13 research outputs found
Predictive and prognostic value of CT based radiomics signature in locally advanced head and neck cancers patients treated with concurrent chemoradiotherapy or bioradiotherapy and its added value to Human Papillomavirus status
L'intelligence artificielle appliquée à la pathologie numérique pour découvrir de nouveaux prédicteurs de l'évolution des patientes atteintes d'un cancer du sein
L'intelligence artificielle (IA) émerge en médecine comme un nouvel outil pour améliorer la précision des stratégies diagnostiques. En identifiant les trames de reconnaissance visuelle, l'IA permet le traitement de grands ensembles de données qui, en raison de leur échelle et de leur diversité, pourraient être très difficiles à gérer s'ils étaient analysés manuellement. L'apprentissage profond (AP), un domaine de l'apprentissage automatique ou machine learning où la profondeur est générée par une séquence de couches, est une approche idéale pour la reconnaissance visuelle, comme le traitement de lames histologiques numérisées. Le mécanisme est basé sur l'entraînement d'algorithmes pour concevoir des modèles mathématiques capables de prédire la configuration dans des cas futurs. L'« entraînement » consiste à fournir d'énormes quantités de données au système et à permettre à l'algorithme de s'ajuster et de s'améliorer. Le cancer du sein (CS) est la première cause de cancer chez les femmes dans le monde. Les CS invasifs sont classés par les pathologistes en différents sous-types en fonction du grade et du type histologique et d'autres caractéristiques telles que la taille tumorale, la présence d'une invasion lympho-vasculaire, l'atteinte des ganglions lymphatiques, l'expression des récepteurs hormonaux et de HER2, ou l'index de prolifération évalué par le Ki67, avec différentes implications pronostiques et prédictives. La vaste hétérogénéité du CS a un impact particulier dans le pronostic et la réponse au traitement. De plus, il a été démontré que le risque et le moment de la récidive de la maladie dépendent également des caractéristiques tumorales mentionnées. Ce fait souligne le besoin de meilleurs outils pour la stratification des patients concernant la prise de décision thérapeutique, à la fois pour la mise en œuvre de traitements adaptés et aussi pour éviter des traitements lourds chez les patients identifiés comme ayant un risque faible de récidive. L'applicabilité de l'IA sur la pathologie numérique pourrait gérer la grande quantité de données générées afin d'optimiser son interprétation, améliorant ainsi la reconnaissance des différents sous-groupes du CS précoce qui pourraient s'associer à différentes évolutions.L'objectif de ce travail était de développer un outil de pathologie numérique basé sur l'IA pour évaluer le risque de rechute à distance à 5 ans chez les patients avec un CS invasif en phase précoce, applicable partout et à un coût abordable. Nous avons utilisé des lames tumorales colorées par hématoxyline-éosine-safran (HES) et scannées, provenant de résections chirurgicales de CS, ainsi que des données cliniques et histopathologiques, comme données d'entrée pour entraîner des réseaux de neurones afin de prédire un risque de rechute. Dans un deuxième temps, nous avons validé les résultats obtenus sur un jeu de données externe et nous avons comparé nos performances à des scores cliniques pertinents utilisés dans la pratique quotidienne, atteignant des valeurs équivalentes ou supérieures en termes de sensibilité et de spécificité. Enfin, et concernant la question de l'explainable AI, nous avons évalué les caractéristiques présentes dans les tuiles sélectionnées par les modèles mathématiques afin d'identifier les éléments responsables de la prédiction du risque, et de valider biologiquement notre approche. Nous avons conclu que notre étude met en évidence les avantages de l'IA appliquée à la pathologie numérique pour améliorer la stratification du risque des patientes atteintes d'un CS et pour élargir l'accès à des stratégies thérapeutiques personnalisées, y compris la désescalade thérapeutique.Artificial intelligence (AI) is emerging in Medicine as a novel tool for improving the precision of diagnostic strategies. By identifying patterns of recognition, AI allows the processing of large datasets, which, due to its scale and diversity, could be very difficult to handle if manually analyzed. Deep learning (DL), an area of machine learning where depth is generated by a sequence of multiple layers, is an ideal approach for visual recognition, such as the processing of scanned histological slides. The mechanism is based on training algorithms to design mathematical models that predict the configuration in future cases. “Training” involves providing huge amounts of data to the system and permitting the algorithm to adjust itself and improve. Breast cancer (BC) is the first cause of cancer in women