Glioblastoma is the most common primary brain tumor in adults, and it is associated with a dismal prognosis with a median survival of 15 months. Despite treatment with chemotherapy, radiation therapy and surgery, patients inevitably have disease recurrence. Bevacizumab is a monoclonal humanized antibody that inhibits vascular endothelial growth factor signaling, and it has been shown to be effective in recurrent glioblastoma with respect to prolonging progression-free survival (PFS). The use of bevacizumab and other anti-angiogenic agents in recurrent glioblastoma have created novel challenges in interpreting magnetic resonance imaging (MRI) of patients. Furthermore, since only some patients appear to have a durable benefit from bevacizumab, there is a need for imaging biomarkers that can reliably identify this subgroup of patients. Partly due to the challenges created by anti-angiogenic agents, the Response Assessment in Neuro-Oncology (RANO) was proposed to address some of the limitations with traditional response assessment criteria. In the first part of this project, we attempted to validate the RANO criteria by performing a comparative analysis of the RANO criteria vs. the Macdonald criteria using imaging from the phase II BRAIN trial. As we hypothesized, the RANO criteria yielded a significantly decreased PFS by identifying a subset of patients who had progression of nonenhancing tumor evident on T2-weighted imaging. Additionally, response and progression as defined by the RANO criteria correlated with subsequent overall survival (OS) in landmark analyses. While this supports the implementation of RANO criteria for response assessment in glioma clinical trials, future research will be necessary to further improve response assessment by incorporating advanced techniques such as volumetric anatomic assessment, perfusion-weighted MR (PWI-MR), diffusion-weighted MR (DWI-MR), MR spectroscopy (MRS) and positron emission tomography (PET). Advanced imaging techniques are becoming increasingly recognized for their ability to provide objective, non-invasive assessment of treatment response but also to serve as predictive and prognostic biomarkers allowing for stratification of patient subgroups with better treatment outcome. In the second part of the project, we attempted to perform volumetric analysis of tumor size based on conventional MRI, as well as a histogram analysis of apparent diffusion coefficients (ADC) derived from diffusion-weighted MRI, to evaluate imaging parameters as predictors for PFS and OS in a single institution database of recurrent glioblastoma patients initiated on bevacizumab. Volumetric percentage change and absolute early post-treatment volume (3-6 weeks after initiation) of enhancing tumor can stratify survival for patients with recurrent glioblastoma receiving bevacizumab therapy. ADC histogram analysis using a multi-component curve-fitting technique within both enhancing and nonenhancing components of tumor prior to the initiation of bevacizumab can also be used to stratify OS in recurrent glioblastoma patients. While prospective studies are necessary to validate findings, future studies will increasingly incorporate multiparametric approaches to elucidate biomarkers that combine the value of conventional MRI with advanced techniques such as DWI-MR, PWI-MR, MRS and PET to obtain better predictors for PFS and OS in recurrent glioblastoma.

The aim of this thesis was to develop and validate prognostic and predictive biomarkers in order to better identify among patients with metastatic colorectal cancer those at high-risk of early death or progression. The interest in developing such biomarkers is that their subsequent use in clinical practice would avoid exposing a patient for months to the toxic side effects of ineffective and expensive treatments, and thus to limit the financial impact of these treatments on our healthcare systems.The projects carried out in the framework of this thesis have shown that:The biomarker WB-MATV (metabolically active tumor volume of the whole body) measured before the start of the last line treatment has a high prognostic value, higher than the general clinical parameters commonly used. This biomarker was then validated in first line treatment and was shown to have a high prognostic value, also higher than the general clinical parameters.The biomarker cfDNA (circulating DNA) also representing the tumor load was then investigated to assess its value added to the previously validated WB-MATV. We showed that the presence of high levels of cfDNA before starting the last-line treatment is significantly associated with poor prognosis and that these two biomarkers are prognostically complementary, each providing an added value.The biomarker of early metabolic response to last line treatment has a high negative predictive value (95%). This biomarker was then validated as a predictive biomarker independent of WB-MATV and clinical factors in first-line treatment setting.In conclusion, the results of this thesis strongly support the clinical use of these prognostic and predictive biomarkers in patients with metastatic colorectal cancer. Allowing a more accurate stratification of patients, the use of the combination of these biomarkers should become an essential tool to help oncologists in tailoring therapeutic strategies according to the patients’ individual risk. / Doctorat en Sciences médicales (Médecine) / info:eu-repo/semantics/nonPublished

No description available.

