<div> Advancements in cancer treatments have increased rapidly in recent years, but cures remain elusive. Surgical tumor resection is a central treatment for many solid malignancies. Residual tumor at surgical margins leads to tumor recurrence. Novel tools for assessing residual tumor at surgical margins could improve surgical outcomes by helping to maximize the extent of resection. Ambient ionization-mass spectrometry (MS) methods generate and analyze ions from minimally prepared samples in near-real-time (e.g. seconds to minutes). These methods leverage the high sensitivity and specificity of mass spectrometry for analyzing gas phase ions and generating those ions quickly and with minimal sample preparation. Recent work has shown that differential profiles of ions, corresponding to phospholipids and small metabolites, are detected from cancerous and their respective normal tissue with ambient ionization-MS methods. When properly implemented, ambient ionization-MS could be used to assess for tumor at surgical margins and provide a molecular diagnosis during surgery. </div><div><br></div><div>The research herein reports efforts in developing rapid intraoperative ambient ionization-MS methods for the molecular assessment of cancerous tissues. Touch spray (TS) ionization and desorption electrospray ionization (DESI) were utilized to analyze kidney cancer and brain cancer.</div><div><br></div><div> As a demonstration of the applicability of TS-MS to provide diagnostic information from fresh surgical tissues, TS-MS was used to rapidly analyze renal cell carcinoma and healthy renal tissue biopsies obtained from human subjects undergoing nephrectomy surgery. Differential phospholipid profiles were identified using principal component analysis (PCA), and the significant ions were characterized using multiple stages of mass spectrometry and high resolution/exact mass MS. The same TS-MS analyzed renal tissues were subsequently analyzed with DESI-MS imaging to corroborate the TS-MS results, and the significant DESI-MS ions were also characterized with MS.</div><div><br></div><div>Significant efforts were made in developing and evaluating a standalone intraoperative DESI-MS system for analyzing brain tissue biopsies during brain tumor surgery. The intraoperative DESI-MS system consists of a linear trap quadrupole mass spectrometer placed on a custom-machined cart that contains all hardware for operating the mass spectrometer. This instrument was operated in the neurosurgical suites at Indiana University School of Medicine to rapidly analyze brain tissue biopsies obtained from glioma resection surgeries. A DESI-MS library of normal brain tissue and glioma was used to statistically classify the brain tissue biopsies collected in the operating room. Multivariate statistical methodologies were employed to predict the disease state and tumor cell percentage of the samples. A DESI-MS assay for detecting 2-hydroxyglutarate (2HG), the oncometabolic product of the isocitrate dehydrogenase (IDH) mutation (a key glioma prognostic marker), was developed and applied to determine the IDH mutation status during the surgical resection. The strengths, weaknesses, and areas of future work in this field are discussed. </div><div><br></div>

Tout ce qui vit, naît, se nourrit, se reproduit et meurt. Pour le cerveau, la question se complexifie car à la survie des neurones s'ajoute le coût de l'activité cérébrale. La question de la gestion énergétique pour les neurones est particulière car les cellules de notre cerveau évoluent de manière concertée et non par compétition. On sait avec l'imagerie médicale que l'usine neuronale ne fonctionne pas uniquement grâce au glucose ; elle utilise d'autres apports énergétiques tels que le lactate ou le glutamate pour soutenir sa production. Lorsqu'une tumeur apparaît, elle change le métabolisme énergétique pour survivre et soutenir sa propre croissance. En particulier, les cellules cancéreuses se fournissent en lactate et choisissent leur substrat préféré en fonction de l'oxygène disponible. La modélisation mathématique des substrats énergétiques est un outil de choix pour décrire et prédire de tels flux. Coupler ces modèles à des données issues de l'IRM et de la SRM permet d'améliorer la prise en charge du patient présentant un gliome.Cette thèse propose l'approche de plusieurs dynamiques en substrat dans le cerveau sain et gliomateux en se basant sur des systèmes d'équations : échanges locaux en lactate (EDO, système lent-rapide), échanges globaux en substrats (EDO), cycle glutamate/glutamine (EDR) et échanges en lactate en dimensions supérieures (EDP). Ces modèles sont expliqués, décrits grâce aux mathématiques et permettent l'élaboration de simulations ajustées selon des données patient ou issues de la littérature.L'énergie est nécessaire au maintien de la vie. Mais si votre voisin consomme une partie de vos ressources, pouvez-vous encore espérer survivre ? / Everything that lives is born, eats, reproduces and dies. For the brain, the question is more complex because neurons have to survive and to support brain activity. Energy management is also particular because brain cells evolve together with no