worldwide. Invasive BCs are classified by pathologists into different subtypes using the histological grade, type, and other features such as the tumor size, the presence of lymph-vascular invasion, lymph-node involvement and expression of hormone receptors, HER2 and Ki67, with different prognostic and predictive implication. BC vast heterogeneity has a particular impact on prognosis and response to treatment. Moreover, it has been demonstrated that the risk and timing of disease recurrence also depends on tumor characteristics aforementioned. This fact highlights the need for better tools for patients' stratification regarding treatment decision, both for the implementation of tailored therapeutic schemes and for avoiding heavy treatments in the so-called low-risk patients. The applicability of AI on digital pathology could manage the data generated in order to optimize its interpretation, improving the recognition of different pathological subgroups of early BC that could associate with different outcomes.The aim of this work was to develop an AI-based digital pathology tool to assess the risk of distant relapse at 5 years in early invasive BC patients, applicable everywhere and at an affordable cost. We used scanned hematoxylin-eosin-safran (HES)-stained tumor slides from BC resection specimens, along with clinico-pathological data, as inputs to train neural networks in order to predict a risk of relapse. Subsequently, we validated the obtained results on an external dataset and we compared our performances to relevant clinical scores used in daily practice, attaining equivalent or superior values in terms of sensitivity and specificity. Finally, and regarding explainable AI question, we assessed the features present in the tiles selected by the mathematical models in order to identify the elements that supported the risk prediction, in order to find biologically coherent results that validated our approach. We concluded that our study highlights the benefit of AI-based digital pathology to improve the risk stratification of breast cancer patients and expands access to personalized therapeutic strategies, including treatment de-escalation purposes
Hyper-AdaC: Adaptive clustering-based hypergraph representation of whole slide images for survival analysis
International audienceThe emergence of deep learning in the medical field has popularized the development of models to predict survival outcomes from histopathology images in precision oncology. Graph-based formalism has opened interesting perspectives for generating informative representations, as they can be context-aware and model local and global topological structures in the tumor’s microenvironment. However, the critical issue in using graph representations lies in their generalizability. They can suffer from overfitting due to their large sizes or high discrepancies between nodes due to random sampling from WSI. In addition, standard graph formulations are limited to pairwise interactions, which can sometimes fail to represent the reality observed in histopathology and hinder the interpretability of those interactions. In this work, we present Hyper-AdaC, an adaptive clustering-based hypergraph representation to model high-order correlations among different regions of the WSIs while being compact enough to help graph neural networks generalize in the case of survival prediction. We evaluate our approach on 5 different public available cancer datasets. Our method outperforms most state-of-the-art graph-based methods for survival prediction with WSIs, creating a more efficient and robust alternative to other graph representations. Moreover, due to our formulation, attention maps are depicted at different resolutions depending on the tissue characteristics of each WSI. The code is available at: https://github.com/HakimBenkirane/Hyper-adaC
Unsupervised Nuclei Segmentation using Spatial Organization Priors
International audienceIn digital pathology, various biomarkers (e.g., KI67, HER2, CD3/CD8) are routinely analyzed by pathologists through immunohistochemistry-stained slides. Identifying these biomarkers on patient biopsies allows for a more informed design of their treatment regimen. The diversity and specificity of these types of images make the availability of annotated databases sparse. Consequently, robust and efficient learning-based diagnostic systems are difficult to develop and apply in a clinical setting. Our study builds on the observation that the overall organization and structure of the observed tissues are similar across different staining protocols. In this paper, we propose to leverage both the wide availability of hematoxylin-eosin stained databases and the invariance of tissue organization and structure in order to perform unsupervised nuclei segmentation on immunohistochemistry images. We implement and evaluate a generative adversarial method that relies on high-level nuclei distribution priors through comparison with largely available hematoxylin-eosin stained cell nuclei masks. Our approach shows promising results compared to classic unsupervised and supervised methods, as we demonstrate on two publicly available datasets. Our code is publicly available to encourage further contributions
