Differential effect of vascularity between long- and short-term survivors with IDH1/2 wild-type glioblastoma

[EN] Introduction: IDH1/2 wt glioblastoma (GB) represents the most lethal tumour of the central nervous system. Tumour vascularity is associated with overall survival (OS), and the clinical relevance of vascular markers, such as rCBV, has already been validated. Nevertheless, molecular and clinical factors may have different influences on the beneficial effect of a favourable vascular signature. Purpose: To evaluate the association between the rCBV and OS of IDH1/2 wt GB patients for long-term survivors (LTSs) and short-term survivors (STSs). Given that initial high rCBV may affect the patient's OS in follow-up stages, we will assess whether a moderate vascularity is beneficial for OS in both groups of patients. Materials and methods: Ninety-nine IDH1/2 wt GB patients were divided into LTSs (OS >= 400 days) and STSs (OS < 400 days). Mann-Whitney and Fisher, uni- and multiparametric Cox, Aalen's additive regression and Kaplan-Meier tests were carried out. Tumour vascularity was represented by the mean rCBV of the high angiogenic tumour (HAT) habitat computed through the haemodynamic tissue signature methodology (available on the ONCOhabitats platform). Results: For LTSs, we found a significant association between a moderate value of rCBV(mean) and higher OS (uni- and multiparametric Cox and Aalen's regression) (p = 0.0140, HR = 1.19; p = 0.0085, HR = 1.22) and significant stratification capability (p = 0.0343). For the STS group, no association between rCBV(mean) and survival was observed. Moreover, no significant differences (p > 0.05) in gender, age, resection status, chemoradiation, or MGMT methylation were observed between LTSs and STSs. Conclusion: We have found different prognostic and stratification effects of the vascular marker for the LTS and STS groups. We propose the use of rCBV(mean) at HAT as a vascular marker clinically relevant for LTSs with IDH1/2 wt GB and maybe as a potential target for randomized clinical trials focused on this group of patients.DPI2016-80054-R (Programa Estatal de Promocion del Talento y su Empleabilidad en I +D+i).; European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 844646; H2020-SC1-BHC-2018-2020 (No. 825750); MTS4up project (National Plan for Scientific and Technical Research and Innovation 2013-2016, No. DPI2016-80054-R); European Union's Horizon 2020 research and innovation programme under Marie Sklodowska-Curie, Grant/Award Number: 844646; Research Council of Norway, Grant/Award Number: 261984; South-Eastern Norway Regional Health Authority, Grant/Award Number: 2017073; European Research Council (ERC) under the European Union's Horizon 2020, Grant/Award Number: 758657Álvarez-Torres, MDM.; Fuster García, E.; Reynes, G.; Juan-Albarracín, J.; Chelebian-Kocharyan, EA.; Oleaga, L.; Pineda, J.... (2021). Differential effect of vascularity between long- and short-term survivors with IDH1/2 wild-type glioblastoma. NMR in Biomedicine. 34(4):1-11. https://doi.org/10.1002/nbm.446211134

MGMT methylation may benefit overall survival in patients with moderately vascularized glioblastomas

[EN] Objectives To assess the combined role of tumor vascularity, estimated from perfusion MRI, andMGMTmethylation status on overall survival (OS) in patients with glioblastoma. Methods A multicentric international dataset including 96 patients from NCT03439332 clinical study were used to study the prognostic relationships betweenMGMTand perfusion markers. Relative cerebral blood volume (rCBV) in the most vascularized tumor regions was automatically obtained from preoperative MRIs using ONCOhabitats online analysis service. Cox survival regression models and stratification strategies were conducted to define a subpopulation that is particularly favored byMGMTmethylation in terms of OS. Results rCBV distributions did not differ significantly (p > 0.05) in the methylated and the non-methylated subpopulations. In patients with moderately vascularized tumors (rCBV 10.73), however, there was no significant effect ofMGMTmethylation (HR = 1.72,p = 0.10, AUC = 0.56). Conclusions Our results indicate the existence of complementary prognostic information provided byMGMTmethylation and rCBV. Perfusion markers could identify a subpopulation of patients who will benefit the most fromMGMTmethylation. Not considering this information may lead to bias in the interpretation of clinical studies.Open Access funding provided by University of Oslo (incl Oslo University Hospital). This study has received funding from MTS4up project (National Plan for Scientific and Technical Research and Innovation 2013-2016, No. DPI2016-80054-R) (JMGG); H2020-SC12016-CNECT Project (No. 727560) (JMGG), H2020-SC1-BHC-20182020 (No. 825750) (JMGG), the European Research Council (ERC) under the European Union's Horizon 2020 (Grant Agreement No. 758657), the South-Eastern Norway Regional Health Authority Grants 2017073 and 2013069, the Research Council of Norway Grants 261984 (KEE). M.A.T was supported by Programa Estatal de Promocion del Talento y su Empleabilidad en I+D+i (DPI2016-80054-R). E.F.G was supported by the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement (No. 844646).Fuster García, E.; Lorente Estellés, D.; Álvarez-Torres, MDM.; Juan-Albarracín, J.; Chelebian-Kocharyan, EA.; Rovira, A.; Auger Acosta, C.... (2021). MGMT methylation may benefit overall survival in patients with moderately vascularized glioblastomas. European Radiology. 31(3):1738-1747. https://doi.org/10.1007/s00330-020-07297-41738174731

Diseño de un clasificador radiogenómico de glioblastoma basado en redes convolucionales para la identificación de subtipos moleculares a partir de imágenes de resonancia magnética

[ES] El Glioblastoma Multiforme (GBM) constituye el 60% de todos los tumores cerebrales en adultos. Es la más frecuente de las neoplasias que afectan a las células gliales y también la más maligna, con un pésimo pronóstico. Uno de los mayores impedimentos para su tratamiento es su alta heterogeneidad en todos los ámbitos. La clasificación tradicional de glioblastoma se basaba en sus características macroscópicas e histológicas. Sin embargo, el avance de la genómica ha propiciado un cambio paradigmático hacia una clasificación basada también en sus características genéticas. Una de las clasificaciones que mayor relevancia clínica ha demostrado es la de Verhaak, que establece los subtipos mesenquimal, clásico, proneural y neural, en función del conjunto de mutaciones presentes. Actualmente, la determinación del genotipo en GBM se realiza analizando el tejido tumoral obtenido durante la resección quirúrgica, con las limitaciones que supone en cuanto a la zona accesible y al coste temporal y económico. El campo de la radiogenómica pretende utilizar técnicas de imágenes de resonancia magnética (RM) para identificar mutaciones presentes de manera no invasiva, lo cual supone una alternativa rápida y conveniente de detectar mutaciones analizando la totalidad de la lesión. Las técnicas de machine learning son las que mejores resultados generan en esta tarea. Así, el objetivo del presente trabajo es diseñar un clasificador radiogenómico basado en redes neuronales convolucionales (CNN) para identificar los subtipos de Verhaak a partir de imágenes de RM. Además, aprovechando la definición de hábitats vasculares, se estudia cuáles de ellos guardan relaciones radiogenómicas, con la esperanza de además poder extraer explicaciones biológicas. Para todo ello, se utiliza la base de datos pública TCGA-GBM que contiene un repositorio de estudios de RM con información genómica del paciente. Las imágenes son preprocesadas mediante el software OncoHabitats que, además, identifica los hábitats vasculares. Una vez obtenidas las imágenes se diseña la CNN para la tarea de clasificación y, mediante análisis estadístico, se estudian las relaciones entre el subtipo molecular y los hábitats. El clasificador desarrollado alcanza una exatitud del 71% incluyendo imágenes de T1 con contraste de Gd, T2, FLAIR y mapas de perfusión de flujo y volumen sanguíneo cerebral (rCBV, rCBF). Asimismo, se demuestra que los valores de rCBV en el hábitat del edema periférico potencialmente infiltrado (IPE) son significativos en la discriminación de los subtipos tumorales y, en especial, el proneural. Así, se revela la importancia de la clasificación y correlaciones radiogenómicas, capaces de identificar combinaciones de mutaciones, que en un futuro podrían ser utilizadas para el desarrollo de tratamientos totalmente individualizados en pacientes con GBM.[EN] Glioblastoma Multiforme (GBM) constitutes 60% of all cerebral tumors in adults. It is the most frequent among the neoplasms that affect glial cells, and also the most malignant, with an awful prognosis. One of the main obstacles regarding its treatment is its high heterogeneity in every aspect. The traditional glioblastoma classification is based upon its macroscopic and histologic features. However, the progress in genomics has contributed to a paradigm shift towards a classification based also upon its genetic features. One of the classifications which has showed the most clinical relevance is the Verhaak clasification, establishing mesenchymal, classic, proneural and neural subtypes according to the present mutations. Nowadays, the determination of the GBM genotype is performed by analyzing the tumoral tissue from the surgical resection, with the limitations it has concerning the accesibility of the regions and the temporary and economic cost. The field of radiogenomics expects to identify mutations non-invasively using magnetic resonance (MR) imaging techniques, which signifies a fast and convinient alternative for detecting mutations analyzing the whole lesion. Machine learning techniques have yielded the best results in this task. Thus, the goal of the present project is to design a radiogenomic classifier based on convolutional neural networks (CNN) for identifying Verhaak subtypes from MR images. Also, leveraging the definition of vascular habitats, radiogenimics correlations will be studied, hoping to draw biological conclusions. To this end, TCGA-GBM public database will be used, which includes a MR archive with patient's genomic information. The images are preprocessed by OncoHabitats softwares wich identifies the vascular habitats, too. Once the images are obtained, a CNN is designed for the classification task and, by means of statistical analysis, subtype-habitats correlations are studied. The developed classifier reaches a 71% accuracy including T1-Gd contrast, T2, FLAIR and flow and volume (rCBV, rCBF) perfusion map images. Additionaly, discriminations ability of rCBV in the potentially infiltrated peripheral edema (IPE) has been proven, especially in proneural subtype. Thus, the importance of radiogenomic classification and correlations have been revealed, for they are able to identify combinations of mutations wich could, eventually, be used in individualized treatments of GBM patients.Chelebian Kocharyan, EA. (2019). Diseño de un clasificador radiogenómico de glioblastoma basado en redes convolucionales para la identificación de subtipos moleculares a partir de imágenes de resonancia magnética. http://hdl.handle.net/10251/123009TFG

Estudio de la predicción de supervivencia en glioblastoma mediante redes neuronales convolucionales usando imágenes de resonancia magnética multimodal

[ES] Aproximadamente el 75% de todos los tumores primarios malignos de cerebro en adultos son gliomas. El glioblastoma, el más letal de entre ellos, representa más de la mitad de los gliomas. Con una supervivencia mediana de 14 meses, sólo cerca del 5% de pacientes de glioblastoma sobreviven más de 5 años. La supervivencia del paciente se considera dependiente de diversos factores como la edad, histopatología, localización, zona de resección, terapia postoperatoria o perfil molecular. Ser capaz de estimar la supervivencia del paciente de manera precisa y robusta puede ser útil para su diagnóstico, planificación terapéutica y predicción de resultado. Sin embargo, debido a su gran heterogeneidad, la supervivencia de los pacientes de glioblastoma varía de manera importante, incluso para sujetos que comparten grado de tumor y tratamiento. Las técnicas de imágenes médicas, específicamente las imágenes de resonancia magnética (MRI), son ampliamente usadas para caracterizar la heterogeneidad del glioblastoma. Recientemente, se han realizado avances importantes en la predicción de la supervivencia usando MRI preoperativo, directa e indirectamente mediante biomarcadores moleculares y caracterización de subtipos. Las imágenes preoperativas son menos invasivas y más comprensibles, con relación a las características que afectan a la supervivencia mencionadas anteriormente. Por ejemplo, el nuevo campo de la radiómica permite obtener características moleculares de imágenes. Además, las imágenes de perfusión, como los mapas de volumen sanguíneo cerebral, han demostrado asociación con la supervivencia. La aproximación más extendida para predecir supervivencia se basa en extraer características de imagen con o sin segmentación previa en los diferentes compartimentos del tumor (i.e. necrosis, tumor activo y edema). Las aproximaciones basadas en deep learning que usan redes neuronales convolucionales (CNN) realizan la extracción de características automática, lo cual evita la selección manual de las más relevantes. También permiten variar el alcance de la información, dependiendo de la porción de la imagen utilizada. Finalmente, permiten mantener relaciones espaciales, lo cual es clave para imágenes multimodales. Esta tesis trabaja bajo la hipótesis de que las imágenes anatómicas y los mapas de perfusión preoperativos contienen suficiente información para desarrollar un modelo de regresión de supervivencia para glioblastoma usando CNN. Por tanto, el objetivo principal es diseñar dicho modelo y optimizarlo para predicción de supervivencia. Para ello, se usa un ensayo clínico y una base de datos de libre acceso.[EN] Gliomas account for approximately 75% of all malignant primary brain tumors in adults. Glioblastoma, the most lethal among them, represents more than half malignant gliomas. With a median survival of 14 months, only near 5% of glioblastoma patients survive more than 5 years. Patient survival is considered to be dependent of several features such as age, histopathology, location, extent of resection, post-operative therapy, or molecular profile. Being able to estimate patient overall survival (OS) accurately and robustly can be of value for diagnosis, therapeutic planning, and outcome prediction. Nonetheless, due to its high heterogeneity, glioblastoma patient OS varies broadly, even for subjects who share tumor grade and treatment. Medical imaging techniques, specifically magnetic resonance imaging (MRI), are broadly used to characterize glioblastoma heterogeneity. Recently, important advances have been made toward OS prediction using preoperative MRI, both directly and indirectly through imaging molecular biomarkers or subtype characterization. Pre-operative images are less invasive to obtain and more comprehensive, relative to the aforementioned features that affect survival. For instance, the new field of radiomics allows to obtain molecular features from imaging techniques. In addition, perfusion imaging features, such as cerebral blood volume, are demonstrated to correlate with OS. The most extended approach to predict OS is based on extracting features from the images with or without segmenting the tumor into its different compartments (i.e. necrosis, enhancing tumor and edema). Deep learning approaches using convolutional neural networks (CNN) perform feature extraction automatically which avoids manually selecting the most relevant features. They also allow varying the scope of information, depending on how much of the image is used for the model. Finally, they maintain spatial relationships, which is key for multi-modal images. This thesis works under the hypothesis that pre-operative anatomical images and perfusion maps contain enough information to build a survival regression model for glioblastoma using CNN. Thus, the main objective is to design such a model and optimize survival prediction. To this end, a clinical trial and an independent open dataset are used.Chelebian Kocharyan, EA. (2020). Estudio de la predicción de supervivencia en glioblastoma mediante redes neuronales convolucionales usando imágenes de resonancia magnética multimodal. Universitat Politècnica de València. http://hdl.handle.net/10251/159588TFG