competition. Thanks to medical imaging, we know that neurons do not consume only glucose. They can use others energetic substrates such as lactate and glutamate as a power source.When a tumor appears, it changes the energetic metabolism to survive and support its own growth. In particular, cancer cells like to consume lactate. They also choose their favorite substrate based on the available oxygen. Modeling of energy substrates is useful to describe and predict energetic kinetics and changes. Mathematical models could get with clinical and medical results to describe, explain or predict low grade glioma dynamics. They can help to characterize and quantify a tumor evolution, then leading to improve their therapeutical management. Exchanges between mathematics and MRI (and MRS) enable to get accurate data and to build suitable mathematical models.This thesis deals with several approaches of substrates dynamics in healthy and gliomatous brains. These researches are based on systems of equations. We model local lactate exchanges (ODE, fast-slow systems), global substrates exchanges (ODE), glutamate/glutamine cycle (RDE) and local lactate exchanges in higher dimensions (PDE). We describe, analyze and give simulations of these models. Simulations are fitted on patient MRI data or literature data. Energy is necessary to live. But if your neighbor consumes a part of your resources, can you still survive ? / Tutto ciò che vive nasce, si nutre, si riproduce e muore. Per il cervello, la questione è più complessa perché i neuroni devono sopravvivere e sostenere l'attività cerebrale. La gestione energetica cerebrale è particolare anche perché le cellule cerebrali evolvono insieme, senza concorrenza. Inoltre, grazie alle immagini mediche, sappiamo che i neuroni non consumano solo del glucosio ma usano altri substrati energetici come il lattato o il glutammato.Quando un tumore si stabilisce, cambia il metabolismo energetico del cervello per sopravvivere e sostenere la propria crescita. In particolare, cellule tumorali consumano del lattato e scelgono il loro substrato preferito basandosi all'ossigeno disponibile.La matematica, e in particolare l'elaborazione di modelli matematici può aiutarci a ottimizzare i dati disponibili, che possono essere, di volta in volta, delle proprietà cellulare o delle lastre MRI o MRS. La modellizzazione dei substrati energetici potrebbe descrivere, spiegare o prevedere le dinamiche energetiche nel cervello.Questa tesi tratta di diversi approcci della dinamica dei substrati nei cervelli sani e gliomatosi. Queste ricerche si basano su sistemi di equazioni. Modellizziamo scambi locali di lattato (ODE, sistemi fast-slow), scambi globali di substrati (ODE), ciclo glutammato/glutammina (RDE) e scambi locali di lattato in dimensioni superiori (PDE). Descriviamo, analizziamo e diamo simulazioni di questi modelli. Le simulazioni sono adeguate su dati MRI paziente o dati di letteratura.Per vivere, l’energia è una necessità. Ma se i Suoi vicini consumassero le Sue risorse, riuscirebbe ancora a sopravvivere ?

<p>My research has focused on the development and translation of ambient ionization mass spectrometry (MS)-based platforms in clinical and surgical settings, specifically in the area of brain cancer diagnostics and surgical decision making. Ambient ionization MS methods, such as those described herein, generate and analyze gas phase ions with high sensitivity and specificity from minimally prepared samples in near-real-time, on the order of seconds to minutes, rendering them well suited to point-of-care applications. We used ambient ionization MS methods, specifically desorption electrospray ionization mass spectrometry (DESI-MS) and extraction nanoelectrospray ionization mass spectrometry (nESI-MS) to molecularly characterize brain cancer biopsies. The characterization was made using diagnostic compounds identified as markers of disease state, tissue composition, tumor type, and genotype in human brain tissue. Methods were developed and validated offline in the laboratory and translated to clinical and surgical settings, thereby generating chemical information on prognostic features intraoperatively and providing valuable information that would be otherwise unavailable. We believe that, with approval, the methodologies described can assist physicians and improve patient outcomes by providing analytical tools and molecular information that can inform surgical decision making and adjuvant treatment strategies, complementing and not interfering with standard of care protocols.