Gray matter (GM) forms the ‘computational engine’ of our brain and plays the key role in brain function. Measures derived from MRI (e.g., structural MRI (sMRI) and diffusion MRI (dMRI)) provide a unique opportunity to non-invasively study GM structure in-vivo and thus can be used to probe GM pathology in development, aging and neuropsychiatric disorders. Investigation of the influence of various factors on MRI measures in GM is critical to facilitate their use for future non-invasive studies in healthy and diseased populations. In this dissertation, GM structure was studied with MRI to understand how it is influenced by genetic and environmental factors. Validation of dMRI- derived measures was conducted by comparing them with histological data from monkeys to better understand the cytoarchitectural features that influence GM measures. First, the influence of genetic and environmental factors was quantified on gray matter macrostructure and microstructure measures using phenotypic modelling of structural and diffusion MRI data obtained from a large twin and sibling population (N = 840). Results of this study showed that in GM, while sMRI measures like cortical thickness and GM volume are mainly affected by genetic factors, advanced dMRI measures of mean squared displacement (MSD) and return to origin probability (RTOP) derived from advanced biexponential model can tap into regionally specific patterns of both genetic and environmental influence in cortical and subcortical GM. Our results thus highlight the potential of these advanced dMRI measures for use in future studies that aim to investigate and follow in healthy and clinical population changes in GM microstructure linked with both genes and environment. Second, using data from a large healthy population (n=550), we investigated changes in sMRI tissue contrast at the gray-white matter boundary with biological development during adolescence to assess how this affects estimation of the developmental trajectory of cortical thickness. Results of this study suggest that increased myelination during brain development contributes to age-related changes in gray-white boundary contrast in sMRI scans causing an apparent shift of the estimated gray-white boundary towards the cortical surface, in turn reducing estimations of cortical thickness and its developmental trajectory. Based on these results, we emphasize the importance of accounting for the effects of myelination on T1 gray-white matter boundary contrast to enable more precise estimation of cortical thickness during neurodevelopment. Finally, we conducted histological validation of dMRI measures in gray matter by comparing dMRI measures derived from two models, conventional Diffusion Tensor Imaging (DTI) model and an advanced biexponential model with histology acquired from the same 4 rhesus monkeys. Results demonstrate differences in the ability of distinct dMRI measures including DTI-derived measures of fractional anisotropy (FA), Trace and advanced Biexponential model-derived measures of MSD and RTOP to capture the biological features of underlying cytoarchitecture and identify the dMRI measures that best reflect underlying gray matter cytoarchitectural properties. Investigation of the contribution of underlying cytoarchitecture (cellular organization) to dMRI measures in gray matter provides validation of dMRI measures of average and regional heterogeneity in MSD & Trace as markers of cytoarchitecture as measured by regional average and heterogeneity in cell area density. This postmortem validation of these dMRI measures makes their use possible for treatment monitoring of various GM pathologies. These studies and their results together demonstrate the utility of imaging measures to investigate the complex relationships between GM cellular organization, brain development, environment and genes.

No description available.

