2025Activity‌​‌ reportProject-TeamMONC

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 mathematical 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​​​‌ 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

3D​​ numerical simulation of a​​​‌ meningioma.

3D numerical simulation of​ a lung tumor.

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 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​​ combination 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.
• 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.​‌

General strategy of the​​ team to build meaningful​​​‌ models in oncology.

3.1 Introduction

We are​‌ working in the context​​ of data-driven medicine against​​​‌ cancer. We aim at​ coupling mathematical 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. Radiotherapy.​‌
• 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.

Since our models‌​‌ are higly data driven,​​ a transverse axis dedicated​​​‌ to data assimilation has‌ been recently added to‌​‌ our research program.

Research​​ program organisation into 3​​​‌ axes and 1 transverse‌ axis.

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

Plot showing​​ the accuracy of our​​​‌ prediction on meningioma volume.‌ Each point corresponds to‌​‌ a patient whose two​​ first exams were used​​​‌ to calibrate our model.‌ A patient-specific prediction was‌​‌ made with this calibrated​​ model and compared with​​​‌ the actual volume as‌ measured on a third‌​‌ time by clinicians. A​​ perfect prediction would be​​​‌ on the black dashed‌ line. Medical data was‌​‌ obtained from Prof. Loiseau,​​​‌ CHU Pellegrin.

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 approach, 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 movement 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​‌ 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 investigating 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 bioeletromagneticians 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 Therapies (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 understand‌​‌ 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.
• 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 radiofrequency 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 interacting​​​‌ 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 understanding​​​‌ 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 dynamical 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 hypotheses 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 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 interaction with​​ conceptual approaches, and aims​​​‌ at addressing more fundamental​ questions in biology, combined​‌ with experiments on yeast​​ cells. It is led​​​‌ by two projects. The​ EvoMulti project (CNRS 80Prime)​‌ is a collaboration with​​ biologist B. Daignan-Fornier at​​​‌ IBGC, and deals with​ experimental evolution combined with​‌ mathematical modeling. The long-term​​ goal is to question​​​‌ the link between cancer​ evolution and multicellularity. The​‌ TISSAGE project (CNRS MITI)​​ is a collaboration with​​​‌ IBGC and F. Gross​ (philosopher of science) at​‌ ImmunoConcept. The goal is​​ to develop and exploit​​​‌ the analogy between a​ metabolic network and a​‌ tissue, in terms of​​ their regulation. This relies​​​‌ on conceptual and modeling​ approaches combined with experiments.​‌

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 and angiosarcoma,
• Kidney​​ cancer and its metastases,​​​‌
• Non small cell lung​ carcinoma,
• Acute Myeloid Leukemia.​‌

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 population dynamics​‌ of yeast organisms, aiming​​ the investigation of fundamental​​​‌ hallmarks of cancer (multicellularity​ disease, escape from regulation).​‌

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).​​

Nicolas Papadakis is deputy​​​‌ director of IMB, in​ charge of the sustainable​‌ development and social responsability.​​

Olivier Saut is in​​​‌ charge of sustainable development​ at CNRS Mathématiques and​‌ member 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

Until 2025,​​​‌ Christèle Etchegaray was in‌ charge of the gender‌​‌ equity committee for IMB,​​ and correspondent on this​​​‌ topic for IMB and‌ MARGAUx Federation for CNRS‌​‌ (Insmi). She still is​​ a member of the​​​‌ Parity, Diversity Equity group‌ of IMB, and of‌​‌ the local Inria parity-equality​​ committee. She takes part​​​‌ in several diffusion initiatives‌ to promote gender equity‌​‌ in science.