</p> <p><br></p> <p>We have successfully demonstrated the use of desorption electrospray ionization mass spectrometry (DESI-MS) for the expedient molecular assessment of human glioma tissue biopsies based on lipid profiles and prognostic metabolites, both at the tumor core and near surgical margins, in two small-scale, clinical studies. Maximal surgical resection of gliomas that avoids non-infiltrated tissue is associated with survival benefit in patients with glioma. The infiltrative nature of gliomas, as well as their morphological and genetic diversity, renders treatment difficult and demands an integrated imaging and diagnostic approach during surgery to guide clinicians in achieving maximal tumor resection. Further, the estimation of tumor cell percentage (TCP), a measure of tumor infiltration at surgical margins, is not routinely assessed intraoperatively. </p> <p>We have previously shown that rapid, offline molecular assessment of tumor infiltration in tissue biopsies is possible and believe that the same assessment performed intraoperatively in biopsied tissue near surgical margins could improve resection and better inform patient management strategies, including postoperative radiotherapy. Using a DESI-MS spectral library of normal brain tissue and glioma biopsies to generate a statistical model to classify brain tissue biopsies intraoperatively, multivariate statistical approaches were used to predict the disease state and tumor cell percentage (TCP) of each biopsy, thereby providing an measure of tumor infiltration at surgical margins via molecular indicators. In addition to assessment of tumor infiltration, we have developed DESI-MS assays for detecting the oncometabolite 2-Hydroxyglutarate (2HG) to detect isocitrate dehydrogenase (IDH) mutations in gliomas intraoperatively. Knowledge of IDH genotypes at the time of surgical resection could improve patient outcomes, as more aggressive tumor resection of IDH-mutated gliomas is associated with increased survival. While assessments of IDH genotype are typically not available until days after surgery, we have demonstrated the ability to provide this information is less than five minutes. An intraoperative DESI-MS system has successfully been used in a proof-of-concept clinical study and intraoperative performance validation of this platform is ongoing. The findings of these two studies as well as strengths, weaknesses, and areas of improvement for upcoming future iterations of the research are discussed.</p> <p><br></p> <p>Point-of-care applications necessitate the adaptation of MS methodologies to smaller devices. Miniature mass spectrometers (Mini MS) boast small footprints, simple operation, and low power consumption, noise levels, and cost, making them attractive candidates for point-of-care use. In a small-scale clinical study, we demonstrated the first application of a Mini MS for determination of IDH mutation status in gliomas intraoperatively. This study paves a path forward for the application of Mini MS in the OR. With its small footprint and low power consumption and noise level, this application of miniature mass spectrometers represents a simple and cost-effective platform for an important intraoperative measurement. </p> <p><br></p> <p>While MS-based methods of tissue analysis can detect molecular features of interest and rapidly produce large quantities of data, their inherent speed is rarely utilized because they are traditionally coupled with time-consuming separation techniques (e.g., chromatography). Ambient ionization MS, specifically DESI-MS, is well suited for high-throughput applications due to its lack of sample preparation and purification techniques. In an attempt to rapidly characterize microarrays of tissue biopsies, we developed a high-throughput DESI-MS (HT-DESI-MS) method for the rapid characterization of disease state, human brain tumor type, glioma classification, and detection of IDH mutations in tissue microarrays (TMA) of banked and fresh human brain tissue biopsies. We anticipate that HT-DESI-MS analysis of TMAs could become a standard tool for the generation of spectral libraries for sample classification, the identification of biomarkers through large-scale studies, the correlation of molecular features with anatomical features when coupled to digital pathology, and the assessment of drug efficacy. </p>

Cette thèse a été conçu pour explorer les modèles dynamiques de régression, d’évaluer les inférences statistiques pour l’analyse des données de survie et de fiabilité. Ces modèles de régression dynamiques que nous avons considérés, y compris le modèle des hasards proportionnels paramétriques et celui de la vie accélérée avec les variables qui peut-être dépendent du temps. Nous avons discuté des problèmes suivants dans cette thèse.Nous avons présenté tout d’abord une statistique de test du chi-deux généraliséeY2nquiest adaptative pour les données de survie et fiabilité en présence de trois cas, complètes,censurées à droite et censurées à droite avec les covariables. Nous avons présenté en détailla forme pratique deY2nstatistique en analyse des données de survie. Ensuite, nous avons considéré deux modèles paramétriques très flexibles, d’évaluer les significations statistiques pour ces modèles proposées en utilisantY2nstatistique. Ces modèles incluent du modèle de vie accélérés (AFT) et celui de hasards proportionnels (PH) basés sur la distribution de Hypertabastic. Ces deux modèles sont proposés pour étudier la distribution de l’analyse de la duré de survie en comparaison avec d’autre modèles paramétriques. Nous avons validé ces modèles paramétriques en utilisantY2n. Les études de simulation ont été conçus.Dans le dernier chapitre, nous avons proposé les applications de ces modèles paramétriques à trois données de bio-médicale. Le premier a été fait les données étendues