Modélisation de la diffraction des ondes de cisaillement en élastographie dynamique ultrasonore

L'élastographie ultrasonore est une technique d'imagerie émergente destinée à cartographier les paramètres mécaniques des tissus biologiques, permettant ainsi d’obtenir des informations diagnostiques additionnelles pertinentes. La méthode peut ainsi être perçue comme une extension quantitative et objective de l'examen palpatoire. Diverses techniques élastographiques ont ainsi été proposées pour l'étude d'organes tels que le foie, le sein et la prostate et. L'ensemble des méthodes proposées ont en commun une succession de trois étapes bien définies: l'excitation mécanique (statique ou dynamique) de l'organe, la mesure des déplacements induits (réponse au stimulus), puis enfin, l'étape dite d'inversion, qui permet la quantification des paramètres mécaniques, via un modèle théorique préétabli. Parallèlement à la diversification des champs d'applications accessibles à l'élastographie, de nombreux efforts sont faits afin d'améliorer la précision ainsi que la robustesse des méthodes dites d'inversion. Cette thèse regroupe un ensemble de travaux théoriques et expérimentaux destinés à la validation de nouvelles méthodes d'inversion dédiées à l'étude de milieux mécaniquement inhomogènes. Ainsi, dans le contexte du diagnostic du cancer du sein, une tumeur peut être perçue comme une hétérogénéité mécanique confinée, ou inclusion, affectant la propagation d'ondes de cisaillement (stimulus dynamique). Le premier objectif de cette thèse consiste à formuler un modèle théorique capable de prédire l'interaction des ondes de cisaillement induites avec une tumeur, dont la géométrie est modélisée par une ellipse. Après validation du modèle proposé, un problème inverse est formulé permettant la quantification des paramètres viscoélastiques de l'inclusion elliptique. Dans la continuité de cet objectif, l'approche a été étendue au cas d'une hétérogénéité mécanique tridimensionnelle et sphérique avec, comme objectifs additionnels, l'applicabilité aux mesures ultrasonores par force de radiation, mais aussi à l'estimation du comportement rhéologique de l'inclusion (i.e., la variation des paramètres mécaniques avec la fréquence d'excitation). Enfin, dans le cadre de l'étude des propriétés mécaniques du sang lors de la coagulation, une approche spécifique découlant de précédents travaux réalisés au sein de notre laboratoire est proposée. Celle-ci consiste à estimer la viscoélasticité du caillot sanguin via le phénomène de résonance mécanique, ici induit par force de radiation ultrasonore. La méthode, dénommée ARFIRE (''Acoustic Radiation Force Induced Resonance Elastography'') est appliquée à l'étude de la coagulation de sang humain complet chez des sujets sains et sa reproductibilité est évaluée.Ultrasound elastography is an emerging technology derived from the concept of manual palpation and dedicated to the mapping of biological tissue mechanical properties in a diagnostic context. Various elastographic approaches have been applied to the study of organs such as the liver, breast or prostate. All proposed techniques rely on a three-steps procedure: first, the tissue to be studied is mechanically excited, in a static or dynamic way. Induced displacements are then measured and used to estimate qualitatively or quantitatively mechanical properties of the medium. This step is called inversion. While application fields of elastography are constantly broadened, efforts are made to provide robust and accurate inversion algorithms. In this monography, theoretical and experimental works related to the development of new inversion methods dedicated to the study of mechanically inhomogeneous media in dynamic ultrasound elastography are provided. In the context of breast cancer diagnosis, a localized tumour can be assumed as a confined mechanical heterogeneity, also referred as an inclusion, which can disturb the propagation of shear waves (dynamic excitation). The first objective of this thesis is to provide a theoretical model to describe physical interactions occurring between incident shear waves and a tumour, here geometrically assumed as an ellipse. Once the theoretical model is validated, an inverse problem is formulated allowing further quantification of inclusion viscoelastic parameters. Aiming the development of realistic models, the previous work has been extended to the case of three dimensional spherical heterogeneities and adapted to the specific case of an acoustic radiation force excitation. Furthermore, the feasibility of assessing the medium rheological model (i.e., the frequency dependence of mechanical properties) is demonstrated. Finally, in the context of vascular diseases and blood coagulation, an inversion method based on the study of the mechanical resonance phenomenon induced by acoustic radiation force is proposed. The technique, termed ARFIRE (Acoustic Radiation Force Induced Resonance Elastography), is applied to human whole blood samples and the reproducibility of results is assessed

