10 research outputs found

    Unsupervised learning for vascular heterogeneity assessment of glioblastoma based on magnetic resonance imaging: The Hemodynamic Tissue Signature

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    [ES] El futuro de la imagen médica está ligado a la inteligencia artificial. El análisis manual de imágenes médicas es hoy en día una tarea ardua, propensa a errores y a menudo inasequible para los humanos, que ha llamado la atención de la comunidad de Aprendizaje Automático (AA). La Imagen por Resonancia Magnética (IRM) nos proporciona una rica variedad de representaciones de la morfología y el comportamiento de lesiones inaccesibles sin una intervención invasiva arriesgada. Sin embargo, explotar la potente pero a menudo latente información contenida en la IRM es una tarea muy complicada, que requiere técnicas de análisis computacional inteligente. Los tumores del sistema nervioso central son una de las enfermedades más críticas estudiadas a través de IRM. Específicamente, el glioblastoma representa un gran desafío, ya que, hasta la fecha, continua siendo un cáncer letal que carece de una terapia satisfactoria. Del conjunto de características que hacen del glioblastoma un tumor tan agresivo, un aspecto particular que ha sido ampliamente estudiado es su heterogeneidad vascular. La fuerte proliferación vascular del glioblastoma, así como su robusta angiogénesis han sido consideradas responsables de la alta letalidad de esta neoplasia. Esta tesis se centra en la investigación y desarrollo del método Hemodynamic Tissue Signature (HTS): un método de AA no supervisado para describir la heterogeneidad vascular de los glioblastomas mediante el análisis de perfusión por IRM. El método HTS se basa en el concepto de hábitat, que se define como una subregión de la lesión con un perfil de IRM que describe un comportamiento fisiológico concreto. El método HTS delinea cuatro hábitats en el glioblastoma: el hábitat HAT, como la región más perfundida del tumor con captación de contraste; el hábitat LAT, como la región del tumor con un perfil angiogénico más bajo; el hábitat IPE, como la región adyacente al tumor con índices de perfusión elevados; y el hábitat VPE, como el edema restante de la lesión con el perfil de perfusión más bajo. La investigación y desarrollo de este método ha originado una serie de contribuciones enmarcadas en esta tesis. Primero, para verificar la fiabilidad de los métodos de AA no supervisados en la extracción de patrones de IRM, se realizó una comparativa para la tarea de segmentación de gliomas de grado alto. Segundo, se propuso un algoritmo de AA no supervisado dentro de la familia de los Spatially Varying Finite Mixture Models. El algoritmo propone una densidad a priori basada en un Markov Random Field combinado con la función probabilística Non-Local Means, para codificar la idea de que píxeles vecinos tienden a pertenecer al mismo objeto. Tercero, se presenta el método HTS para describir la heterogeneidad vascular del glioblastoma. El método se ha aplicado a casos reales en una cohorte local de un solo centro y en una cohorte internacional de más de 180 pacientes de 7 centros europeos. Se llevó a cabo una evaluación exhaustiva del método para medir el potencial pronóstico de los hábitats HTS. Finalmente, la tecnología desarrollada en la tesis se ha integrado en la plataforma online ONCOhabitats (https://www.oncohabitats.upv.es). La plataforma ofrece dos servicios: 1) segmentación de tejidos de glioblastoma, y 2) evaluación de la heterogeneidad vascular del tumor mediante el método HTS. Los resultados de esta tesis han sido publicados en diez contribuciones científicas, incluyendo revistas y conferencias de alto impacto en las áreas de Informática Médica, Estadística y Probabilidad, Radiología y Medicina Nuclear y Aprendizaje Automático. También se emitió una patente industrial registrada en España, Europa y EEUU. Finalmente, las ideas originales concebidas en esta tesis dieron lugar a la creación de ONCOANALYTICS CDX, una empresa enmarcada en el modelo de negocio de los companion diagnostics de compuestos farmacéuticos.[EN] The future of medical imaging is linked to Artificial Intelligence (AI). The manual analysis of medical images is nowadays an arduous, error-prone and often unaffordable task for humans, which has caught the attention of the Machine Learning (ML) community. Magnetic Resonance Imaging (MRI) provides us with a wide variety of rich representations of the morphology and behavior of lesions completely inaccessible without a risky invasive intervention. Nevertheless, harnessing the powerful but often latent information contained in MRI acquisitions is a very complicated task, which requires computational intelligent analysis techniques. Central nervous system tumors are one of the most critical diseases studied through MRI. Specifically, glioblastoma represents a major challenge, as it remains a lethal cancer that, to date, lacks a satisfactory therapy. Of the entire set of characteristics that make glioblastoma so aggressive, a particular aspect that has been widely studied is its vascular heterogeneity. The strong vascular proliferation of glioblastomas, as well as their robust angiogenesis and extensive microvasculature heterogeneity have been claimed responsible for the high lethality of the neoplasm. This thesis focuses on the research and development of the Hemodynamic Tissue Signature (HTS) method: an unsupervised ML approach to describe the vascular heterogeneity of glioblastomas by means of perfusion MRI analysis. The HTS builds on the concept of habitats. A habitat is defined as a sub-region of the lesion with a particular MRI profile describing a specific physiological behavior. The HTS method delineates four habitats within the glioblastoma: the HAT habitat, as the most perfused region of the enhancing tumor; the LAT habitat, as the region of the enhancing tumor with a lower angiogenic profile; the potentially IPE habitat, as the non-enhancing region adjacent to the tumor with elevated perfusion indexes; and the VPE habitat, as the remaining edema of the lesion with the lowest perfusion profile. The research and development of the HTS method has generated a number of contributions to this thesis. First, in order to verify that unsupervised learning methods are