Christèle Etchegaray​​ and Nicolas Papadakis also​​​‌ co-supervised the M2 internship‌ of Julie Lesthelle in‌​‌ sociology, in collaboration with​​ Sophie Duchesne (Centre Émile​​​‌ Durkheim): "Undergraduate Mathematics Studies‌ Through the Lens of‌​‌ Gender: The Construction of​​ a Masculine Environment and​​​‌ the Experiences of Women‌ Students in a Minority‌​‌ Situation". The study focused​​ on female undergraduate students​​​‌ in Mathematics at Bordeaux‌ University. This led to‌​‌ a conference given for​​ IMB, Labri and Inria​​​‌ centre of Bordeaux University,‌ and drew the attention‌​‌ fo the national mathematics​​ community. The aim is​​​‌ now to recruit Julie‌ Lesthelle as an engineer‌​‌ to pursue the project.​​

7.1 Latest software developments​​

8.1 AI​​ for medical imaging

Participants:​​​‌ Baudouin Denis de Senneville​, Nicolas Papadakis.​‌

This first axis of​​ research highlights advances in​​​‌ medical imaging and computational​ modeling for clinical applications.​‌ At CBMS’25, Hadj Bouzid​​ et al. and Petitpas​​​‌ et al. presented innovative​ approaches for airway abnormality​‌ segmentation on UTE-MRI and​​ automated detection of pleural​​​‌ plaques in asbestos-exposed individuals,​ respectively—both leveraging deep learning​‌ and reinforcement learning to​​ improve diagnostic accuracy. Robert​​​‌ et al. further contributed​ to this domain with​‌ frameworks for 3D semantic​​ cell segmentation, unsupervised tissue​​​‌ analysis, and segmentation improvements​ in electron microscopy, demonstrating​‌ the integration of intercellular​​ priors and synthetic data​​​‌ to refine biomedical imaging​ workflows. Finally, Turcotte et​‌ al.’s large-scale morphological analysis​​ of 55,213 stones in​​​‌ the World Journal of​ Urology underscores the potential​‌ of AI integration in​​ endoscopic stone recognition, bridging​​​‌ computational innovation with clinical​ practice.

The second theme​‌ centers on theoretical and​​ algorithmic advancements in computational​​​‌ methods. Renaud et al.​ made significant strides in​‌ stochastic optimization and sampling,​​ presenting analyses of Langevin​​​‌ diffusion stability and proximal​ MCMC convergence at NeurIPS’25​‌ and SSVM’25, alongside work​​ on equivariant denoisers for​​​‌ image restoration. Spagnoletti et​ al. introduced LATINO-PRO, a​‌ latent consistency solver with​​ prompt optimization, at ICCV’25,​​​‌ pushing boundaries in inverse​ problem-solving.

• A. I. Hadj​‌ Bouzid et al. 3D​​ semantic segmentation of airway​​​‌ abnormalities on UTE-MRI with​ reinforcement learning on deep​‌ supervision. nternational Symposium on​​ Biomedical Imaging (ISBI'25), United​​​‌ States, 2025
• Y. Petitpas​ et al. Automatic detection​‌ of pleural plaques presence​​ in asbestos-exposed individuals. International​​​‌ Symposium on Computer-Based Medical​ Systems (CBMS'25), Spain, 2025​‌
• M. Renaud et al.​​ From stability of Langevin​​​‌ diffusion to convergence of​ proximal MCMC for non-log-concave​‌ sampling. 39th Annual Conference​​ on Neural Information Processing​​​‌ Systems (NeurIPS'25), United States,​ 2025
• M. Renaud et​‌ al. Convergence Analysis of​​ a Proximal Stochastic Denoising​​​‌ Regularization Algorithm. International Conference​ on Scale Space and​‌ Variational Methods in Computer​​ Vision (SSVM'25), United Kingdom,​​​‌ 2025
• M. Renaud et​ al. Equivariant Denoisers for​‌ Image Restoration.International Conference on​​ Scale Space and Variational​​​‌ Methods in Computer Vision​ (SSVM'25), United Kingdom, 2025​‌
• F. Robert et al.​​ 3D Semantic Cell Segmentation​​​‌ via Propagation of 2D​ results and Integration of​‌ Intercellular Priors.International Symposium on​​ Computer-Based Medical Systems (CBMS'25),​​​‌ Spain, 2025
• F. Robert​ et al. A Comprehensive​‌ Framework for Unsupervised Deep​​ Analysis of Tissue Bioarchitecture.International​​ Symposium on Computer-Based Medical​​​‌ Systems (CBMS'25), Spain, 2025‌
• F. Robert et al.‌​‌ Improving cell instance segmentation​​ in scanning electron microscopy​​​‌ via semantic image synthesis,‌ International Symposium on Biomedical‌​‌ Imaging (ISBI'25), United States,​​ 2025
• F. Robert et​​​‌ al. Enhancing cell instance‌ segmentation in scanning electron‌​‌ microscopy images via a​​ deep contour closing operator.Computers​​​‌ in Biology and Medicine,‌ 2025
• A. Spagnoletti et‌​‌ al. LATINO-PRO: LAtent consisTency​​ INverse sOlver with PRompt​​​‌ Optimization. IEEE International Conference‌ on Computer Vision (ICCV'25),‌​‌ United States, 2025
• B.​​ Turcotte et al. Comprehensive​​​‌ analysis of 55,213 stones:‌ understanding common morphological associations‌​‌ advances endoscopic stone recognition​​ and AI integration. World​​​‌ Journal of Urology, 2025.‌

8.2 Modeling, Data assimilation‌​‌ and AI for cancer​​ biology and pulmonary diseases​​​‌

Participants: Annabelle Collin,‌ Christèle Etchegaray, Olivier‌​‌ Saut.

A significant​​ body of this research​​​‌ focuses on applying deep‌ learning and multimodal data‌​‌ integration to improve cancer​​ prognosis and treatment evaluation.​​​‌ Michot et al. demonstrated‌ the use of deep‌​‌ learning on digital pathology​​ images to predict metastatic​​​‌ relapse-free survival in soft-tissue‌ sarcomas, and highlighted the‌​‌ smaller importance of tumor​​ margin compared with tumor​​​‌ center and periphery in‌ the risk assessment. Similarly,‌​‌ Beltzung et al. leveraged​​ deep learning to quantify​​​‌ immune cells and assess‌ prognosis in radiotherapy-treated oropharyngeal‌​‌ squamous cell carcinomas, offering​​ new insights for personalized​​​‌ treatment strategies. Meanwhile, Costa‌ et al. used deep‌​‌ learning in a multicenter​​ pilot study to accurately​​​‌ predict the prognosis of‌ gynecologic smooth muscle tumors‌​‌ of uncertain malignant potential,​​ reinforcing the role of​​​‌ AI in addressing complex‌ diagnostic challenges. Chouleur et‌​‌ al. further advanced this​​ field by integrating transcriptomics,​​​‌ proteomics, and radiomics data‌ to predict recurrence in‌​‌ IDH-mutant gliomas, showcasing the​​ power of multimodal approaches​​​‌ in oncology.

Another key‌ area of innovation lies‌​‌ in mathematical modeling and​​ data assimilation to integrate​​​‌ and interpret longitudinal biological‌ data. Ciavolella et al.‌​‌ developed a parameterized mathematical​​ model to decipher the​​​‌ binding dynamics of circulating‌ tumor cells in microfluidic‌​‌ systems, providing a framework​​ for understanding metastasis and​​​‌ help the identification of‌ new therapeutic targets. Finally,‌​‌ Decoene et al proposed​​ a multiscale model that​​​‌ links cilia, mucus, and‌ airflow in airway clearance,‌​‌ showing stable mucus patterns​​ without ventilation and nonlinear​​​‌ feedbacks during breathing—key for‌ respiratory health and disease.‌​‌

• A. Michot et al.​​ Prognostic prediction in soft-tissue​​​‌ sarcomas using deep learning‌ and digital pathology of‌​‌ tumor and margin areas.Scientific​​ Reports, 2025
• F. Beltzung​​​‌ et al. Leveraging Deep‌ Learning for Immune Cell‌​‌ Quantification and Prognostic Evaluation​​ in Radiotherapy-Treated Oropharyngeal Squamous​​​‌ Cell Carcinomas.Laboratory Investigation, 2025‌
• T. Chouleur et al.‌​‌ A strategy for multimodal​​ integration of transcriptomics, proteomics,​​​‌ and radiomics data for‌ the prediction of recurrence‌​‌ in patients with IDH-mutant​​ gliomas.International Journal of Cancer,​​​‌ 2025
• G. Ciavolella et‌ al. Deciphering circulating tumor‌​‌ cells binding in a​​ microfluidic system thanks to​​​‌ a parameterized mathematical model,‌ Journal of Theoretical Biology,‌​‌ 2025.
• J. Costa et​​ al. Deep learning can​​​‌ accurately predict the prognosis‌ of gynecologic smooth muscle‌​‌ tumors of uncertain malignant​​​‌ potential: a multicenter pilot​ study. Laboratory Investigation, 2025​‌
• A. Decoene et al.​​ Mathematical Modeling of Mucus​​​‌ Transport in the Bronchial​ Tree with Ventilation Effects,​‌ 2026

8.3 Modeling and​​ analysis for cardiac and​​​‌ electroporation ablation therapies

Participants:​ Annabelle Collin, Clair​‌ Poignard.

The final​​ set of studies explores​​​‌ mathematical and physical modeling​ in cardiac and ablation​‌ therapies. Collin et al.​​ conducted an asymptotic analysis​​​‌ of the static bidomain​ model, specifically for pulsed​‌ field cardiac ablation, providing​​ theoretical insights into the​​​‌ behavior of electric fields​ during such procedures. Complementing​‌ this, Sutter et al.​​ investigated the correlation between​​​‌ computed electric dose maps​ and early post-operative MRI​‌ findings to evaluate the​​ effectiveness of irreversible electroporation.​​​‌ Together, these works highlight​ the importance of integrating​‌ advanced mathematical models and​​ imaging techniques to optimize​​​‌ and assess ablation therapies​ in clinical settings. Finally,​‌ at ISBI’25 and CBMS’25,​​ Désier et al. introduced​​​‌ deep learning models to​ optimize electric field distribution​‌ and dosimetry in electroporation​​ therapies, enhancing precision in​​​‌ ablation treatments.

• A. Collin​ et al. Asymptotic Analysis​‌ of the Static Bidomain​​ Model for Pulsed Field​​​‌ Cardiac Ablation. Mathematical Methods​ in the Applied Sciences,​‌ 2025
• K. Désier et​​ al. Deep modelling of​​​‌ electric field distribution for​ clinical electroporation ablation therapies.​‌ International Symposium on Biomedical​​ Imaging (ISBI'25), United States,​​​‌ 2025
• K. Désier et​ al. A deep learning-assisted​‌ hybrid model for electric​​ dosimetry in electroporation therapies.​​​‌ International Symposium on Computer-Based​ Medical Systems (CBMS'25), Spain,​‌ 2025
• O. Sutter et​​ al. Correlation between computed​​​‌ electric dose maps and​ early post-operative MRI for​‌ the evaluation of irreversible​​ electroporation. Physics in Medicine​​​‌ and Biology, 2025

9.1 Bilateral​​ contracts with industry

Participants:​​​‌ Olivier Saut.

• Contract​ with Dassault Systems (at​‌ Inria level, through the​​ Meditwin consortium).
• Contract with​​​‌ Institut Bergonié.

Participants: Astrid​ Decoene.

• Cifre contract​‌ with Siemens (with Université​​ Paris-Saclay).
• Cifre contract with​​​‌ EDF (with Inria team​ MEMPHIS)

Participants: Nicolas Papadakis​‌.

• Cifre contract with​​ Acteon (with CREATIS, Lyon)​​​‌

10.1 International research visitors​‌

10.2 European initiatives​

10.3 National initiatives​​​‌

10.4‌ Regional initiatives

11.1 Promoting scientific activities​​

11.2 Teaching -​‌ Supervision - Juries -​​ Educational and pedagogical outreach​​​‌

11.3 Popularization‌​‌

12.1 Major publications

• 1‌​‌ articleT.Tiffanie Chouleur​​, C.Christèle Etchegaray​​​‌, L.Laura Villain‌, A.Antoine Lesur‌​‌, T.Thomas Ferté​​, M.Marco Rossi​​​‌, L.Laetitia Andrique‌, C.Costanza Simoncini‌​‌, A.-S.Anne-Sophie Giacobbi​​, M.Matteo Gambaretti​​​‌, E.Egesta Lopci‌, B.Bethania Fernades‌​‌, G.Gunnar Dittmar​​, R.Rolf Bjerkvig​​​‌, B.Boris Hejblum‌, R.Rodolphe Thiebaut‌​‌, O.Olivier Saut​​, L.Lorenzo Bello​​​‌ and A.Andreas Bikfalvi‌. A strategy for‌​‌ multimodal integration of transcriptomics,​​ proteomics, and radiomics data​​​‌ for the prediction of‌ recurrence in patients with‌​‌ IDH-mutant gliomas.International​​ Journal of Cancer157​​​‌3August 2025,‌ 573-587HALDOI
• 2‌​‌ articleG.Giorgia Ciavolella​​, J.Julien Granet​​​‌, J.Jacky Goetz‌, N.Nael Osmani‌​‌, C.Christèle Etchegaray​​ and A.Annabelle Collin​​​‌. Deciphering circulating tumor‌ cells binding in a‌​‌ microfluidic system thanks to​​ a parameterized mathematical model​​​‌.Journal of Theoretical‌ Biology600March 2025‌​‌, 112029HALDOI​​
• 3 articleA.Annabelle​​​‌ Collin, C.Cédrick‌ Copol, V.Vivien‌​‌ Pianet, T.Thierry​​​‌ Colin, J.Julien​ Engelhardt, G.Guy​‌ Kantor, H.Hugues​​ Loiseau, O.Olivier​​​‌ Saut and B.Benjamin​ Taton. Spatial mechanistic​‌ modeling for prediction of​​ the growth of asymptomatic​​​‌ meningioma.Computer Methods​ and Programs in Biomedicine​‌2020HAL
• 4 article​​A.Annabelle Collin,​​​‌ T.Thibaut Kritter,​ C.Clair Poignard and​‌ O.Olivier Saut.​​ Joint state-parameter estimation for​​​‌ tumor growth model.​SIAM Journal on Applied​‌ Mathematics812March​​ 2021HALDOI
• 5​​​‌ articleA.Amandine Crombé​, C.Cynthia Perier​‌, M.Michèle Kind​​, B.Baudouin Denis​​​‌ de Senneville, F.​Francois Le Loarer,​‌ A.Antoine Italiano,​​ X.Xavier Buy and​​​‌ O.Olivier Saut.​ T2-based MRI Delta-Radiomics Improve​‌ Response Prediction in Soft-Tissue​​ Sarcomas Treated by Neoadjuvant​​​‌ Chemotherapy.Journal of​ Magnetic Resonance Imaging50​‌2August 2019,​​ 497-510HALDOI
• 6​​​‌ articleG.Guillaume Dechristé​, J.Jérôme Fehrenbach​‌, E.Elena Griseti​​, V.Valérie Lobjois​​​‌ and C.Clair Poignard​. Viscoelastic modeling of​‌ the fusion of multicellular​​ tumor spheroids in growth​​​‌ phase.Journal of​ Theoretical Biology454October​‌ 2018, 102-109HAL​​DOI
• 7 articleB.​​​‌Baudouin Denis de Senneville​, N.Nora Frulio​‌, H.Hervé Laumonier​​, C.Cécile Salut​​​‌, L.Luc Lafitte​ and H.Hervé Trillaud​‌. Liver contrast-enhanced sonography:​​ Computer-assisted differentiation between focal​​​‌ nodular hyperplasia and inflammatory​ hepatocellular adenoma by reference​‌ to microbubble transport patterns​​.European Radiology2020​​​‌HALDOI