Semi-supervised ViT knowledge distillation network with style transfer normalization for colorectal liver metastases survival prediction

Colorectal liver metastases (CLM) significantly impact colon cancer patients, influencing survival based on systemic chemotherapy response. Traditional methods like tumor grading scores (e.g., tumor regression grade - TRG) for prognosis suffer from subjectivity, time constraints, and expertise demands. Current machine learning approaches often focus on radiological data, yet the relevance of histological images for survival predictions, capturing intricate tumor microenvironment characteristics, is gaining recognition. To address these limitations, we propose an end-to-end approach for automated prognosis prediction using histology slides stained with H&E and HPS. We first employ a Generative Adversarial Network (GAN) for slide normalization to reduce staining variations and improve the overall quality of the images that are used as input to our prediction pipeline. We propose a semi-supervised model to perform tissue classification from sparse annotations, producing feature maps. We use an attention-based approach that weighs the importance of different slide regions in producing the final classification results. We exploit the extracted features for the metastatic nodules and surrounding tissue to train a prognosis model. In parallel, we train a vision Transformer (ViT) in a knowledge distillation framework to replicate and enhance the performance of the prognosis prediction. In our evaluation on a clinical dataset of 258 patients, our approach demonstrates superior performance with c-indexes of 0.804 (0.014) for OS and 0.733 (0.014) for TTR. Achieving 86.9% to 90.3% accuracy in predicting TRG dichotomization and 78.5% to 82.1% accuracy for the 3-class TRG classification task, our approach outperforms comparative methods. Our proposed pipeline can provide automated prognosis for pathologists and oncologists, and can greatly promote precision medicine progress in managing CLM patients.Comment: 16 pages, 7 figures and 7 tables. Submitted to Medical Journal Analysis (MedIA) journa

Radiomics using computed tomography to predict CD73 expression and prognosis of colorectal cancer liver metastases

ABSTRACT: Background Finding a noninvasive radiomic surrogate of tumor immune features could help identify patients more likely to respond to novel immune checkpoint inhibitors. Particularly, CD73 is an ectonucleotidase that cata- lyzes the breakdown of extracellular AMP into immunosuppressive adenosine, which can be blocked by therapeutic antibodies. High CD73 expression in colorectal cancer liver metastasis (CRLM) resected with curative intent is associ- ated with early recurrence and shorter patient survival. The aim of this study was hence to evaluate whether machine learning analysis of preoperative liver CT-scan could estimate high vs low CD73 expression in CRLM and whether such radiomic score would have a prognostic significance. Methods We trained an Attentive Interpretable Tabular Learning (TabNet) model to predict, from preoperative CT images, stratified expression levels of CD73 (CD73High vs. CD73Low ) assessed by immunofluorescence (IF) on tissue microarrays. Radiomic features were extracted from 160 segmented CRLM of 122 patients with matched IF data, preprocessed and used to train the predictive model. We applied a five-fold cross-validation and validated the perfor- mance on a hold-out test set. Results TabNet provided areas under the receiver operating characteristic curve of 0.95 (95% CI 0.87 to 1.0) and 0.79 (0.65 to 0.92) on the training and hold-out test sets respectively, and outperformed other machine learning models. The TabNet-derived score, termed rad-CD73, was positively correlated with CD73 histological expression in matched CRLM (Spearman’s ρ = 0.6004; P < 0.0001). The median time to recurrence (TTR) and disease-specific survival (DSS) after CRLM resection in rad-CD73High vs rad-CD73 Low patients was 13.0 vs 23.6 months (P = 0.0098) and 53.4 vs 126.0 months (P = 0.0222), respectively. The prognostic value of rad-CD73 was independent of the standard clinical risk score, for both TTR (HR = 2.11, 95% CI 1.30 to 3.45, P < 0.005) and DSS (HR = 1.88, 95% CI 1.11 to 3.18, P = 0.020)