reliable to extract MRI patterns to describe the heterogeneity of a lesion, a comparison among several unsupervised learning methods was conducted for the task of high grade glioma segmentation. Second, a Bayesian unsupervised learning algorithm from the family of Spatially Varying Finite Mixture Models is proposed. The algorithm integrates a Markov Random Field prior density weighted by the probabilistic Non-Local Means function, to codify the idea that neighboring pixels tend to belong to the same semantic object. Third, the HTS method to describe the vascular heterogeneity of glioblastomas is presented. The HTS method has been applied to real cases, both in a local single-center cohort of patients, and in an international retrospective cohort of more than 180 patients from 7 European centers. A comprehensive evaluation of the method was conducted to measure the prognostic potential of the HTS habitats. Finally, the technology developed in this thesis has been integrated into an online open-access platform for its academic use. The ONCOhabitats platform is hosted at https://www.oncohabitats.upv.es, and provides two main services: 1) glioblastoma tissue segmentation, and 2) vascular heterogeneity assessment of glioblastomas by means of the HTS method. The results of this thesis have been published in ten scientific contributions, including top-ranked journals and conferences in the areas of Medical Informatics, Statistics and Probability, Radiology & Nuclear Medicine and Machine Learning. An industrial patent registered in Spain, Europe and EEUU was also issued. Finally, the original ideas conceived in this thesis led to the foundation of ONCOANALYTICS CDX, a company framed into the business model of companion diagnostics for pharmaceutical compounds.[CA] El futur de la imatge mèdica està lligat a la intel·ligència artificial. L'anàlisi manual d'imatges mèdiques és hui dia una tasca àrdua, propensa a errors i sovint inassequible per als humans, que ha cridat l'atenció de la comunitat d'Aprenentatge Automàtic (AA). La Imatge per Ressonància Magnètica (IRM) ens proporciona una àmplia varietat de representacions de la morfologia i el comportament de lesions inaccessibles sense una intervenció invasiva arriscada. Tanmateix, explotar la potent però sovint latent informació continguda a les adquisicions de IRM esdevé una tasca molt complicada, que requereix tècniques d'anàlisi computacional intel·ligent. Els tumors del sistema nerviós central són una de les malalties més crítiques estudiades a través de IRM. Específicament, el glioblastoma representa un gran repte, ja que, fins hui, continua siguent un càncer letal que manca d'una teràpia satisfactòria. Del conjunt de característiques que fan del glioblastoma un tumor tan agressiu, un aspecte particular que ha sigut àmpliament estudiat és la seua heterogeneïtat vascular. La forta proliferació vascular dels glioblastomes, així com la seua robusta angiogènesi han sigut considerades responsables de l'alta letalitat d'aquesta neoplàsia. Aquesta tesi es centra en la recerca i desenvolupament del mètode Hemodynamic Tissue Signature (HTS): un mètode d'AA no supervisat per descriure l'heterogeneïtat vascular dels glioblastomas mitjançant l'anàlisi de perfusió per IRM. El mètode HTS es basa en el concepte d'hàbitat, que es defineix com una subregió de la lesió amb un perfil particular d'IRM, que descriu un comportament fisiològic concret. El mètode HTS delinea quatre hàbitats dins del glioblastoma: l'hàbitat HAT, com la regió més perfosa del tumor amb captació de contrast; l'hàbitat LAT, com la regió del tumor amb un perfil angiogènic més baix; l'hàbitat IPE, com la regió adjacent al tumor amb índexs de perfusió elevats, i l'hàbitat VPE, com l'edema restant de la lesió amb el perfil de perfusió més baix. La recerca i desenvolupament del mètode HTS ha originat una sèrie de contribucions emmarcades a aquesta tesi. Primer, per verificar la fiabilitat dels mètodes d'AA no supervisats en l'extracció de patrons d'IRM, es va realitzar una comparativa en la tasca de segmentació de gliomes de grau alt. Segon, s'ha proposat un algorisme d'AA no supervisat dintre de la família dels Spatially Varying Finite Mixture Models. L'algorisme proposa un densitat a priori basada en un Markov Random Field combinat amb la funció probabilística Non-Local Means, per a codificar la idea que els píxels veïns tendeixen a pertànyer al mateix objecte semàntic. Tercer, es presenta el mètode HTS per descriure l'heterogeneïtat vascular dels glioblastomas. El mètode HTS s'ha aplicat a casos reals en una cohort local d'un sol centre i en una cohort internacional de més de 180 pacients de 7 centres europeus. Es va dur a terme una avaluació exhaustiva del mètode per mesurar el potencial pronòstic dels hàbitats HTS. Finalment, la tecnologia desenvolupada en aquesta tesi s'ha integrat en una plataforma online ONCOhabitats (https://www.oncohabitats.upv.es). La plataforma ofereix dos serveis: 1) segmentació dels teixits del glioblastoma, i 2) avaluació de l'heterogeneïtat vascular dels glioblastomes mitjançant el mètode HTS. Els resultats d'aquesta tesi han sigut publicats en deu contribucions científiques, incloent revistes i conferències de primer nivell a les àrees d'Informàtica Mèdica, Estadística i Probabilitat, Radiologia i Medicina Nuclear i Aprenentatge Automàtic. També es va emetre una patent industrial registrada a Espanya, Europa i els EEUU. Finalment, les idees originals concebudes en aquesta tesi van donar lloc a la creació d'ONCOANALYTICS CDX, una empresa emmarcada en el model de negoci dels companion diagnostics de compostos farmacèutics.En este sentido quiero agradecer a las diferentes instituciones y estructuras de financiación de investigación que han contribuido al desarrollo de esta tesis. En especial quiero agradecer a la Universitat Politècnica de València, donde he desarrollado toda mi carrera acadèmica y científica, así como al Ministerio de Ciencia e Innovación, al Ministerio de Economía y Competitividad, a la Comisión Europea, al EIT Health Programme y a la fundación Caixa ImpulseJuan Albarracín, J. (2020). Unsupervised learning for vascular heterogeneity assessment of glioblastoma based on magnetic resonance imaging: The Hemodynamic Tissue Signature [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/149560TESI