des temps de rémission des patients de leucémie aiguë qui ont été proposées par Freireich et al. sur la comparaison de deux groupes de traitement avec des informations supplémentaires sur les log du blanc du nombre de globules. Elle a montré que le modèle Hypertabastic AFT est un modèle précis pour ces données. Le second a été fait sur l’étude de tumeur cérébrale avec les patients de gliome malin, ont été proposées par Sauerbrei & Schumacher. Elle a montré que le meilleur modèle est Hypertabastic PH à l’ajout de cinq variables de signification. La troisième demande a été faite sur les données de Semenova & Bitukov, à concernant les patients de myélome multiple. Nous n’avons pas proposé un modèle exactement pour ces données. En raison de cela était les intersections de temps de survie.Par conséquent, nous vous conseillons d’utiliser un autre modèle dynamique que le modèle de la Simple Cross-Effect à installer ces données. / This thesis was designed to explore the dynamic regression models, assessing the sta-tistical inference for the survival and reliability data analysis. These dynamic regressionmodels that we have been considered including the parametric proportional hazards andaccelerated failure time models contain the possibly time-dependent covariates. We dis-cussed the following problems in this thesis.At first, we presented a generalized chi-squared test statisticsY2nthat is a convenient tofit the survival and reliability data analysis in presence of three cases: complete, censoredand censored with covariates. We described in detail the theory and the mechanism to usedofY2ntest statistic in the survival and reliability data analysis. Next, we considered theflexible parametric models, evaluating the statistical significance of them by usingY2nandlog-likelihood test statistics. These parametric models include the accelerated failure time(AFT) and a proportional hazards (PH) models based on the Hypertabastic distribution.These two models are proposed to investigate the distribution of the survival and reliabilitydata in comparison with some other parametric models. The simulation studies were de-signed, to demonstrate the asymptotically normally distributed of the maximum likelihood estimators of Hypertabastic’s parameter, to validate of the asymptotically property of Y2n test statistic for Hypertabastic distribution when the right censoring probability equal 0% and 20%.n the last chapter, we applied those two parametric models above to three scenes ofthe real-life data. The first one was done the data set given by Freireich et al. on thecomparison of two treatment groups with additional information about log white blood cellcount, to test the ability of a therapy to prolong the remission times of the acute leukemiapatients. It showed that Hypertabastic AFT model is an accurate model for this dataset.The second one was done on the brain tumour study with malignant glioma patients, givenby Sauerbrei & Schumacher. It showed that the best model is Hypertabastic PH onadding five significance covariates. The third application was done on the data set given by Semenova & Bitukov on the survival times of the multiple myeloma patients. We did not propose an exactly model for this dataset. Because of that was an existing oneintersection of survival times. We, therefore, suggest fitting other dynamic model as SimpleCross-Effect model for this dataset.

Gliomas are the most common primary brain tumours and are associated with poor prognosis. Among them, diffuse gliomas – which include their most aggressive form glioblastoma (GBM) – are known to be highly infiltrative. The diagnosis and follow-up of gliomas rely on positron emission tomography (PET) and magnetic resonance imaging (MRI). However, these imaging techniques do not currently allow to assess the whole extent of such infiltrative tumours nor to anticipate their preferred invasion patterns, leading to sub-optimal treatment planning. Mathematical tumour growth modelling has been proposed to address this problem. Reaction-diffusion tumour growth models, which are probably the most commonly used for diffuse gliomas growth modelling, propose to capture the proliferation and migration of glioma cells by means of a partial differential equation. Although the potential of such models has been shown in many works for patient follow-up and therapy planning, only few limited clinical applications have seemed to emerge from these works. This thesis aims at revisiting reaction-diffusion tumour growth models using state-of-the-art medical imaging and data processing technologies, with the objective of integrating multi-parametric PET/MRI data to further personalise the model. Brain tissue segmentation on MR images is first addressed with the aim of defining a patient-specific domain to solve the model. A previously proposed method to derive a tumour cell diffusion tensor from the water diffusion tensor assessed by diffusion-tensor imaging (DTI) is then implemented to guide the anisotropic migration of tumour cells along white matter tracts. The use of dynamic [S-methyl-11C]methionine ([11C]MET) PET is also investigated to derive patient-specific proliferation potential maps for