Unsupervised Nuclei Segmentation using Spatial Organization Priors
International audienceIn digital pathology, various biomarkers (e.g., KI67, HER2, CD3/CD8) are routinely analyzed by pathologists through immunohistochemistry-stained slides. Identifying these biomarkers on patient biopsies allows for a more informed design of their treatment regimen. The diversity and specificity of these types of images make the availability of annotated databases sparse. Consequently, robust and efficient learning-based diagnostic systems are difficult to develop and apply in a clinical setting. Our study builds on the observation that the overall organization and structure of the observed tissues are similar across different staining protocols. In this paper, we propose to leverage both the wide availability of hematoxylin-eosin stained databases and the invariance of tissue organization and structure in order to perform unsupervised nuclei segmentation on immunohistochemistry images. We implement and evaluate a generative adversarial method that relies on high-level nuclei distribution priors through comparison with largely available hematoxylin-eosin stained cell nuclei masks. Our approach shows promising results compared to classic unsupervised and supervised methods, as we demonstrate on two publicly available datasets. Our code is publicly available to encourage further contributions
Hyper-AdaC: Adaptive clustering-based hypergraph representation of whole slide images for survival analysis
International audienceThe emergence of deep learning in the medical field has popularized the development of models to predict survival outcomes from histopathology images in precision oncology. Graph-based formalism has opened interesting perspectives for generating informative representations, as they can be context-aware and model local and global topological structures in the tumor’s microenvironment. However, the critical issue in using graph representations lies in their generalizability. They can suffer from overfitting due to their large sizes or high discrepancies between nodes due to random sampling from WSI. In addition, standard graph formulations are limited to pairwise interactions, which can sometimes fail to represent the reality observed in histopathology and hinder the interpretability of those interactions. In this work, we present Hyper-AdaC, an adaptive clustering-based hypergraph representation to model high-order correlations among different regions of the WSIs while being compact enough to help graph neural networks generalize in the case of survival prediction. We evaluate our approach on 5 different public available cancer datasets. Our method outperforms most state-of-the-art graph-based methods for survival prediction with WSIs, creating a more efficient and robust alternative to other graph representations. Moreover, due to our formulation, attention maps are depicted at different resolutions depending on the tissue characteristics of each WSI. The code is available at: https://github.com/HakimBenkirane/Hyper-adaC
Unsupervised Nuclei Segmentation using Spatial Organization Priors
In digital pathology, various biomarkers (e.g., KI67, HER2, CD3/CD8) are routinely analysed by pathologists through immunohistochemistry-stained slides. Identifying these biomarkers on patient biopsies allows for a more informed design of their treatment regimen. The diversity and specificity of these types of images make the availability of annotated databases sparse. Consequently, robust and efficient learningbased diagnostic systems are difficult to develop and apply in a clinical setting. Our study builds on the observation that the overall organization and structure of the observed tissues is similar across different staining protocols. In this paper, we propose to leverage both the wide availability of hematoxylin-eosin stained databases and the invariance of tissue organization and structure in order to perform unsupervised nuclei segmentation on immunohistochemistry images. We implement and evaluate a generative adversarial method that relies on high-level nuclei distribution priors through comparison with largely available hematoxylin-eosin stained cell nuclei masks. Our approach shows promising results compared to classic unsupervised and supervised methods, as we demonstrate on two publicly available datasets
Influence of tumor-associated macrophages and HLA class I expression according to HPV status in head and neck cancer patients receiving chemo/bioradiotherapy
International audienc