    Local detection of microvessels in IDH-wildtype glioblastoma using relative cerebral blood volume: an imaging marker useful for astrocytoma grade 4 classification

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    [EN] Background The microvessels area (MVA), derived from microvascular proliferation, is a biomarker useful for high-grade glioma classification. Nevertheless, its measurement is costly, labor-intense, and invasive. Finding radiologic correlations with MVA could provide a complementary non-invasive approach without an extra cost and labor intensity and from the first stage. This study aims to correlate imaging markers, such as relative cerebral blood volume (rCBV), and local MVA in IDH-wildtype glioblastoma, and to propose this imaging marker as useful for astrocytoma grade 4 classification. Methods Data from 73 tissue blocks belonging to 17 IDH-wildtype glioblastomas and 7 blocks from 2 IDH-mutant astrocytomas were compiled from the Ivy GAP database. MRI processing and rCBV quantification were carried out using ONCOhabitats methodology. Histologic and MRI co-registration was done manually with experts' supervision, achieving an accuracy of 88.8% of overlay. Spearman's correlation was used to analyze the association between rCBV and microvessel area. Mann-Whitney test was used to study differences of rCBV between blocks with presence or absence of microvessels in IDH-wildtype glioblastoma, as well as to find differences with IDH-mutant astrocytoma samples. Results Significant positive correlations were found between rCBV and microvessel area in the IDH-wildtype blocks (p < 0.001), as well as significant differences in rCBV were found between blocks with microvascular proliferation and blocks without it (p < 0.0001). In addition, significant differences in rCBV were found between IDH-wildtype glioblastoma and IDH-mutant astrocytoma samples, being 2-2.5 times higher rCBV values in IDH-wildtype glioblastoma samples. Conclusions The proposed rCBV marker, calculated from diagnostic MRIs, can detect in IDH-wildtype glioblastoma those regions with microvessels from those without it, and it is significantly correlated with local microvessels area. In addition, the proposed rCBV marker can differentiate the IDH mutation status, providing a complementary non-invasive method for high-grade glioma classification.This work was funded by grants from the National Plan for Scientific and Technical Research and Innovation 2017-2020, No. PID2019-104978RB-I00) (JMGG); H2020-SC1-2016-CNECT Project (No. 727560) (JMGG), and H2020SC1-BHC-2018-2020 (No. 825750) (JMGG). M.A.T was supported by DPI201680054-R (Programa Estatal de Promocion del Talento y su Empleabilidad en I + D + i). EFG was supported by the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 844646. The funding body played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.Álvarez-Torres, MDM.; Fuster García, E.; Juan-Albarracín, J.; Reynes, G.; Aparici-Robles, F.; Ferrer Lozano, J.; Garcia-Gomez, JM. (2022). Local detection of microvessels in IDH-wildtype glioblastoma using relative cerebral blood volume: an imaging marker useful for astrocytoma grade 4 classification. BMC Cancer. 22(1):1-13. https://doi.org/10.1186/s12885-021-09117-411322

