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Numerous studies have suggested rapid weight gain following diagnosis and initial treatment of childhood craniopharyngioma (CP) due to the damage sustained by the hypothalamus. Hypothalamic lesions formed by the treatment of the tumor and/or by invasiveness of the tumor itself are known to cause intractable weight gain, known as hypothalamic obesity. In contrast, hypothalamic obesity manifested in pediatric low-grade glioma (PLGG) patients is not as prominently addressed in literature; likely due to the expansive set of histological tumor subtypes that makes generalization challenging. Specifically, there is a lack of analysis that examines the difference in treatment, endocrinopathies, and weight gain between CP and PLGG patients. The purpose of this study was to compare hypothalamic damage in subjects diagnosed with pediatric hypothalamic low-grade glioma versus subjects diagnosed with childhood craniopharyngioma. We hypothesized that CP patients will have a more rapid post diagnosis weight gain and a greater degree of obesity compared with PLGG patients due to the more invasive nature of the tumor and the aggressive surgical treatments involved. We performed a retrospective review of the clinical records of patients who received a diagnosis of childhood craniopharyngioma or pediatric low-grade glioma at Dana-Farber Cancer Institute between 1980 and 2009. We identified 45 patients, who met criteria for evaluation, 28 were previously diagnosed with childhood craniopharyngioma and 17 were diagnosed with hypothalamic pediatric low-grade glioma. We analyzed the impact of treatment, the presence of endocrinopathies, and weight gain after diagnosis. We concluded that there was no statistically significant difference in the rate or magnitude of post diagnosis weight gain, disproving our initial hypotheses.

No description available.

No description available.

Brain tumors present the second highest cause of death among pediatric cancers. About 60% are located in the posterior fossa region of the brain; among the most frequent types the ones considered for this project were astrocytomas, medulloblastomas, and ependymomas. Diagnosis can be done either through invasive histopathology exams or by non-invasive magnetic resonance (MR) scans. The tumors listed can be difficult to diagnose, even for trained radiologists, so machine learning methods, in particular deep learning, can be useful in helping to assess a diagnosis. Deep learning has been investigated only in a few other studies.The dataset used included 115 different subjects, some with multiple scan sessions, for which there were 142 T2-w, 119 T1Gd-w, and 89 volumes that presented both MR modalities. 2D slices have been manually extracted from the registered and skull-stripped volumes in the transversal, sagittal, and frontal anatomical plane and have been preprocessed by normalizing them and selecting the slices containing the tumor. The scans employed are T2-w, T1Gd-w, and a combination of the two referred to as multimodal images. The images were divided session-wise into training, validation, and testing, using stratified cross-validation and have also been augmented. The convolutional neural networks (CNN) investigated were ResNet50, VGG16, and MobileNetV2. The model performances were evaluated for two-class and three-class classification tasks by computing the confusion matrix, accuracy, receiver operating characteristic curve (ROC), the area under the curve (AUROC), and F1-score. Moreover,  explanations for the behavior of networks were investigated using GradCAMs and occlusion maps. Preliminary investigations showed that the best plane and modality were the transversal one and T2-w images. Overall the best model was VGG16, for the two-class tasks the best classification was between astrocytomas and medulloblastomas which reached an F1-score of 0.86 for both classes on multimodal images, followed by astrocytomas and ependymomas with an F1-score of 0.76 for astrocytomas and 0.74 for ependymomas on T2-w, and last F1-score of 0.30 for ependymomas and 0.65 for medulloblastomas on multimodal images. The three-class classification reached F1-score values of 0.59 for astrocytomas, 0.46 for ependymomas, and 0.64 for medulloblastomas on T2-w images. GradCAMs and occlusion maps showed that VGG16 was able to focus mostly on the tumor region but that there also seemed to be other information in the background of the images that contributed to the final classification.To conclude, the classification of infratentorial pediatric brain tumors can be achieved with acceptable results by means of deep learning and using a single MR modality, though one might have to account for the dataset size, number of classes and class imbalance. GradCAMs and occlusion maps offer important insights into the decision process of the networks