La présente thèse propose des méthodes pour l'estimation du mouvement des organes d'un patient autravers de l'imagerie tomographique. Le but est la correction du mouvement spatio-temporel sur les imagesmédicales tomographiques. En tant que paradigme expérimental, nous considérons le problème de l'estimation dumouvement dans l'imagerie IRM de diffusion, une modalité d'imagerie sensible à la diffusion des molécules d'eaudans le corps. Le but de ces travaux de thèse est l'évaluation des patients atteints de lymphome, car l'eau diffusedifféremment dans les tissus biologiques sains et dans les lésions. L'effet de la diffusion de l'eau peut être mieuxreprésenté par une image paramétrique, grâce au coefficient de diffusion apparente (image à ADC), créé sur la based'une série d'images DWI du même patient (séquence d'images 3D), acquises au moment de la numérisation. Unetelle image paramétrique a la possibilité de devenir un biomarqueur d'imagerie d’IRM et de fournir aux médecinsdes informations complémentaires concernantl'image de FDG-PET qui est la méthode d'imagerie de base pour lelymphome et qui montre la quantité de glucose métabolisée.Nos principales contributions sont au nombre de trois. Tout d'abord, nous proposons une méthode de recalaged'image déformable en groupe spécialement conçue pour la correction de mouvement dans l’IRM de diffusion, carelle est guidée par un modèle physiologique décrivant le processus de diffusion qui se déroule lors de l'acquisitionde l'image. Notre méthode détermine une image à ADC de plus grande précision en termes de représentation dugradient de la diffusion des molécules d'eau par rapport à l` image correspondante obtenue par pratique couranteou par d'autres méthodes de recalage d'image non basé sur un modèle. Deuxièmement, nous montrons qu'enimposant des contraintes spatiales sur le calcul de l'image à ADC, les tumeurs de l'image peuvent être encore mieuxcaractérisées en les classant dans les différentes catégories liées à la maladie. Troisièmement, nous montronsqu'une corrélation entre DWI et FDG-PET doit exister en examinant la corrélation entre les caractéristiquesstatistiques extraites par l'image à ADC lisse découlant de notre méthode du recalage d’image déformable et lesscores de recommandation sur la malignité des lésions, donnés par des experts basés sur une évaluation des imagesFDG-PET correspondantes du patient. / This doctoral thesis develops methods to estimate patient's motion, voluntary and involuntary (organs'motion), in order to correct for motion in spatiotemporal tomographic medical images. As an experimentalparadigm we consider the problem of motion estimation in Diffusion-Weighted Magnetic Resonance Imaging (DWI),an imaging modality sensitive to the diffusion of water molecules in the body. DWI is used for the evaluation oflymphoma patients, since water diffuses differently in healthy tissues and in lesions. The effect of water diffusioncan be better depicted through a parametric map, the so-called apparent diffusion coefficient (ADC map), createdbased on a series of DW images of the same patient (3D image sequence), acquired in time during scanning. Such aparametric map has the potentiality to become an imaging biomarker in DWI and provide physicians withcomplementary information to current state-of-the-art FDG-PET imaging reflecting quantitatively glycosemetaboslism.Our contributions are three fold. First, we propose a group-wise deformable image registration methodespecially designed for motion correction in DWI, as it is guided by a physiological model describing the diffusionprocess taking place during image acquisition. Our method derives an ADC map of higher accuracy in terms ofdepicting the gradient of the water molecules' diffusion in comparison to the corresponding map derived bycommon practice or by other model-free group-wise image registration methods. Second, we show that by imposingspatial constraints on the computation of the ADC map, the tumours in the image can be even better characterized interms of classifying them into the different types of the disease. Third, we show that a correlation between DWI andFDG-PET should exist by examining the correlation between statistical features extracted by the smooth ADC mapderived by our deformable registration method, and recommendation scores on the malignancy of the lesions, givenby experts based on an evaluation of the corresponding FDG-PET images of the patient.

No description available.