    Characterization of vascular heterogeneity of astrocytomas grade 4 for supporting patient prognosis estimation, and treatment response assessment

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    [ES] Los tumores cerebrales son una de las enfermedades más devastadoras en la actualidad por el importante deterioro cognitivo que sufren los pacientes, la elevada tasa de mortalidad y el mal pronóstico. Los astrocitomas de grado 4 conllevan una supervivencia de cinco años en aproximadamente el 5% de los pacientes diagnosticados, siendo los tumores más agresivos y letales del Sistema Nervioso Central (SNC). Los astrocitomas de grado 4 siguen siendo un problema médico complejo aún sin resolver. A pesar de representar más del 60% de los tumores cerebrales malignos en adultos, estos tumores tienen una baja prevalencia relativa y se consideran una enfermedad huérfana, lo que dificulta el desarrollo de nuevos fármacos o tratamientos que puedan beneficiar a los pacientes. La agresividad de estos tumores se debe a diferentes características, como la fuerte angiogénesis, la necrosis, la microproliferación vascular, la capacidad de invasión e infiltración de las células tumorales y un microambiente inmunológico particular. Además, debido a la rápida progresión de los astrocitomas de grado 4, en la zona de la lesión coexisten diferentes regiones específicas que cambian con el tiempo. Esta naturaleza compleja, junto con la marcada heterogeneidad interpaciente, intratumoral y longitudinal, complica el éxito de un único tratamiento eficaz para todos los pacientes. La imagen de resonancia magnética (MRI) supone una técnica útil para caracterizar la morfología y la vascularidad del tumor. El uso de métodos avanzados y robustos para analizar las imágenes de MR recogidas en las fases iniciales del tratamiento de los pacientes permite la delimitación de las diferentes regiones de los astrocitomas de grado 4, convirtiéndose en herramientas útiles para investigadores, radiólogos y neurocirujanos. Además, el cálculo de biomarcadores vasculares de imagen, como los propuestos en esta tesis, facilitaría la caracterización del tumor, la estimación del pronóstico y los enfoques de tratamiento más personalizados. Esta tesis propone cuatro pilares fundamentales para avanzar en el manejo de los astrocitomas de grado 4. Estos incluyen I) la caracterización multinivel del tumor para mejorar las clasificaciones de los gliomas de alto grado del SNC; II) la búsqueda y desarrollo de biomarcadores robustos para estimar el pronóstico de los pacientes desde el momento prequirúrgico; III) así como para evaluar la respuesta a los tratamientos y la selección de los pacientes que pueden beneficiarse de terapias específicas; y IV) el diseño e implementación de estudios clínicos y protocolos para la recogida de datos a largo plazo de cohortes de pacientes notables a nivel internacional. Para abordar estos cuatro pilares, se ha utilizado un enfoque interdisciplinario que combina el análisis de imágenes médicas, técnicas avanzadas de inteligencia artificial y variables moleculares, histopatológicas y clínicas. En conclusión, hemos abordado la influencia de la heterogeneidad interpaciente e intratumoral del astrocitoma de grado 4 para la caracterización y clasificación del tumor, la estimación del pronóstico del paciente y la predicción de las respuestas al tratamiento. Además, se han diseñado e implementado diferentes estudios clínicos que permiten la recogida de datos multinivel de cohortes internacionales de pacientes con astrocitoma de grado 4.[CA] Els tumors cerebrals són una de les malalties més devastadores en l'actualitat per la important deterioració cognitiva que pateixen els pacients, l'elevada taxa de mortalitat i el mal pronòstic. Els astrocitomes de grau 4 comporten una supervivència de cinc anys en aproximadament el 5% dels pacients diagnosticats, sent els tumors més agressius i letals del Sistema Nerviós Central (SNC). Els astrocitomes de grau 4 continuen sent un problema mèdic complex encara sense resoldre. Malgrat representar més del 60% dels tumors cerebrals malignes en adults, aquests tumors tenen una baixa prevalença relativa i es consideren una malaltia òrfena, la qual cosa dificulta el desenvolupament de nous fàrmacs o tractaments que puguen beneficiar als pacients. L'agressivitat d'aquests tumors es deu a diferents característiques, com la forta angiogènesis, la necrosi, la microproliferació vascular, la capacitat d'invasió i infiltració de les cèl·lules tumorals i un microambient immunològic particular. A més, a causa de la ràpida progressió dels astrocitomes de grau 4, en la zona de la lesió coexisteixen diferents regions específiques que canvien amb el temps. Aquesta naturalesa complexa, juntament amb la marcada heterogeneïtat interpacient, intratumoral i longitudinal fa que es complique l'èxit d'un únic tractament eficaç per a tots els pacients. L'imatge de ressonància magnètica (MRI) suposa una tècnica útil per a caracteritzar la morfologia i la vascularitat del tumor. L'ús de mètodes avançats i robustos per a analitzar les imatges de MR recollides en les fases inicials del tractament dels pacients permet la delimitació de les diferents regions dels astrocitomes de grau 4, convertint-se en eines útils per a investigadors, radiòlegs i neurocirugians. A més, el càlcul de biomarcadors vasculars d'imatge, com els proposats en aquesta tesi, facilitaria la caracterització del tumor, l'estimació del pronòstic i els enfocaments de tractament més personalitzats. Aquesta tesi proposa quatre pilars fonamentals per a avançar en el maneig dels astrocitomes de grau 4. Aquests inclouen I) la caracterització multinivell del tumor per a millorar les classificacions dels gliomes d'alt grau del SNC; II) la cerca i desenvolupament de biomarcadors robustos per a estimar el pronòstic dels pacients des del moment prequirúrgic; III) així com per a avaluar la resposta als tractaments i la selecció dels pacients que poden beneficiar-se de teràpies específiques; i IV) el disseny i implementació d'estudis clínics i protocols per a la recollida de dades a llarg termini de cohorts de pacients notables a nivell internacional. Per a abordar aquests quatre pilars, s'ha utilitzat un enfocament interdisciplinari que combina l'anàlisi d'imatges mèdiques, tècniques avançades d'intel·ligència artificial i variables moleculars, histopatològiques i clíniques. En conclusió, hem abordat la influència de l'heterogeneïtat interpacient i intratumoral del astrocitoma de grau 4 per a la caracterització i classificació del tumor, l'estimació del pronòstic del pacient i la predicció de les respostes al tractament. A més, s'han dissenyat i implementat diferents estudis clínics que permeten la recollida de dades multinivell de cohorts internacionals de pacients amb astrocitoma de grau 4.[EN] Brain tumors are one of the most devastating diseases today because of the significant cognitive impairment suffered by patients, high mortality rates, and poor prognosis. Astrocytomas grade 4 bring five-year survival in approximately 5% of diagnosed patients, being the most aggressive and lethal tumors of the Central Nervous System (CNS). Astrocytomas grade 4 continue to be an unresolved complex medical problem. Despite accounting for more than 60% of malignant brain tumors in adults, these tumors have a low relative prevalence and are considered an orphan disease, making difficult developing new drugs or treatments that might benefit patients. The aggressiveness of these tumors is due to different characteristics, such as strong angiogenesis, necrosis, vascular microproliferation, the capacity of the tumor cells to invade and infiltrate, and a particular immune microenvironment. In addition, due to the rapid progression of astrocytomas grade 4, different specific regions coexist in the lesion area which change over time. This complex nature, along with the marked interpatient, intratumor, and longitudinal heterogeneity, makes complicate the success of a single efficient treatment for all patients. Magnetic Resonance Imaging (MRI) represents a useful technique to characterize tumor morphology and vascularity. Using advanced and robust methods to analyze MR images collected from initial stages of patient management allows the delineation of different regions of astrocytomas grade 4, becoming useful tools for researchers, radiologists and neurosurgeons. In addition, the calculation of imaging vascular biomarkers, such as those proposed in this thesis, would facilitate tumor characterization, prognosis estimation and more personalized treatment approaches. This thesis proposes four fundamental pillars to advance the management of astrocytomas grade 4. These include I) the multilevel characterization of the tumor to improve classifications of high-grade CNS gliomas; II) the search and development of robust biomarkers for estimating patient prognosis from the presurgical moment; III) as well as for evaluating the response to treatments and the selection of patients who may benefit from specific therapies; and IV) the design and implementation of clinical studies and protocols for long-term collecting data from internationally remarkable cohorts of patients. To address these four pillars, an interdisciplinary approach has been used that combines medical imaging analysis, advanced artificial intelligence techniques, and molecular, histopathological, and clinical variables. Concluding, we have addressed the influence of both interpatient and intratumor heterogeneity of astrocytoma grade 4 for tumor characterization and classification, patient prognosis estimation and predicting treatment responses. In addition, different clinical studies have been designed and implemented allowing the collection of multilevel data from international cohorts of patients with astrocytoma grade 4.Álvarez Torres, MDM. (2022). Characterization of vascular heterogeneity of astrocytomas grade 4 for supporting patient prognosis estimation, and treatment response assessment [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/18895