• 8 article​B.Baudouin Denis de​‌ Senneville, F. Z.​​Fatma Zohra Khoubai,​​​‌ M.Marc Bevilacqua,​ A.Alexandre Labedade,​‌ K.Kathleen Flosseau,​​ C.Christophe Chardot,​​​‌ S.Sophie Branchereau,​ J.Jean Ripoche,​‌ S.Stefano Cairo,​​ E.Etienne Gontier and​​​‌ C.Christophe Grosset.​ Deciphering tumour tissue organization​‌ by 3D electron microscopy​​ and machine learning.​​​‌Communications Biology41​2021, 1390HAL​‌DOI
• 9 articleO.​​Olivier Gallinato, B.​​​‌Baudouin Denis de Senneville​, O.Olivier Seror​‌ and C.Clair Poignard​​. Numerical Workflow of​​​‌ Irreversible Electroporation for Deep-Seated​ Tumor.Physics in​‌ Medicine and Biology64​​5March 2019,​​​‌ 055016HALDOI
• 10​ articleO.Olivier Gallinato​‌ and C.Clair Poignard​​. Superconvergent second order​​​‌ Cartesian method for solving​ free boundary problem for​‌ invadopodia formation.Journal​​ of Computational Physics339​​​‌June 2017, 412​ - 431HALDOI​‌
• 11 articleS.Samuel​​ Hurault, A.Antonin​​​‌ Chambolle, A.Arthur​ Leclaire and N.Nicolas​‌ Papadakis. Convergent plug-and-play​​ with proximal denoiser and​​​‌ unconstrained regularization parameter.​Journal of Mathematical Imaging​‌ and Vision2024.​​ In press. HAL
• 12​​​‌ inproceedingsS.Samuel Hurault​, U.Ulugbek Kamilov​‌, A.Arthur Leclaire​​ and N.Nicolas Papadakis​​​‌. Convergent Bregman Plug-and-Play​ Image Restoration for Poisson​‌ Inverse Problems.Neural​​ Information Processing Systems (NeurIPS'23)​​​‌New Orleans, United States​December 2023HAL
• 13​‌ articleD.-C.Diane-Charlotte Imbs​​, R.Raouf El​​ Cheikh, A.Arnaud​​​‌ Boyer, J.Joseph‌ Ciccolini, C.Celine‌​‌ Mascaux, B.Bruno​​ Lacarelle, F.Fabrice​​​‌ Barlesi, D.Dominique‌ Barbolosi and S.Sébastien‌​‌ Benzekry. Revisiting bevacizumab​​ + cytotoxics scheduling using​​​‌ mathematical modeling: proof of‌ concept study in experimental‌​‌ non-small cell lung carcinoma​​.CPT: Pharmacometrics and​​​‌ Systems Pharmacology2018,‌ 1-9HALDOI
• 14‌​‌ articleG.Guillaume Lefebvre​​, F.François Cornelis​​​‌, P.Patricio Cumsille‌, T.Thierry Colin‌​‌, C.Clair Poignard​​ and O.Olivier Saut​​​‌. Spatial modelling of‌ tumour drug resistance: the‌​‌ case of GIST liver​​ metastases Mathematical Medicine and​​​‌ Biology Advance.Mathematical‌ Medicine and Biology00‌​‌2016, 1 -​​ 26HALDOI
• 15​​​‌ articleC.Charles Mesguich‌, E.Elif Hindie‌​‌, B.Baudouin Denis​​ de Senneville, G.​​​‌Ghoufrane Tlili, J.-B.‌Jean-Baptiste Pinaquy, G.‌​‌Gerald Marit and O.​​Olivier Saut. Improved​​​‌ 18-FDG PET/CT diagnosis of‌ multiple myeloma diffuse disease‌​‌ by radiomics analysis.​​Nuclear Medicine Communications42​​​‌10October 2021,‌ 1135-1143HALDOI
• 16‌​‌ articleF.Fabio Raman​​, E.Elizabeth Scribner​​​‌, O.Olivier Saut‌, C.Cornelia Wenger‌​‌, T.Thierry Colin​​ and H. M.Hassan​​​‌ M Fathallah-Shaykh. Computational‌ Trials: Unraveling Motility Phenotypes,‌​‌ Progression Patterns, and Treatment​​ Options for Glioblastoma Multiforme​​​‌.PLoS ONE11‌1January 2016HAL‌​‌DOI
• 17 inproceedingsM.​​Marien Renaud, V.​​​‌Valentin de Bortoli,‌ A.Arthur Leclaire and‌​‌ N.Nicolas Papadakis.​​ From stability of Langevin​​​‌ diffusion to convergence of‌ proximal MCMC for non-log-concave‌​‌ sampling.NeurIPS 2025​​ - 39th Annual Conference​​​‌ on Neural Information Processing‌ SystemsSan Diego (California),‌​‌ United StatesDecember 2025​​HAL

12.2 Publications of​​​‌ the year