clinicians, sometimes it implies
leaving a bit our area of expertise.

– Evaluating RT efficacy and finding correlation between the radiological responses and the
clinical outcome: our goal is to help doctors to identify correlation between the response to
RT (as seen on images) and the longer term clinical outcome of the patient. Typically, we
aim at helping them to decide when to plan the next exam after the RT. For patients whose
response has been linked to worse prognosis, this exam would have to be planned earlier.
This is the subject of collaborations with Institut Bergonié and CHU Bordeaux on different
cancers (head and neck, pancreas). The response is evaluated from image markers (e.g. using
texture information) or with a mathematical model developed in Axis 1. The other challenges
are either out of reach or not in the domain of expertise of the team. Yet our works may tackle
some important issues for adaptive radiotherapy.

• Both IRE and electrochemotherapy are anticancerous treatments based on the same phenomenon:
the electroporation of cell membranes. This phenomenon is known for a few decades but it is still
not well understood, therefore our interest is two fold:

1. We want to use mathematical models in order to better understand the biological behavior
and the effect of the treatment. We work in tight collaboration with biologists and bioeletro-
magneticians to derive precise models of cell and tissue electroporation, in the continuity of
the research program of the Inria team-project MC2. These studies lead to complex non-linear
mathematical models involving some parameters (as less as possible). Numerical methods to
compute precisely such models and the calibration of the parameters with the experimental
data are then addressed. Tight collaborations with the Vectorology and Anticancerous Ther-
apies (VAT) of IGR at Villejuif, Laboratoire Ampère of Ecole Centrale Lyon and the Karlsruhe
Institute of technology will continue, and we aim at developing new collaborations with
Institute of Pharmacology and Structural Biology (IPBS) of Toulouse and the Laboratory of
Molecular Pathology and Experimental Oncology (LMPEO) at CNR Rome, in order to under-
stand differences of the electroporation of healthy cells and cancer cells in spheroids and
tissues.

2. This basic research aims at providing new understanding of electroporation, however it
is necessary to address, particular questions raised by radio-oncologists that apply such
treatments. One crucial question is "What pulse or what train of pulses should I apply
to electroporate the tumor if the electrodes are located as given by the medical images"?
Even if the real-time optimization of the placement of the electrodes for deep tumors may
seem quite utopian since the clinicians face too many medical constraints that cannot be
taken into account (like the position of some organs, arteries, nerves. . . ), one can expect
to produce real-time information of the validity of the placement done by the clinician.
Indeed, once the placement is performed by the radiologists, medical images are usually
used to visualize the localization of the electrodes. Using these medical data, a crucial goal
is to provide a tool in order to compute in real-time and visualize the electric field and
the electroporated region directly on theses medical images, to give the doctors a precise
knowledge of the region affected by the electric field. In the long run, this research will benefit
from the knowledge of the theoretical electroporation modeling, but it seems important to
use the current knowledge of tissue electroporation – even quite rough –, in order to rapidly
address the specific difficulty of such a goal (real-time computing of non-linear model, image
segmentation and visualization). Tight collaborations with CHU Pellegrin at Bordeaux, and
CHU J. Verdier at Bondy are crucial.

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• Radiofrequency ablation. In a collaboration with Hopital Haut Leveque, CHU Bordeaux we are
trying to determine the efficacy and risk of relapse of hepatocellular carcinoma treated by radi-
ofrequency ablation. For this matter we are using geometrical measurements on images (margins
of the RFA, distance to the boundary of the organ) as well as texture information to statistically
evaluate the clinical outcome of patients.

• Intensity focused ultrasound. In collaboration with Utrecht Medical center, we aim at tackling

several challenges in clinical applications of IFU: target tracking, dose delivery. . .

3.4 Axis 3: Quantitative cancer modeling for biological studies

With the emergence and improvement of a plethora of experimental techniques, the molecular, cellular
and tissue biology has operated a shift toward a more quantitative science, in particular in the domain
of cancer biology. These quantitative assays generate a large amount of data that call for theoretical
formalism in order to better understand and predict the complex phenomena involved. Indeed, due
to the huge complexity underlying the development of a cancer disease that involves multiple scales
(from the genetic, intra-cellular scale to the scale of the whole organism), and a large number of inter-
acting physiological processes (see the so-called "hallmarks of cancer"), several questions are not fully
understood. Among these, we want to focus on the most clinically relevant ones, such as the general laws
governing tumor growth and the development of metastases (secondary tumors, responsible of 90% of
the deaths from a solid cancer), and the physics of tumors which is crucial to quantify drug uptake for
instance.