Biennale du livre de sciences humaines et sociales : La fabrique du travail

La deuxième édition de la Biennale du livre de sciences humaines et sociales s’attèle à une thématique complexe : la fabrique du travail. Ces rencontres permettent d’apporter un regard, une perspective, économique, philosophique, historique, sociologique et éthique : travailler, mais jusqu’où ? Le régime de l’intermittence est-il vraiment un modèle ? Et si, de plus en plus, on entendait : « le travail ? Non merci… ». Pour donner à réfléchir, nous avons fait appel à des chercheurs confirmés, des jeunes chercheurs - car il nous a semblé que c’était la place d’une institution que de leur donner aussi la parole - et à un écrivain

Prediction of CD3 T-cell infiltration status in colorectal liver metastases: a radiomics-based imaging biomarker

Colorectal cancer (CRC) continues to be a leading cause of cancer-related death in the developed world due to metastatic progression of the disease. In an effort to improve the understanding of tumor biology and developing prognostic tools, it was found that CD3+ tumor infiltrating lymphocytes (TIL) had a very strong prognostic value in primary CRC as well as in colorectal liver metastases (CLM). Quantification of TILs remains labor intensive and requires tissue samples, hence being of limited use in the pre-operative period or in the context of non-operable disease. Computed tomography (CT) images however are widely available for patients with CLM. In this study, we propose a pipeline to predict CD3 T-cell infiltration in CLM from pre-operative CT images. Radiomic features were extracted from 58 automatically segmented CLM lesions. Subsequently, dimensionality reduction was performed by training an autoencoder (AE) on the full feature set. We then used AE bottleneck embeddings to predict CD3 T-cell density, stratified into two categories: CD3hi and CD3low. For this, we implemented a 1D convolutional neural network (1D-CNN) and compared its performance against five machine learning models using 5-fold cross-validation. Results showed that the proposed 1D-CNN outperformed the other trained models achieving a mean accuracy of 0.69 (standard deviation [SD], 0.01) and a mean area under the receiver operating curve (AUROC) of 0.75 (SD, 0.02) on the validation set. Our findings demonstrate a relationship between CT radiomic features and CD3 tumor infiltration status with the potential of noninvasively determining CD3 status from preoperative CT images

Predicting the Response to FOLFOX-Based Chemotherapy Regimen from Untreated Liver Metastases on Baseline CT: a Deep Neural Network Approach

In developed countries, colorectal cancer is the second cause of cancer-related mortality. Chemotherapy is considered a standard treatment for colorectal liver metastases (CLM). Among patients who develop CLM, the assessment of patient response to chemotherapy is often required to determine the need for second-line chemotherapy and eligibility for surgery. However, while FOLFOX-based regimens are typically used for CLM treatment, the identification of responsive patients remains elusive. Computer-aided diagnosis systems may provide insight in the classification of liver metastases identified on diagnostic images. In this paper, we propose a fully automated framework based on deep convolutional neural networks (DCNN) which first differentiates treated and untreated lesions to identify new lesions appearing on CT scans, followed by a fully connected neural networks to predict from untreated lesions in pre-treatment computed tomography (CT) for patients with CLM undergoing chemotherapy, their response to a FOLFOX with Bevacizumab regimen as first-line of treatment. The ground truth for assessment of treatment response was histopathology-determined tumor regression grade. Our DCNN approach trained on 444 lesions from 202 patients achieved accuracies of 91% for differentiating treated and untreated lesions, and 78% for predicting the response to FOLFOX-based chemotherapy regimen. Experimental results showed that our method outperformed traditional machine learning algorithms and may allow for the early detection of non-responsive patients