[ES] La explotación de datos de salud ha demostrado ser de creciente interés en la comunidad científica, especialmente para la creación y uso de sistemas de ayuda a la decisión clínica (SADC). Para abordar este problema, tradicionalmente se ha investigado por separado en materia de modelos de información, modelos de dominio y SADC. En lo que se refiere a modelos de información, las propuestas presentan limitaciones semánticas y no tienen en cuenta la interacción con los modelos de dominio, que pretenden proporcionar una comprensión formal y compartida del conocimiento clínico, ni con los SADC, cuya finalidad es proporcionar apoyo a la toma de decisión clínica a partir de la historia clínica electrónica (HCE). La finalidad de esta tesis se enmarca dentro del objetivo general de enriquecer sistemas de HCE con resultados de SADC y modelos de dominio representados mediante datos enlazados abiertos. Para ello, se investiga la combinación y explotación conjunta de las tecnologías más avanzadas para modelos de información, modelos de dominio y SADC. La principal contribución de esta tesis es el desarrollo de metodologías y herramientas para enriquecer la HCE con resultados de SADC y datos enlazados abiertos. Las contribuciones específicas son las siguientes: * Definición conceptual y metodológica de la HCE aumentada con información potencialmente relevante de la web semántica. * Definición conceptual y metodológica del informe radiológico estructurado (IRE), enriquecido con resultados de SADC basados en reglas, visión por computación y modelos de aprendizaje automático. * Caso de uso de HCE aumentada, consistente en enriquecer la HCE resumida del Sistema Nacional de Salud de España con datos enlazados abiertos sobre interacciones farmacológicas y tratamientos recomendados para los episodios activos del paciente. * Sistema de IRE enriquecido con resultados de SADC. Incluye el desarrollo de plantillas de IRE y mecanismos para el enriquecimiento de las mismas con resultados de SADC basados en reglas, cuantificación de imagen médica y redes neuronales. Nuestro objetivo es mejorar el grado de interoperabilidad en las integraciones de sistemas de HCE con SADC y datos enlazados abiertos, mediante estrategias basadas en los tres pilares de la interoperabilidad semántica: modelos de información, de arquetipos y de dominio. Esto tiene el potencial de repercutir positivamente sobre la salud y el cuidado del paciente, especialmente en el paradigma de la medicina personalizada. / [CA] L'explotació de dades de salut ha demostrat ser de creixent interés en la comunitat científica, especialment per a la creació i ús de sistemes d'ajuda a la decisió clínica (SADC). Per a abordar aquest problema, tradicionalment s'ha investigat per separat en matèria de models d'informació, models de domini i SADC. Pel que fa a models d'informació, les propostes presenten limitacions semàntiques i no tenen en compte la interacció amb els models de domini, que pretenen proporcionar una comprensió formal i compartida del coneixement clínic, ni amb els SADC, la finalitat dels quals és proporcionar suport a la presa de decisió clínica partint de la història clínica electrònica (HCE). La finalitat d'aquesta tesi s'emmarca dins de l'objectiu general d'enriquir sistemes de HCE amb resultats de SADC i models de domini representats mitjançant dades enllaçades obertes. Per a això, s'investiga la combinació i explotació conjunta de les tecnologies més avançades per a models d'informació, models de domini i SADC. La principal contribució d'aquesta tesi és el desenvolupament de metodologies i eines per a enriquir la HCE amb resultats de SADC i dades enllaçades obertes. Les contribucions específiques són les següents: * Definició conceptual i metodològica de la HCE augmentada amb informació potencialment rellevant de la web semàntica. * Definició conceptual i metodològica de l'informe radiològic estructurat (IRE), enriquit amb resultats de SADC basats en regles, visió per computació i models d'aprenentatge automàtic. * Cas d'ús de HCE augmentada, consistent a enriquir la HCE resumida del Sistema Nacional de Salut d'Espanya amb dades enllaçades obertes sobre interaccions farmacològiques i tractaments recomanats per als episodis actius del pacient. * Sistema de IRE enriquit amb resultats de SADC. Inclou el desenvolupament de plantilles de IRE i mecanismes per a l'enriquiment de les mateixes amb resultats de SADC basats en regles, quantificació d'imatge mèdica i xarxes neuronals. El nostre objectiu és millorar el grau d'interoperabilitat en les integracions de sistemes de HCE amb SADC i dades enllaçades obertes, mitjançant estratègies basades en els tres pilars de la interoperabilitat semàntica: models d'informació, d'arquetips i de domini. Això té el potencial de repercutir positivament sobre la salut i la cura del pacient, especialment en el paradigma de la medicina personalitzada. / [EN] The exploitation of health data has proven to be of increasing interest in the scientific community, especially for the creation and use of clinical decision support systems (CDSS). To address this problem, separate research has traditionally been done on information models, domain models and CDSS. Regarding information models, the proposals present semantic limitations and do not consider the interaction with domain models, which aim to provide a formal and shared understanding of clinical knowledge, nor with CDSS, whose purpose is to provide clinical decision support from the electronic health record (EHR). The aim of this thesis is framed within the general goal of enriching EHR systems with SADC results and domain models represented by open linked data. For this purpose, the combination and joint exploitation of state-of-the-art technologies for information models, domain models and SADC is investigated. The main contribution of this thesis is the development of methodologies and tools to enrich EHR with SADC results and open linked data. Specific contributions are: * Conceptual and methodological definition of EHR augmented with potentially relevant information from the semantic web. * Conceptual and methodological definition of the structured radiology report (SRR), enriched with CDSS results based on logical rules, computer vision and machine learning models. * Augmented EHR use case, consisting of enriching the summarized EHR of the Spanish National Health System with linked open data on pharmacological interactions and recommended treatments for active patient episodes. * SRR system enriched with CDSS results. Includes the development of SRR templates and mechanisms for enriching them with SADC results based on logical rules, medical image quantification and neural networks. Our goal is to improve the degree of interoperability in EHR system integrations with CDSS results and linked open data, through strategies based on the three pillars of semantic interoperability: information, archetype and domain models. This has the potential to positively impact health and patient care, especially in the personalized medicine paradigm. / Mañas García, A. (2022). Enriquecimiento de la historia clínica electrónica con información de sistemas de ayuda a la decisión clínica y datos enlazados abiertos [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/187730