In this context, it is thus challenging to exploit the diversity of the data available in experimental
settings (such as in vitro tumor spheroids or in vivo mice experiments) in order to improve our under-
standing of the disease and its dynamics, which in turn lead to validation, refinement and better tuning
of the macroscopic models used in the axes 1 and 2 for clinical applications.

In recent years, several new findings challenged the classical vision of the metastatic development
biology, in particular by the discovery of organism-scale phenomena that are amenable to a dynam-
ical description in terms of mathematical models based on differential equations. These include the
angiogenesis-mediated distant inhibition of secondary tumors by a primary tumor the pre-metastatic
niche or the self-seeding phenomenon Building a general, cancer type specific, comprehensive theory
that would integrate these dynamical processes remains an open challenge.

Starting from the available multi-modal data and relevant biological or therapeutic questions, our
purpose is to develop adapted mathematical models (i.e. identifiable from the data) that recapitulate the
existing knowledge and reduce it to its more fundamental components, with two main purposes:

1. to generate quantitative and empirically testable predictions that allow to assess biological hypo-

theses or

2. to investigate the therapeutic management of the disease and profile optimal experimental strategies

for anti-cancerous therapies (drug uptakes or electric field parameters for instance).

We believe that the feedback loop between theoretical modeling and experimental studies can help
to generate new knowledge and improve our predictive abilities for clinical diagnosis, prognosis, and
therapeutic decision. Let us note that the first point is in direct link with the axes 1 and 2 of the team since
it allows us to experimentally validate the models at the biological scale (in vitro and in vivo experiments)
for further clinical applications.

More precisely, we first base ourselves on a thorough exploration of the biological literature of
the biological phenomena we want to model: growth of tumor spheroids, in vivo tumor growth in
mice, initiation and development of the metastases, distribution of anti-cancerous drugs. Then we
investigate, using basic statistical tools, the data we dispose, which can range from: spatial distribution
of heterogeneous cell population within tumor spheroids, expression of cell markers (such as green
fluorescent protein for cancer cells or specific antibodies for other cell types), bioluminescence, direct
volume measurement or even intra-vital images obtained with specific imaging devices. According to
the data type, we further build dedicated mathematical models that are based either on PDEs (when
spatial data is available, or when time evolution of a structured density can be inferred from the data, for

12

Inria Annual Report 2023

instance for a population of tumors) or ODEs (for scalar longitudinal data). These models are confronted
to the data by two principal means:

1. when possible, experimental assays can give a direct measurement of some parameters (such as

the proliferation rate or the migration speed) or

2. statistical tools to infer the parameters from observables of the model.

This last point is of particular relevance to tackle the problem of the large inter-animal variability and we
use adapted statistical tools such as the mixed-effects modeling framework.

Once the models are shown able to describe the data and are properly calibrated, we use them to test
or simulate biological hypotheses. Based on our simulations, we then aim at proposing to our biological
collaborators new experiments to confirm or infirm newly generated hypotheses, or to test different
administration protocols of the drugs for in vivo and in vitro protocols.

Another motivation of this axis deals with the link between cancer evolution and multicellularity. The
goals is somehow to revisit the cancer description under the prisms of multicellularity and metabolism. A
tight collaboration with B. Daignan-Fornier ’s team at IBGC has just started with the 80 prime’s project
driven by C. Etchegaray on this topics.

4 Application domains

4.1 Tumor growth monitoring and therapeutic evaluation

Each type of cancer is different and requires an adequate model. More specifically, we are currently
working on the following diseases:

• Glioma (brain tumors) of various grades,

• Metastases to the lung, liver and brain from various organs,

• Soft-tissue sarcoma,

• Kidney cancer and its metastases,

• Non small cell lung carcinoma.

In this context our application domains are:

• Image-driven patient-specific simulations of tumor growth and treatments,

• Parameter estimation and data assimilation of medical images,

• Machine and deep learning methods for delineating the lesions and stratifying patients according

to their responses to treatment or risks of relapse.

4.2 Biophysical therapies

• Modeling of irreversible electroporation adn electrochemotherapy on biological and clinical scales.

• Evaluation of radiotherapy and radiofrequency ablation.

4.3 In-vitro and animals experimentations in oncology

• Theoretical biology of the rheology of microtumors: dynamics of a population of tumors in mutual
interactions, uptake of drugs, effect of electric field on the growth of microtumors, growth under
mechanical or chemical (hypoxia) constraints.

• Mathematical models of multicellular and unicellular yeast organism, model for metabolism.

Project MONC

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5 Social and environmental responsibility

5.1 Footprint of research activities

Numerical computations on (GPU) clusters like Plafrim. The permanent members of the team reduced
drastically their travels to scientifically relevant journey (for instance small conferences where we can
really meet scientists, stay of at least 1 week for efficient collaboration etc).

A. Collin is in charge of the sustainable development and social responsability at Enseirb-Matmeca.
In addition A. Collin is involved in "Fresques du climat" formation. She is also involved in EcoInfo CNRS
group.

N. Papadakis is co-director of IMB, in charge of the sustainable development and social responsability.
O. Saut is in charge of sustainable development at CNRS Mathématiques and membre of the National

Sustainable Development Council at CNRS.

5.2 Impact of research results

In the long run, our research could yield interesting outcomes for cancer patients. Yet we are mostly
building proofs of concept that would have to be taken over by an industrial partner for any transfer
towards clinics (like we did with Sophia Genetics in the past).

The softwares EVolution and IRENA are combined into PrimetimeIRE and into 3D Slicer extensions

for clinical purpose. PrimetimeIRE is in its test phase at Avicenne hospital, AP-HP.

5.3 Gender equity

C. Etchegaray is in charge of the gender equity committee for IMB. She is also correspondent on this topic
for CNRS (Insmi), and co-head of the local Inria parity-equality committee.

6 Highlights of the year

6.1 Starting projects

• ANR MIRE4VTACH (4 years, 250keuros, leader: A. Collin, Bdx INP, co-PI: P. Jais IHU LIRYC)

• Dielectric project (3 years, 450keuros, leader: P. Jais, IHU LIRYC, co-PIs: H. Cochet IHU, C.Poignard

Inria)

• ANR Holibrain (3 years, 550keuros, leader: P. Coupé, CNRS, co-leader: N. Papadakis, CNRS)

• Univ. Bordeaux Research project MONC4AS (2 years, 90keuros, leader: A. Collin, Bdx INP, co-PI F.

Moisan, CR Inserm)

6.2 Defenses

• HDR defense of A. Collin.

• PhD defense of P. Jaramillo-Aguayo.

6.3 Software testing

• The plugins EVOLUTION and Irena for 3DSlicer that enable to compute during the ablation the
electric field on the medical image have been tested in clinical configuration at CHU Poitiers.

6.4 Awards

• The project AI4DEEP led by L. Lafitte, B. Denis de Senneville and C. Poignard won the DEEPTECH

award at the Hackatech 2023.

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Inria Annual Report 2023

7 New results

7.1 AI for medical images

Participants:

Annabelle Collin, Baudouin Denis de Senneville, Olivier Saut.

• Integration of operator-validated contours in deformable image registration for dose accumulation

in radiotherapy.
L. Bosma et al. Physics and Imaging in Radioation Oncology, 2023, 5 (4).

• Evaluation of four tumour growth models to describe the natural history of meningiomas

J. Engelhardt et al. EBioMedicine, 2023, 94

• Generating high-resolution synthetic CT from lung MRI with ultrashort echo-times: initial evalu-

ation in cystic fibrosis.
A. Longuefosse et al. Raiology 2023, 308(1).

• Non-Contrast-Enhanced Functional Lung MRI to Evaluate Treatment Response of Allergic Bron-

chopulmonary Aspergillosis in Patients With Cystic Fibrosis: A Pilot Study
I. Benlala et al. Journal of Magnetic Resonance Imaging, In press.

• 3D motion strategy for online volumetric thermometry using simultaneous multi-slice EPI at 1.5T :

an evaluation study.
V. Ozenne et al. International Journal of Hyperthermia, 2023, 40 (1),

• Development and external validation of a prediction model for the transition from mild to moderate

or severe form of COVID-19.
M. Zysman, et al. European Radiology, In press.

7.2 Electroporation modeling

Participants:

Annabelle Collin, Clair Poignard.

• Deciphering Immediate Post-Pulse Membrane Resealing from 4-Electrode Impedance Measure-

ments by Numerical Modeling
A. Collin et al. Journal of Bioelectricity, 2023, 5 (4)

• How impedance measurements and imaging can be used to characterize the conductivity of tissues

during the workflow of an electroporation-based therapy.
O. Sutter, et al. IEEE Transactions on Biomedical Engineering, 2023.

• Percutaneous irreversible electroporation for the treatment of pancreatic insulinoma

J.-P. Tasu et al. Letter in Radiology, 2023. 2023

• Phase-field model of bilipid membrane electroporation

P. Jaramillo-Aguayo et al. Journal of Mathematical Biology, 2023, 87 (18).

8 Bilateral contracts and grants with industry

8.1 Bilateral contracts with industry

Project MONC

15

Participants: Olivier Saut.

• Collaboration contract with Sophia Genetics in the context of Plan de Relance.

• Contract with Roche.

9 Partnerships and cooperations

9.1 National initiatives

On-going research projects:

9.1.1 Plan Cancer MECI

Participants:

Annabelle Collin, Baudouin Denis de Senneville, Christèle Etchegaray,
Clair Poignard, Olivier Saut.

• Title: Mathematical Models of Electroporation Ablation Combined with Immunotherapy for Liver

and Pancreas Tumors

• Partners: INRIA Monc (leader), IPBS, AP-HP Avicenne, CHU Poitiers

• Total funds: 575k€.

9.1.2 ANR Project MIRE4VTACH

Participants:

Annabelle Collin
Christèle Etchegaray, Clair Poignard.

(PI),

Baudouin Denis

de

Senneville,

• Title: Modeling of Irreversible Electroporation for Ventricular Tachycardia

• Partners: INRIA Monc (leader), IHU LIRYC

• Total funds: 250k€.

9.1.3 ANR Project HOLIBRAIN

Participants:

Baudouin Denis de Senneville, Nicolas Papadakis (co-PI).

• Title: Holistic Brain Analysis

• Partners: Labri (leader), INRIA Monc

• Total funds: 250k€.

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Inria Annual Report 2023

9.1.4 French Federation for Cardiology grant DIELECTRIC

Participants:

Annabelle Collin, Clair Poignard.

• Title: Ablation rapide par électroporation et jumeau numérique de la tachycardie ventriculaire.

• Partners: IHU LIRYC (leader), INRIA MONC, INRIA CARMEN

• Total funds: 450keuros.

9.1.5 80Prime CNRS call

Participants:

Annabelle Collin, Christèle Etchegaray (PI), Clair Poignard.

• Title: EvoMulti: Identification de conditions favorisant la transition évolutive vers la multicellularité

: approche expérimentale et modélisation mathématique.

• Partners: INRIA MONC, IBGC

• Acronym: EvolMulti

• Total funds: 19k€.

10 Dissemination

10.1 Promoting scientific activities

10.1.1 Scientific events: organisation

Clair Poignard is member of the scientific and organisation committees of the Math-Bio-Santé days (Nov.
2023)

10.1.2 Journal

Member of the editorial boards Clair Poignard is Editor-in-Chief with R. Eftimié of Math. Mod. Nat.
Phenom. Journal.

Clair Poignard is member of the editorial board of AIMS Bioeng. Journal.

10.1.3 Invited talks

Christèle Etchegaray : Journées Normandes de modélisation et applications, LMNO, Caen University.

10.1.4 Scientific expertise

Olivier Saut was member of the expert panel for the ANR CES45 and for the National Science Council of
Poland.

Christèle Etchegaray was a reviewer for the ANR CES45.

Project MONC

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10.1.5 Research administration

Nicolas Papadakis is vice-director of the Insitute of math of Bordeaux.

Olivier Saut is a scientific delegate at CNRS Mathématiques (), in charge of interdiscplinariy and in
the steering comittee of until September 2023 then in charge of Sustainable Development and Thematic
Networks at CNRS Mathématiques. He is also a member of the Ethical comittee of University of Bordeaux.
Clair Poignard was in charge of the Commission des Emplois Recherche of the Inria Research Center

at Univ. Bordeaux.

Christèle Etchegaray is a member of IMB’s lab council.

10.2 Teaching - Supervision - Juries

10.2.1 Teaching

• Engineering School Enseirb-Matmeca of Bordeaux INP(undergraduate level): Annabelle Collin

(270h), Baudouin Denis de Senneville (20h), Nicolas Papadakis (12h)

• Master in mathematics: Nicolas Papadakis (15h)

• Engineering School ENSAM (undergraduate): Clair Poignard, (60heq).

• Cancer Biology Master: Olivier Saut and Clair Poignard (4h)

• Autumn school GDR Mathsav : Annabelle Collin (4h).

10.2.2 Supervision

The permanent staff of the Team supervises the 14 phd students.

10.2.3 Juries

Annabelle Collin was jury member of 2 PhD defense. Clair Poignard was President of the jury of 1 HDR
and 1 PhD defense, and member as reviewer of 2 PhD defenses. Nicolas Papadakis was jury member of 1
HdR defense (as reviewer) and 5 PhD defense (2 as reviewer). Olivier Saut was jury member was President
of the jury of 1 PhD defence, reviewer of 2 additional PhD defenses, member of the jury of one medical
thesis.

10.3 Popularization

10.3.1 Interventions

Annabelle Collin gave a talk at INSERM Bettencourt school for promoting applied mathematics for future
clinicians.

Annabelle Collin gave 1 talk about the environmental impact of the digital (IA4Industry)
Christèle Etchegaray gave a talk for high schools students during the "Maths week" at Bordeaux

university.

Christèle Etchegaray gave 5 talks for high schools students within the "1 scientifique, 1 classe, Chiche!"

initiative, at St Jean de Luz.

Christèle Etchegaray gave 1 talk for high schools female students during the event "Filles & Maths,

une équation lumineuse" at Pau.

Christèle Etchegaray took part in the Inria video initiative "Une minute avec...".
Olivier Saut gave 1 talk at Université Populaire de Bordeaux and at the SIN department of University

of Bordeaux.

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Inria Annual Report 2023

11 Scientific production

11.1 Major publications

[1] G. Aujay, C. Etchegaray, J.-F. Blanc, B. Lapuyade, P. Papadopoulos, M.-A. Pey, L. Bordenave, H.
Trillaud, O. Saut and J.-B. Pinaquy. ‘Comparison of MRI-based response criteria and radiomics for
the prediction of early response to transarterial radioembolization in patients with hepatocellular
carcinoma’. In: Diagnostic and Interventional Imaging 103.7-8 (1st July 2022), pp. 360–366. DOI:
10.1016/j.diii.2022.01.009. URL: https://hal.inria.fr/hal-03930592.

[2] A. Collin, C. Copol, V. Pianet, T. Colin, J. Engelhardt, G. Kantor, H. Loiseau, O. Saut and B. Taton.
‘Spatial mechanistic modeling for prediction of the growth of asymptomatic meningioma’. In:
Computer Methods and Programs in Biomedicine (2020). URL: https://hal.inria.fr/hal-023
97720.

[3] A. Collin, T. Kritter, C. Poignard and O. Saut. ‘Joint state-parameter estimation for tumor growth
model’. In: SIAM Journal on Applied Mathematics 81.2 (23rd Mar. 2021). DOI: 10.1137/20M131775
X. URL: https://hal.inria.fr/hal-02960283.

[4] A. Crombé, M. Kind, D. Fadli, F. Le Loarer, A. Italiano, X. Buy and O. Saut. ‘Intensity Harmonization
Techniques Influence Radiomics Features and Radiomics-based Predictions in Sarcoma Patients’.
In: Scientific Reports 10.1 (Dec. 2020). DOI: 10.1038/s41598-020-72535-0. URL: https://hal
.inria.fr/hal-03050686.

[5] A. Crombé, C. Perier, M. Kind, B. Denis de Senneville, F. Le Loarer, A. Italiano, X. Buy and O. Saut.
‘T2-based MRI Delta-Radiomics Improve Response Prediction in Soft-Tissue Sarcomas Treated by
Neoadjuvant Chemotherapy’. In: Journal of Magnetic Resonance Imaging 50.2 (Aug. 2019), pp. 497–
510. DOI: 10.1002/jmri.26589. URL: https://hal.inria.fr/hal-01929807.

[6] G. Dechristé, J. Fehrenbach, E. Griseti, V. Lobjois and C. Poignard. ‘Viscoelastic modeling of the
fusion of multicellular tumor spheroids in growth phase’. In: Journal of Theoretical Biology 454
(7th Oct. 2018), pp. 102–109. DOI: 10.1016/j.jtbi.2018.05.005. URL: https://hal.inria.f
r/hal-01786027.

[7] B. Denis de Senneville, N. Frulio, H. Laumonier, C. Salut, L. Lafitte and H. Trillaud. ‘Liver contrast-
enhanced sonography: Computer-assisted differentiation between focal nodular hyperplasia
and inflammatory hepatocellular adenoma by reference to microbubble transport patterns’. In:
European Radiology (2020). DOI: 10.1007/s00330-019-06566-1. URL: https://hal.science
/hal-02377657.

[8] B. Denis de Senneville, F. Z. Khoubai, M. Bevilacqua, A. Labedade, K. Flosseau, C. Chardot, S.
Branchereau, J. Ripoche, S. Cairo, E. Gontier and C. Grosset. ‘Deciphering tumour tissue organiza-
tion by 3D electron microscopy and machine learning’. In: Communications Biology 4.1 (2021),
p. 1390. DOI: 10.1038/s42003-021-02919-z. URL: https://hal.science/hal-03772518.

[9] O. Gallinato, B. Denis de Senneville, O. Seror and C. Poignard. ‘Numerical Workflow of Irreversible
Electroporation for Deep-Seated Tumor’. In: Physics in Medicine and Biology 64.5 (1st Mar. 2019),
p. 055016. DOI: 10.1088/1361-6560/ab00c4. URL: https://hal.inria.fr/hal-02063020.

[10] G. Lefebvre, F. Cornelis, P. Cumsille, T. Colin, C. Poignard and O. Saut. ‘Spatial modelling of tumour
drug resistance: the case of GIST liver metastases Mathematical Medicine and Biology Advance’.
In: Mathematical Medicine and Biology 00 (2016), pp. 1–26. DOI: 10.1093/imammb/dqw002. URL:
https://hal.inria.fr/hal-01380292.

[11] C. Mesguich, E. Hindie, B. Denis de Senneville, G. Tlili, J.-B. Pinaquy, G. Marit and O. Saut. ‘Improved
18-FDG PET/CT diagnosis of multiple myeloma diffuse disease by radiomics analysis’. In: Nuclear
Medicine Communications 42.10 (Oct. 2021), pp. 1135–1143. DOI: 10.1097/MNM.0000000000001
437. URL: https://hal.inria.fr/hal-03428536.

[12] F. Raman, E. Scribner, O. Saut, C. Wenger, T. Colin and H. M. Fathallah-Shaykh. ‘Computational
Trials: Unraveling Motility Phenotypes, Progression Patterns, and Treatment Options for Glio-
blastoma Multiforme’. In: PLoS ONE 11.1 (Jan. 2016). DOI: 10.1371/journal.pone.0146617.
URL: https://hal.inria.fr/hal-01396271.

Project MONC

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11.2 Publications of the year

International journals

[13]

I. Benlala, R. Klaar, T. Gaass, J. Macey, S. Bui, B. Denis de Senneville, P. Berger, F. Laurent, G.
Dournes and J. Dinkel. ‘Non-Contrast-Enhanced Functional Lung MRI to Evaluate Treatment
Response of Allergic Bronchopulmonary Aspergillosis in Patients With Cystic Fibrosis: A Pilot
Study’. In: Journal of Magnetic Resonance Imaging (June 2023). DOI: 10.1002/jmri.28844. URL:
https://hal.science/hal-04268654.

[14] L. Bosma, M. Ries, B. Denis de Senneville, B. Raaymakers and C. Zachiu. ‘Integration of operator-
validated contours in deformable image registration for dose accumulation in radiotherapy’. In:
Physics and Imaging in Radiation Oncology 27 (6th July 2023), p. 100483. DOI: 10.1016/j.phro.2
023.100483. URL: https://hal.science/hal-04268748.

[15] L. Bosma, C. Zachiu, B. Denis de Senneville, B. Raaymakers and M. Ries. ‘Technical Note: Intensity-
based quality assurance criteria for deformable image registration in image-guided radiotherapy’.
In: Medical Physics 50.9 (Mar. 2023), pp. 5715–5722. DOI: 10.1002/mp.16367. URL: https://hal
.science/hal-04268726.

[16] A. Collin, T. García-Sánchez, S. Corridore, L. Mir and C. Poignard. ‘Deciphering Immediate Post-
Pulse Membrane Resealing from 4-Electrode Impedance Measurements by Numerical Modeling’.
In: Journal of Bioelectricity 5.4 (1st Dec. 2023), pp. 266–278. DOI: 10.1089/bioe.2023.0012. URL:
https://inria.hal.science/hal-04361087.

[17]

J. Engelhardt, V. Montalibet, O. Saut, H. Loiseau and A. Collin. ‘Evaluation of four tumour growth
models to describe the natural history of meningiomas’. In: EBioMedicine 94 (4th July 2023),
p. 104697. DOI: 10.1016/j.ebiom.2023.104697. URL: https://hal.science/hal-04346355.

[18] P. Jaramillo-Aguayo, A. Collin and C. Poignard. ‘Phase-field model of bilipid membrane electropora-
tion’. In: Journal of Mathematical Biology 87.18 (July 2023). DOI: 10.1007/s00285-023-01956-y.
URL: https://inria.hal.science/hal-04145549.

[19] A. Longuefosse, J. Raoult, I. Benlala, B. Denis de Senneville, T. Benkert, J. Macey, S. Bui, P. Berger,
G. Ferretti, J.-Y. Gaubert, R. Liberge, A. Hutt, B. Morel, F. Laurent, F. Baldacci and G. Dournes.
‘Generating high-resolution synthetic CT from lung MRI with ultrashort echo-times: initial evalu-
ation in cystic fibrosis’. In: Radiology 308.1 (1st July 2023). DOI: 10.1148/radiol.230052. URL:
https://hal.science/hal-04268631.

[20] P. Loubet, A. Vincent, A. Collin, C. Dejous, A. Ghiotto and C. Jego. ‘Life cycle assessment of ICT in
higher education: a comparison between desktop and single-board computers’. In: International
Journal of Life Cycle Assessment (14th Jan. 2023). DOI: 10.1007/s11367- 022- 02131- z. URL:
https://inria.hal.science/hal-03938948.

[21] V. Ozenne, P. Bour, B. Denis de Senneville and B. Quesson. ‘3D motion strategy for online volumetric
thermometry using simultaneous multi-slice EPI at 1.5T: an evaluation study’. In: International
Journal of Hyperthermia 40.1 (20th Apr. 2023). DOI: 10.1080/02656736.2023.2194595. URL:
https://hal.science/hal-04107830.

[22] O. Sutter, D. Voyer, J.-P. Tasu and C. Poignard. ‘How impedance measurements and imaging can be
used to characterize the conductivity of tissues during the workflow of an electroporation-based
therapy’. In: IEEE Transactions on Biomedical Engineering (2023), pp. 1–9. DOI: 10.1109/TBME.20
23.3336193. URL: https://inria.hal.science/hal-04361148.

[23] M. Zysman, J. Asselineau, O. Saut, E. Frison, M. Oranger, A. Maurac, J. Charriot, R. Achkir, S.
Regueme, E. Klein, S. Bommart, A. Bourdin, G. Dournes, J. Casteigt, A. Blum, G. Ferretti, B. Degano,
R. Thiébaut, F. Chabot, P. Berger, F. Laurent and I. Benlala. ‘Development and external validation
of a prediction model for the transition from mild to moderate or severe form of COVID-19’. In:
European Radiology (2023). DOI: 10.1007/s00330-023-09759-x. URL: https://hal.science
/hal-04153209.

20

Inria Annual Report 2023

International peer-reviewed conferences

[24] S. Hurault, U. Kamilov, A. Leclaire and N. Papadakis. ‘Convergent Bregman Plug-and-Play Image
Restoration for Poisson Inverse Problems’. In: Neural Information Processing Systems (NeurIPS’23).
New Orleans, United States, 10th Dec. 2023. URL: https://hal.science/hal-04121529.

[25] V.-L. Le, A. Michot, A. Crombé, C. Ngo, C. Honoré, J.-M. Coindre, O. Saut and F. Le-Loarer. ‘A
Deep Attention-Multiple Instance Learning Framework to Predict Survival of Soft-Tissue Sarcoma
from Whole Slide Images’. In: Lecture Notes in Computer Science. MICCAI 2023 - CaPTion Workshop
on Cancer Prevention through early detecTion. Vol. LNCS-14295. Cancer Prevention Through
Early Detection. Vancouver, Canada: Springer Nature Switzerland, 7th Oct. 2023, pp. 3–16. DOI:
10.1007/978-3-031-45350-2_1. URL: https://inria.hal.science/hal-04235077.

[26] V.-L. Le and O. Saut. ‘Coordinate-Unet 3D for segmentation of lung parenchyma’. In: WSCG 2023 –
31th International Conference in Central Europe on Computer Graphics, Visualization and Com-
puter Vision. Pilsen, Czech Republic, 15th May 2023, pp. 36–42. DOI: 10.24132/CSRN.3301.6.
URL: https://inria.hal.science/hal-04222468.

[27] V.-L. Le and O. Saut. ‘RRc-UNet 3D for Lung Tumor Segmentation from CT Scans of Non-Small
Cell Lung Cancer Patients’. In: Proceedings of the IEEE/CVF International Conference on Computer
Vision (ICCV) Workshops. ICCV CVAMD 2023 - Workshop of International Conference on Computer
Vision. Paris, France, 2023, pp. 2316–2325. URL: https://inria.hal.science/hal-04236510.

[28] A. Longuefosse, B. Denis de Senneville, G. Dournes, I. Benlala, F. Laurent, P. Desbarats and F.
Baldacci. ‘MR to CT synthesis using GANs : a practical guide applied to thoracic imaging’. In:
VISIGRAPP 2023 - International Joint Conference on Computer Vision, Imaging and Computer
Graphics Theory and Applications. Lisbon, Portugal: SCITEPRESS - Science and Technology Public-
ations, Mar. 2023, pp. 268–274. DOI: 10.5220/0011895700003417. URL: https://hal.science
/hal-04268701.

[29] A. Longuefosse, G. Dournes, I. Benlala, B. Denis de Senneville, F. Laurent, P. Desbarats and F.
Baldacci. ‘Lung CT Synthesis Using GANs with Conditional Normalization on Registered Ultrashort
Echo-Time MRI’. In: ISBI 2023 - 20th IEEE International Symposium on Biomedical Imaging.
Cartagena de Indias, Colombia: IEEE, Apr. 2023, pp. 1–5. DOI: 10.1109/ISBI53787.2023.102303
31. URL: https://hal.science/hal-04268682.

[30] S. Nati Poltri, G. Caluori, P. Jaïs, A. Collin and C. Poignard. ‘Electrocardiology Modeling After
Catheter Ablations for Atrial Fibrillation’. In: Lecture Notes in Computer Science. FIMH 2023 - 12th
International Conference Functional Imaging and Modeling of the Heart. Vol. LNCS-13958. Lecture
Notes in Computer Science. Lyon, France, 16th June 2023, pp. 184–193. DOI: 10.1007/978-3-031
-35302-4_19. URL: https://hal.science/hal-04155159.

[31]

[32]

J. Prost, A. Houdard, A. Almansa and N. Papadakis. ‘Inverse problem regularization with hierarchical
variational autoencoders’. In: ICCV 2023 - IEEE International Conference on Computer Vision.
Paris, France, 20th Mar. 2023. URL: https://hal.science/hal-04038644.

J. Prost, A. Houdard, N. Papadakis and A. Almansa. ‘Diverse super-resolution with pretrained deep
hiererarchical VAEs’. In: International Conference on Computer Vision Theory and Applications
(VISAPP’24). Rome, Italy, 27th Feb. 2024. URL: https://hal.science/hal-03675314.

Reports & preprints

[33] S. Benzekry, M. Mastri, C. Nicolò and J. M. Ebos. Machine-learning and mechanistic modeling of
primary and metastatic breast cancer growth after neoadjuvant targeted therapy. 23rd Feb. 2023.
DOI: 10.1101/2023.02.22.529613. URL: https://hal.science/hal-04384182.

[34] G. Ciavolella, N. David and A. Poulain. Effective interface conditions for a porous medium type

problem. 7th July 2023. URL: https://hal.science/hal-03231456.

[35] G. Ciavolella, J. Granet, J. Goetz, N. Osmani, C. Etchegaray and A. Collin. Deciphering circulating tu-
mor cells binding in a microfluidic system thanks to a parameterized mathematical model. 20th Oct.
2023. DOI: 10.1101/2023.10.18.562910. URL: https://inria.hal.science/hal-04253803.

Project MONC

21

[36] E. Leschiera, G. Al-Hity, M. S. Flint, C. Venkataraman, T. Lorenzi, L. Almeida and C. Audebert. An
individual-based model to explore the impact of psychological stress on immune infiltration into
tumour spheroids. 24th July 2023. URL: https://hal.science/hal-04170128.

Other scientific publications

[37]

J.-P. Tasu, M. Vionnet, S. Velasco, L. Lafitte and C. Poignard. Percutaneous irreversible electroporation
for the treatment of pancreatic insulinoma. Mar. 2023. DOI: 10.1016/j.diii.2023.03.006. URL:
https://inria.hal.science/hal-04054322.
<scratchpad>
- Main topic: Research Centre at the University of Bordeaux
- Key points:
  - The MONC project-team aims to develop new mathematical models for cancer research
  - The team focuses on tumor modeling, bio-physical modeling, and quantitative cancer modeling
  - They use data-driven medicine to address challenges in cancer biology and pharmacology
  - The team collaborates with clinicians, biologists, and industry partners
- Structure: The document is divided into sections, including activity report, research program, application domains, social and environmental responsibility, and highlights of the year
- Potential artifacts to ignore: Links, HTML tags, and unnecessary web-related elements
</scratchpad>

<final_summary>
The MONC project-team at the University of Bordeaux is dedicated to developing new mathematical models for cancer research. The team focuses on three main areas: tumor modeling, bio-physical modeling, and quantitative cancer modeling. They use data-driven medicine to address challenges in cancer biology and pharmacology, collaborating with clinicians, biologists, and industry partners. The team aims to improve our understanding of cancer growth and development, as well as develop personalized decision-helping tools for monitoring disease progression and evaluating therapies. By integrating biological knowledge into mathematical models, the team hopes to provide valuable insights for clinicians and biologists, ultimately leading to better treatment options for cancer patients.
The MONC project-team at the University of Bordeaux is dedicated to developing new mathematical models for cancer research. The team focuses on three main areas: tumor modeling, bio-physical modeling, and quantitative cancer modeling. They use data-driven medicine to address challenges in cancer biology and pharmacology, collaborating with clinicians, biologists, and industry partners. The team aims to improve our understanding of cancer growth and development, as well as develop personalized decision-helping tools for monitoring disease progression and evaluating therapies. By integrating biological knowledge into mathematical models, the team hopes to provide valuable insights for clinicians and biologists, ultimately leading to better treatment options for cancer patients.