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

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    [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

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

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    [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

    Lack of Benefit of Extending Temozolomide Treatment in Patients with High Vascular Glioblastoma with Methylated MGMT

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    [EN] Despite the complete treatment with surgery, chemotherapy and radiotherapy, patients with glioblastoma have a devasting prognosis. Although the role of extending temozolomide treatment has been explored, the results are inconclusive. Recent evidence suggested that tumor vascularity may be a modulating factor in combination with methylation of O6-methylguanine-DNA methyltransferase (MGMT) promotor gene on the effect of temozolomide-based therapies, opening new possibilities for personalized treatments. Before proposing a prospective interventional clinical study, it is necessary to confirm the beneficial effect of the combined effect of MGMT methylation and moderate tumor vascularity, as well as the lack of benefit of temozolomide in patients with a highly vascular tumor.In this study, we evaluated the benefit on survival of the combination of methylation of O6-methylguanine-DNA methyltransferase (MGMT) promotor gene and moderate vascularity in glioblastoma using a retrospective dataset of 123 patients from a multicenter cohort. MRI processing and calculation of relative cerebral blood volume (rCBV), used to define moderate- and high-vascular groups, were performed with the automatic ONCOhabitats method. We assessed the previously proposed rCBV threshold (10.7) and the new calculated ones (9.1 and 9.8) to analyze the association with survival for different populations according to vascularity and MGMT methylation status. We found that patients included in the moderate-vascular group had longer survival when MGMT is methylated (significant median survival difference of 174 days, p = 0.0129*). However, we did not find significant differences depending on the MGMT methylation status for the high-vascular group (p = 0.9119). In addition, we investigated the combined correlation of MGMT methylation status and rCBV with the prognostic effect of the number of temozolomide cycles, and only significant results were found for the moderate-vascular group. In conclusion, there is a lack of benefit of extending temozolomide treatment for patients with high vascular glioblastomas, even presenting MGMT methylation. Preliminary results suggest that patients with moderate vascularity and methylated MGMT glioblastomas would benefit more from prolonged adjuvant chemotherapy.M.A-T was supported by DPI2016-80054-R (Programa Estatal de Promocion del Talento y su Empleabilidad en I+D+i). This work was partially supported by the ALBATROSS project (National Plan for Scientific and Technical Research and Innovation 2017-2020, No. PID2019-104978RB-I00). This study was partially funded by the Fundacio La Marato TV3 (665/C/2013) (http://www.ccma.cat/tv3/marato/projectes-financats/2012/231/ Accessed on 8th September 2021). (JMGG); H2020-SC1-2016-CNECT Project (No. 727560) (JMGG), and H2020-SC1-BHC-2018-2020 (No. 825750) (JMGG). EFG was supported by the European Unions Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 844646 and South-Eastern Norway Regional Health Authority Grant 2021057.Álvarez-Torres, MDM.; Fuster García, E.; Balaña, C.; Puig, J.; Garcia-Gomez, JM. (2021). Lack of Benefit of Extending Temozolomide Treatment in Patients with High Vascular Glioblastoma with Methylated MGMT. Cancers. 13(21):1-14. https://doi.org/10.3390/cancers13215420S114132

    Evaluación de las características de estudios de neuroimagen del glioblastoma en relación con el receptor de andrógenos.

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    Introducción: El glioblastoma es el tumor primario más común del sistema nervioso central. Una mayor actividad del receptor de andrógenos se ha asociado previamente con un peor pronóstico de este tumor, pero no se ha establecido una relación entre la actividad del receptor de andrógenos (RA) y las características de imagen. Objetivo: Identificar la relación entre la expresión y actividad del RA en el tejido del glioblastoma y las características de la resonancia magnética (RM) prequirúrgica. Material y métodos: Para este estudio se utilizaron datos de The Cancer Genome Atlas (TCGA) y The Cancer Imaging Archive (TCIA). Solo se seleccionaron pacientes con datos de proteínas y RM prequirúrgica. Los datos de ambas bases de datos se combinaron y analizaron estadísticamente. Los volúmenes de realce tumoral, necrosis y edema T2/FLAIR se estimaron utilizando la plataforma en línea OncoHabitats, que utiliza redes neuronales convolucionales de aprendizaje profundo. La actividad RA se determinó calculando el AR-score, un índice calculado mediante el uso de la expresión (a nivel de ARN) de 13 genes de respuesta a andrógenos, que se ha validado previamente en otras líneas celulares cancerosas. Se realizó un análisis comparativo entre pacientes con AR-score alto o bajo (utilizando p50 como umbral). Se realizaron análisis bivariados de correlación y supervivencia. Resultados: Se incluyeron 47 pacientes (22 mujeres; edad media 55,5 años [DE = 14,32]). Los pacientes con AR-score más alto presentaron peor SG que aquellos con AR-score bajo (15,87 vs. 29,63 meses; p= 0,004). El análisis de regresión lineal mostró una relación negativa entre el AR-score y el volumen de necrosis (Beta=-0,411; p = 0,03). Ninguna otra característica de imagen se asoció con el AR-score. No se encontró asociación entre la expresión de RA y los volúmenes tumorales. Conclusión: Existe una relación negativa entre el volumen de necrosis y la actividad de RA. Ningún otro volumen tumoral muestra asociación ni con expresión RA ni actividad RA.Background: Glioblastoma is the most common primary tumor of the central nervous system. A higher androgen receptor activity has been previously associated with a worse prognosis of this tumor, but no relationship has been established between the androgen receptor (AR) activity and imaging features. Aim: The aim of the present work is to identify any relationship between the androgen receptor expression and activity in glioblastoma tissue and the presurgical magnetic resonance imaging (MRI) features. Methods: Data from The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA) were used for this study. Only patients with protein data and presurgical MRI were selected. Data from both databases was combined and statistically analyzed. Contrast-enhanced tumor, necrosis and T2/FLAIR edema volumes were estimated using the online platform OncoHabitats, that uses deep learning convolutional neural networks. The AR-activity was determined by calculating the AR-score, an index calculated by using the expression (at RNAlevel) of 13 androgen-responsive-genes, which has been previously validated in other cancer cell lines. Comparative analysis between patients with high or low AR-score (using p50 as threshold) was performed. Bivariate correlation and survival analysis were done. Results: Forty-seven patients (22 women; mean age 55.5 years [SD=14.32]) were included in the study. Patients with higher AR-score presented a worse OS than those with low AR-score (15.5 vs. 29.6 months; p=0.017). Linear regression analysis showed a negative relationship between the AR-score and the necrosis volume (Beta=-0.411; p=0.03). No other imaging feature was associated with the AR-score. No association between AR expression and tumoral volumes was found. Conclusion: There is a negative relationship between necrosis volume and AR activity. No other tumor volume shows association nor with AR expression neither AR activit

    Desarrollo de un estudio clínico observacional multicéntrico retrospectivo para validar la tecnología de análisis de imagen médica OncoHabitats

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    [ES] El Glioblastoma es uno de los tumores primarios del sistema nervioso central más frecuente en adultos con una supervivencia media de 15 meses. Desde la publicación del tratamiento Stupp estándar en el año 2005, ninguna terapia ha demostrado una mejora significativa en la supervivencia global de los pacientes que lo sufren. Uno de los factores responsables de su alta agresividad y resistencia contra las terapias existentes, es su heterogeneidad intra- e inter-paciente. Esta situación pone de manifiesto la necesidad de crear tecnologías que de forma no invasiva y precisa sean capaces de crear subgrupos de pacientes con una baja heterogeneidad, permitiendo la creación de terapias mucho más dirigidas. La tecnología de análisis de imagen médica OncoHabitats define unas regiones de interés y marcadores asociados dentro de las zonas de tumor activo y de edema, en función de la información hemodinámica obtenida a partir de las secuencias de perfusión de los estudios de imágenes de resonancia magnética. En las pruebas de concepto realizadas se ha obtenido una correlación significativa entre estos marcadores y la supervivencia global del paciente. Sin embargo, para establecerse como un biomarcador pronóstico se debe de crear un estudio que englobe una mayor cantidad de pacientes y de centros. Por esta razón, se ha desarrollado un estudio multicéntrico e internacional de tipo observacional y retrospectivo, en el cual han participado 7 hospitales pertenecientes a 4 países diferentes, reclutando un total de 305 pacientes. En el presente trabajo se han desarrollado todas las fases del estudio, excepto la obtención de los resultados estadísticos definitivos. Se ha comprobado que los resultados preliminares se mantienen en línea con los hallazgos de los estudios piloto. La validación de OncoHabitats como biomarcador pronóstico permitirá su uso como herramienta de decisión en el seguimiento de pacientes con Glioblastoma.[EN] Glioblastoma is one of the most common primary tumours of the central nervous system in adults with an average survival of 15 months. Since the publication of the standard Stupp treatment in 2005, no therapy has shown a significant improvement in the overall survival of patients suffering from it. One of the factors responsible for its high aggressiveness and resistance against existing therapies is its intra- and inter-patient heterogeneity. This situation highlights the need to create technologies that are non-invasively and precisely capable of creating subgroups of patients with low heterogeneity, allowing the creation of much more targeted therapies. The OncoHabitats medical image analysis technology defines regions of interest and associated markers within the active tumor and edema zones, based on hemodynamic information obtained from the perfusion sequences of magnetic resonance imaging studies. In the proofs of concept carried out, a significant correlation has been obtained between these markers and the overall survival of the patient. However, in order to establish itself as a prognostic biomarker, it is necessary to create a study that includes a larger number of patients and centres. For this reason, a multicenter, international observational and retrospective study has been developed in which 7 hospitals from 4 different countries participated, recruiting a total of 305 patients. In the present work, all phases of the study have been developed, except the obtaining of the definitive statistical results. It has been proven that the preliminary results are in line with the findings of the pilot studies. The validation of OncoHabitats as a prognostic biomarker will allow its use as a decision tool in the follow-up of patients with Glioblastoma.Bellvis Bataller, F. (2018). Desarrollo de un estudio clínico observacional multicéntrico retrospectivo para validar la tecnología de análisis de imagen médica OncoHabitats. http://hdl.handle.net/10251/110659TFG

    Brain MRI study for glioma segmentation using convolutional neural networks and original post-processing techniques with low computational demand

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    Gliomas are brain tumors composed of different highly heterogeneous histological subregions. Image analysis techniques to identify relevant tumor substructures have high potential for improving patient diagnosis, treatment and prognosis. However, due to the high heterogeneity of gliomas, the segmentation task is currently a major challenge in the field of medical image analysis. In the present work, the database of the Brain Tumor Segmentation (BraTS) Challenge 2018, composed of multimodal MRI scans of gliomas, was studied. A segmentation methodology based on the design and application of convolutional neural networks (CNNs) combined with original post-processing techniques with low computational demand was proposed. The post-processing techniques were the main responsible for the results obtained in the segmentations. The segmented regions were the whole tumor, the tumor core, and the enhancing tumor core, obtaining averaged Dice coefficients equal to 0.8934, 0.8376, and 0.8113, respectively. These results reached the state of the art in glioma segmentation determined by the winners of the challenge.Comment: 34 pages, 12 tables, 23 figure

    Magnetic resonance image-based brain tumour segmentation methods : a systematic review

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    Background: Image segmentation is an essential step in the analysis and subsequent characterisation of brain tumours through magnetic resonance imaging. In the literature, segmentation methods are empowered by open-access magnetic resonance imaging datasets, such as the brain tumour segmentation dataset. Moreover, with the increased use of artificial intelligence methods in medical imaging, access to larger data repositories has become vital in method development. Purpose: To determine what automated brain tumour segmentation techniques can medical imaging specialists and clinicians use to identify tumour components, compared to manual segmentation. Methods: We conducted a systematic review of 572 brain tumour segmentation studies during 2015–2020. We reviewed segmentation techniques using T1-weighted, T2-weighted, gadolinium-enhanced T1-weighted, fluid-attenuated inversion recovery, diffusion-weighted and perfusion-weighted magnetic resonance imaging sequences. Moreover, we assessed physics or mathematics-based methods, deep learning methods, and software-based or semi-automatic methods, as applied to magnetic resonance imaging techniques. Particularly, we synthesised each method as per the utilised magnetic resonance imaging sequences, study population, technical approach (such as deep learning) and performance score measures (such as Dice score). Statistical tests: We compared median Dice score in segmenting the whole tumour, tumour core and enhanced tumour. Results: We found that T1-weighted, gadolinium-enhanced T1-weighted, T2-weighted and fluid-attenuated inversion recovery magnetic resonance imaging are used the most in various segmentation algorithms. However, there is limited use of perfusion-weighted and diffusion-weighted magnetic resonance imaging. Moreover, we found that the U-Net deep learning technology is cited the most, and has high accuracy (Dice score 0.9) for magnetic resonance imaging-based brain tumour segmentation. Conclusion: U-Net is a promising deep learning technology for magnetic resonance imaging-based brain tumour segmentation. The community should be encouraged to contribute open-access datasets so training, testing and validation of deep learning algorithms can be improved, particularly for diffusion- and perfusion-weighted magnetic resonance imaging, where there are limited datasets available
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