Les tumeurs gliales et glioneuronales sont les tumeurs cérébrales les plus fréquentes chez l'enfant. Elles sont généralement d'excellent pronostic. En revanche, les astrocytomes pilocytiques (AP) hypothalamo-chiasmatiques, ont un potentiel évolutif plus agressif. Ce travail de thèse propose une nouvelle stratégie thérapeutique pour ce sous-type d'AP selon la méthode du « drug repositioning », en employant la combinaison du celecoxib et de la fluvastatine. Nos travaux ont montré in vitro que cette association de molécules était synergique, capable d'arrêter le cycle cellulaire, de diminuer la prolifération et d'induire l'apoptose des cellules tumorales. Cette combinaison a également été testée avec succès chez une patiente souffrant d'un AP multifocal et réfractaire aux traitements conventionnels dans le cadre d'une thérapie métronomique. Ce manuscrit décrit également l'étude histo-moléculaire de plusieurs séries de tumeurs gliales et glioneuronales pédiatriques menées afin d'améliorer leur caractérisation et leur diagnostic. Nos travaux ont confirmé la présence de la fusion KIAA1549:BRAF dans les AP analysés ainsi que le caractère péjoratif de la topographie hypothalamo-chiasmatique, du variant histologique pilomyxoïde et de l'âge au diagnostic inférieur à 36 mois. Ils ont également montré l'absence de différence moléculaire entre les gliomes corticaux de grade II et des DNT. Enfin, nos travaux ont montré que les DNT, les GG et les PXA partagent la mutation BRAFV600E et l'expression de CD34. Ces travaux confirment l'implication majeure de l'altération de la voie des MAPKinases dans la tumorigenèse de ces tumeurs, constituant ainsi une cible thérapeutique prometteuse. / Glial and glioneuronal tumors are the most frequent brain tumors in children. They are characterized by an excellent prognosis. However, hypothalamic-chiasmatic pilocytic astrocytomas (PA) have a more aggressive outcome. In the first part, we propose a new therapeutic strategy for hypothalamic-chiasmatic PA according to drug repositioning method, by using celecoxib, and fluvastatin. We showed that, in vitro, this combination was synergistic, stopped cell cycle, inhibited cell proliferation and increased apoptosis. In addition, this combination was tested with success, under a metronomic chemotherapy, for a girl suffering from a multifocal PA and refractory to conventional treatment. This new strategy of treatment appears promising for this type of tumor because it is less toxic than conventional chemotherapy and not too expensive. In the second part, this manuscript describes the histo-molecular study of several retrospective series of glial and glioneuronal pediatric tumors conducted to improve their characterization and their diagnosis. We confirmed the presence of the fusion gene KIAA1549: BRAF in PA as well as the pejorative nature of the hypothalamic-chiasmatic topography, pilomyxoïde histology and the age at diagnosis less than 36 months. We also showed no molecular difference between cortical grade II gliomas associated with chronic epilepsy and the DNT group. Finally, we showed that DNT, GG and PXA share BRAFV600E mutation and expression of CD34. These studies confirm the major implication of the MAPKinase altered pathway in tumorigenesis of glial and glioneuronal pediatric tumors, constituting a promising therapeutic target.

La classification OMS des tumeurs cérébrales de l'enfant distingue les tumeurs gliales des tumeurs glioneuronales, les gliomes circonscrits des infiltrants. Elle représente le meilleur indicateur pronostic mais se heurte pourtant à des limites de reproductibilité. Pour mieux préciser le diagnostic, mieux définir des sous-groupes de pronostic différent, et mieux orienter le thérapeutique, nous avons recherché les profils moléculaires de 108 tumeurs cérébrales circonscrites de l'enfant : astrocytome pilocytique (PA), tumeurs neuroépithéliales dysembryoplasiques (DNT), xanthoastrocytomes pléïomorphes (PXA) et gangliogliomes (GG). Aucune différence n'est retrouvée entre les gliomes corticaux de grade II (GC) et les DNT concernant IDH1 et 2, TP53 et la délétion1p19q. Les DNT non spécifiques et les GC partagent le même profil incluant CD34 et la mutation V600E de BRAF dans 50% des cas. Le PXA exprime la mutation V600E de BRAF dans plus de 50 % des cas et se rapproche du groupe des tumeurs glioneuronales. Concernant le PA, nous confirmons le caractère péjoratif de la topographie hypothalamo-chiasmatique, de l'histologie pilomyxoide, de l'âge inférieur à 36 mois et de l'exérèse partielle. A l'opposé des tumeurs infiltrantes qui appartiendraient au groupe " histones dépendantes", les tumeurs circonscrites pourraient être regroupées sous le terme "MAPKinases dépendantes". On y distinguerait alors les tumeurs avec fusion KIAA1543-BRAF de celles avec mutation V600E de BRAF. Ce travail a permis de mieux caractériser les tumeurs gliales et glioneuronales de l'enfant, reposant sur le transfert en routine de marqueurs moléculaires simples. / The OMS classification for pediatric brain tumors includes glial tumors and mixed glial and glioneuronal tumors, diffuse and no diffuse glioma. All strategic decision making are based on this current classification but it drives to some limits of diagnosis reproductibility.The goal of our study was to define molecular profils for low grade no diffuse pediatric brain tumors including pilocytic astrocytoma (PA), dysembryoplasic neuroepithelial tumor (DNT), pleiomorphic xanthoastrocytoma (PXA) and benign gangliogliome (GG), to improve the quality of diagnosis, define different subgroups with different prognosis and then to improve treatment strategy decision making.No molecular difference was found between cortical grade II glioma (GC) and DNT regarding IDH1 and 2 TP53 alterations and 1p19q deletion. Similarly 50 % of no specific form of DNT share the same molecular profil with GC with CD34 expression and V600E mutation of BRAF. PXA demonstrated BRAFV600E mutation in 60 % of cases. PXA could then be very close glioneuronal tumors. Finally in PA we confirmed the negative impact of hypothalochiasmatic location, pilomyxoid diagnosis and age lower than 36 months and partial resection. We could work on the elaboration of a new classification and define the group named “Histone dependant” for tumors with histone aberrations and the group named “MAPKinases dependant” for tumors with either KIAA 1543-BRAF fusion or V600E BRAF mutation.In conclusion, this work has led to improve the molecular profil characteristics of glioneuronal tumors of childhood with different easy diagnostic markers that can be used in routine practice, and could potentially replace DNA sequencing.