the model. These investigations lead to the development of a microscopic compartmental model for amino acid PET tracer transport in gliomas. Based on the compartmental model results, a novel methodology is proposed to extract parametric maps from dynamic [11C]MET PET data using principal component analysis (PCA). The problem of estimating the initial conditions of the model from MR images is then addressed by means of a translational MRI/histology study in a case of non-operated GBM. Numerical solving strategies based on the widely used finite difference and finite element methods are finally implemented and compared. All these developments are embedded within a common framework allowing to study glioma growth in silico and providing a solid basis for further research in this field. However, commonly accepted hypothesis relating the outlines of abnormalities visible on MRI to tumour cell density iso-contours have been invalidated by the translational study carried out, leaving opened the questions of the initialisation and the validation of the model. Furthermore, the analysis of the temporal evolution of real multi-treated glioma patients demonstrates the limitations of the formulated model. These latter statements highlight current obstacles to the clinical application of reaction-diffusion tumour growth models and pave the way to further improvements. / Les gliomes sont les tumeurs cérébrales primitives les plus communes et sont associés à un mauvais pronostic. Parmi ces derniers, les gliomes diffus – qui incluent la forme la plus agressive, le glioblastome (GBM) – sont connus pour être hautement infiltrants. Le diagnostic et le suivi des gliomes s'appuient sur la tomographie par émission de positons (TEP) ainsi que l'imagerie par résonance magnétique (IRM). Cependant, ces techniques d'imagerie ne permettent actuellement pas d'évaluer l'étendue totale de tumeurs aussi infiltrantes ni d'anticiper leurs schémas d'invasion préférentiels, conduisant à une planification sous-optimale du traitement. La modélisation mathématique de la croissance tumorale a été proposée pour répondre à ce problème. Les modèles de croissance tumorale de type réaction-diffusion, qui sont probablement les plus communément utilisés pour la modélisation de la croissance des gliomes diffus, proposent de capturer la prolifération et la migration des cellules tumorales au moyen d'une équation aux dérivées partielles. Bien que le potentiel de tels modèles ait été démontré dans de nombreux travaux pour le suivi des patients et la planification de thérapies, seules quelques applications cliniques restreintes semblent avoir émergé de ces derniers. Ce travail de thèse a pour but de revisiter les modèles de croissance tumorale de type réaction-diffusion en utilisant des technologies de pointe en imagerie médicale et traitement de données, avec pour objectif d'y intégrer des données TEP/IRM multi-paramétriques pour personnaliser davantage le modèle. Le problème de la segmentation des tissus cérébraux dans les images IRM est d'abord adressé, avec pour but de définir un domaine propre au patient pour la résolution du modèle. Une méthode proposée précédemment permettant de dériver un tenseur de diffusion tumoral à partir du tenseur de diffusion de l'eau évalué par imagerie DTI a ensuite été implémentée afin de guider la migration anisotrope des cellules tumorales le long des fibres de matière blanche. L'utilisation de l'imagerie TEP dynamique à la [S-méthyl-11C]méthionine ([11C]MET) est également investiguée pour la génération de cartes de potentiel prolifératif propre au patient afin de nourrir le modèle. Ces investigations ont mené au développement d'un modèle compartimental pour le transport des traceurs TEP dérivés des acides aminés dans les gliomes. Sur base des résultats du modèle compartimental, une nouvelle méthodologie est proposée utilisant l'analyse en composantes principales pour extraire des cartes paramétriques à partir de données TEP dynamiques à la [11C]MET. Le problème de l'estimation des conditions initiales du modèle à partir d'images IRM est ensuite adressé par le biais d'une étude translationelle combinant IRM et histologie menée sur un cas de GBM non-opéré. Différentes stratégies de résolution numérique basées sur les méthodes des différences et éléments finis sont finalement implémentées et comparées. Tous ces développements sont embarqués dans un framework commun permettant d'étudier in silico la croissance des gliomes et fournissant une base solide pour de futures recherches dans le domaine. Cependant, certaines hypothèses communément admises reliant les délimitations des anormalités visibles en IRM à des iso-contours de densité de cellules tumorales ont été invalidée par l'étude translationelle menée, laissant ouverte les questions de l'initialisation et de la validation du modèle. Par ailleurs, l'analyse de l'évolution temporelle de cas réels de gliomes multi-traités démontre les limitations du modèle. Ces dernières affirmations mettent en évidence les obstacles actuels à l'application clinique de tels modèles et ouvrent la voie à de nouvelles possibilités d'amélioration. / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished