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dc.contributor.advisorOrtega-Martorell, Sandra
dc.contributor.authorHernández Rodríguez, Orlando
dc.contributor.authorRestrepo Galvis, Paula Restrepo
dc.date.accessioned2023-02-03T20:21:01Z
dc.date.available2023-02-03T20:21:01Z
dc.date.issued2022
dc.identifier.urihttps://repositorio.escuelaing.edu.co/handle/001/2168
dc.description.abstractEste estudio presenta la comparación entre varios modelos de aprendizaje automático con el fin de establecer el mejor modelo para predecir si hay respuesta a la terapia en pacientes comprometidos con glioblastomas (GBM), utilizando señales derivadas de imágenes espectroscópicas de resonancia magnética (MRSI). La aplicación de modelos de clasificación lineales, no lineales y redes neuronales convolucionadas de una dimensión permitirían determinar la temprana identificación del nivel respuesta a la terapia, con el objetivo de personalizar los tratamientos para el GBM y así mejorar su eficacia, pues podría incrementar la tasa de supervivencia. Actualmente, los pacientes diagnosticados con GBM reciben tratamiento de quimioterapia y radioterapia, y en algunos casos incluye la resección quirúrgica del tumor. Posterior al tratamiento, puede ocurrir remisión del tumor. Nuestro aporte radica en mejorar la interpretación de los datos obtenidos en la fase de tratamiento, para ayudar a entender, de una manera no invasiva, si los tumores están en respuesta a la terapia. Para ello estudiaremos la composición química de las muestras revelando la información metabólica (biomarcadores) (Horská & Barker, 2010), para profundizar en la investigación del GBM, uno de los tumores cerebrales más agresivos y fatales en los humanos. La comparación de estos modelos y su respectiva evaluación tendrán presente métricas relacionadas con estudios médicos, analizando el desempeño de los modelos por medio de la especificidad de los resultados y así evaluar la capacidad de discriminar los falsos negativos que se traduce en la falta de la detección temprana de la enfermedad (Komori, 2022).spa
dc.description.abstractThis paper presents the comparison between several machine learning models to establish the best model to predict response to therapy in glioblastomas (GBM) patients, based on the analysis of signals derived from magnetic resonance spectroscopic imaging (MRSI). This analysis would allow early identification of the therapy response, enabling personalization of treatments for GBM and thus improving their efficacy, as it could increase the survival rate. The application of linear, nonlinear classification models and one-dimensional convolutional neural networks would make it possible to determine whether there is a response to the treatment provided. Currently, after patients have been diagnosed with GBM (for which MRSI can be used), treatment would include chemotherapy and radiotherapy, even surgical resection of the tumour area. Still, remission can occur. Our contribution lies in improving the interpretation of the data obtained during the therapy to understand the chemical composition of the samples revealing metabolic information (biomarkers) from the analysis of larger areas compared to previous technologies (Horská & Barker, 2010). This is essential for further investigation of GBM, one of the most aggressive and fatal tumours in humans. The comparison of these models and their respective evaluation will take into account metrics related to medical studies, analyzing the performance of the models through the specificity of the results and thus demonstrating the ability to discriminate false negatives that results in the lack of early detection of the disease (Komori, 2022).eng
dc.format.extent66páginasspa
dc.format.mimetypeapplication/pdfspa
dc.language.isospaspa
dc.publisherEscuela Colombiana De Ingeniería Julio Garavitospa
dc.titleAplicación de redes neuronales convolucionadas de una dimensión para estudiar la respuesta a la terapia en glioblastomasspa
dc.typeTrabajo de grado - Maestríaspa
dc.type.versioninfo:eu-repo/semantics/publishedVersionspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85spa
dc.description.degreelevelMaestríaspa
dc.description.degreenameMagíster en Ciencia de Datosspa
dc.identifier.urlhttps://catalogo.escuelaing.edu.co/cgi-bin/koha/opac-detail.pl?biblionumber=23286
dc.publisher.placeBogotá D.Cspa
dc.publisher.programMaestría en Ciencia de Datosspa
dc.relation.indexedN/Aspa
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dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.subject.armarcCerebro - Tumores
dc.subject.armarcRedes neuronales (Neurobiología)
dc.subject.armarcGlioblastoma
dc.subject.proposalCerebro - Tumoresspa
dc.subject.proposalBrain - Tumorseng
dc.subject.proposalRedes neuronales (Neurobiología)spa
dc.subject.proposalGlioblastomaspa
dc.subject.proposalGlioblastomaeng
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