Nearly 80% of children treated for cancer are expected to survive, but not without cost. Survivors face unprecedented challenges associated with long-term consequences of treatment, also called late effects. Approximately half of children treated for cancer are at risk for experiencing cognitive late effects, which typically emerge several years post diagnosis. The nature and extent of cognitive late effects appear to be developmental and related to patient, disease, and treatment variables. However, the relationships between these variables is not well understood because there have been few prospective and longitudinal studies that report on the contributions of these variables over time. This dissertation examined the effects of patient, disease, and treatment variables, as well as their interactions over time on neurocognitive functioning in childhood cancer survivors. It comprises part of a large prospective, randomized clinical trial designed to examine changes in cognitive function over three years as a function of different levels of monitoring of school-based intervention based on individual educational plans (IEPs). This dissertation uniquely contributed a new measure (the Treatment Intensity Rating Scale) that was used to systematically classify treatment severity across different types of cancer and cancer treatments. Participants included 61 children ages 7 to 12 years at enrollment who were two to five years from completion of treatment for a brain tumor, leukemia, or lymphoma. Participants received yearly neuropsychological evaluations for a follow-up period of 3 years. Results of these evaluations were used to develop IEPs. Participants were randomized to have their IEPs monitored on a quarterly or annual basis for the duration of the study. Contrary to the progressive decline in neurocognitive functioning that is typically anticipated in pediatric cancer survivors, analyses revealed relative stability of performance on neurocognitive measures over time. Higher neurocognitive performance was noted in children whose IEPs were monitored more frequently versus less frequently. Results also supported gender-specific risk for late effects, with lower performance on select neurocognitive measures in females compared to males. Results of this study provide encouraging evidence of the positive effects of school-based interventions and their close monitoring. This has important implications for quality of life as these children survive well beyond childhood into adulthood.

Receiver operating characteristic (ROC) curves have been widely used in evaluation of the goodness of the diagnostic method in many study fields, such as disease diagnosis in medicine. The area under the ROC curve (AUC) naturally became one of the most used variables in gauging the goodness of the diagnosis (Mossman, Somoza 1991). Since medical diagnosis often is not dichotomous, the ROC curve and AUC need to be generalized to a multi-dimensional case. The generalization of AUC to multi-class case has been studied by many researchers in the past decade. Most recently, Nakas & Yiannoutsos (2004) considered the ordered d classes ROC analysis by only considering the sensitivities of each class. Hence, their dimension is only d. Cha (2005) considered more types of mis-classification in the ordered multiple-class case, but reduced the dimension of Ferri, at.el. from d(d-1) to 2(d-1). In this dissertation we are trying to adjust and calculate the VUS for an ordered multipleclass with Cha’s 2(d-1)-dimension method. Our methodology of finding the VUS is introduced. We present the method of adjusting and calculating VUS and their statistical inferences for the 2(d-1)-dimension. Some simulation results are included and a real example will be presented. Intellectual outcomes in pediatric brain-tumor patients were investigated in a prospective longitudinal study. The Standard-Binet Intelligence Scale-Fourth Edition (SB-IV) Standard Age Score (SAS) and Composite intelligence quotient (IQ) score are examined as cognitive outcomes in pediatric brain-tumor patients. Treatment factors, patient factors and time since diagnosis are taken into account as the risk factors. Hierarchical linear/quadratic models and Gompertz based hierarchical nonlinear growth models were applied to build linear and nonlinear longitudinal curves. We use PRESS and Volume Under the Surface (VUS) as the criterions to compare these two methods. Some model interpretations are presented in this dissertation.