Publicación:
Evaluación de representaciones de datos tabular y volumétrica en el modelamiento predictivo aplicado a la planificación radioterapéutica del cáncer de cabeza y cuello

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dc.contributor.advisorGarcía Jaramillo, Maira Alejandra
dc.contributor.authorAmado Montaña, Jesús Santiago
dc.contributor.authorCárdenas Rodríguez, Yodid Jair
dc.contributor.authorMotta Méndez, José Fernando
dc.contributor.juryMartínez Sepúlveda, José Alejandro
dc.contributor.juryLeón Velásquez, Elizabeth
dc.contributor.researchgroupCiencia, tecnología e innovación::Tecnológico ONTARE Maira Alejandra García Jaramillo Categoría A1 COL0026879
dc.creator.id1233910300
dc.creator.id80872182
dc.creator.id1000271981
dc.date.accessioned2026-06-05T02:24:38Z
dc.date.issued2026-05-27
dc.description.abstractLa planificación de la radioterapia de intensidad modulada (IMRT) en cáncer de cabeza y cuello enfrenta retos complejos derivados de la alta densidad de estructuras anatómicas críticas y la variabilidad en los planes de tratamiento generados manualmente por los especialistas, lo que motiva el desarrollo de modelos de aprendizaje automático capaces de apoyar y estandarizar este proceso. Este trabajo presenta una evaluación metodológica comparativa desde la perspectiva de la Ciencia de Datos, caracterizando dos paradigmas de representación: la representación tabular estructurada, basada en variables clínicas y demográficas, y la representación volumétrica tridimensional, basada en imágenes de tomografía computarizada (CT) y máscaras de estructuras anatómicas segmentadas.spa
dc.description.abstractIntensity-modulated radiation therapy (IMRT) planning for head and neck cancer faces complex challenges stemming from the high density of critical anatomical structures and the variability in treatment plans generated manually by specialists. This motivates the development of machine learning models capable of supporting and standardizing this process. This paper presents a comparative methodological evaluation from a data science perspective, characterizing two representation paradigms: structured tabular representation, based on clinical and demographic variables, and three-dimensional volumetric representation, based on computed tomography (CT) images and masks of segmented anatomical structures.eng
dc.description.degreelevelMaestríaspa
dc.description.degreenameMagíster en Ciencias de Datosspa
dc.description.researchareaCIENCIA, TECNOLOGÍA E INNOVACIÓN::TECNOLOGICO ONTARE MAIRA ALEJANDRA GARCIA JARAMILLO Categoría A1 COL0026879::Tecnología de la información y comunicaciones
dc.description.tableofcontentsLista de Figuras .............................................................................................................................. 11 Lista de Tablas ............................................................................................................................... 12 Anexos .......................................................................................................................................... 12 Introducción .................................................................................................................................. 13 Objetivos ....................................................................................................................................... 18 Objetivo general ..................................................................................................................................... 18 Objetivos específicos .............................................................................................................................. 18 Justificación ................................................................................................................................... 19 Marco Teórico ............................................................................................................................... 20 Naturaleza tridimensional y dinámica del problema dosimétrico ......................................................... 22 Fundamentos del aprendizaje supervisado en contextos clínicos ......................................................... 23 Arquitecturas Volumétricas para Segmentación y Predicción: La U-Net 3D ......................................... 26 Comparación Metodológica entre Enfoques Tabular y Volumétrico ..................................................... 31 Consideraciones Éticas, Regulatorias y de Validación Clínica ................................................................ 33 Hipótesis ....................................................................................................................................... 35 Variables ....................................................................................................................................... 36 1. Variables de entrada – Enfoque Tabular ........................................................................................... 37 1.1 Estadio del tumor (TNM) ............................................................................................................. 37 1.2 Edad del paciente ........................................................................................................................ 37 1.3 Género ......................................................................................................................................... 38 1.4 Número de fracciones ................................................................................................................. 38 1.5 Dosis total prescrita (Gy) ............................................................................................................. 38 2. Variables de entrada – Enfoque Volumétrico .................................................................................... 38 2.1 Imagen CT tridimensional ............................................................................................................ 39 2.2 Máscara del PTV .......................................................................................................................... 39 2.3 Máscaras de órganos en riesgo (OAR) ......................................................................................... 39 3. Variables objetivo ............................................................................................................................... 39 3.1 Variable objetivo en el modelo tabular ....................................................................................... 40 3.2 Variable objetivo en el modelo volumétrico ............................................................................... 40 4. Métricas de evaluación (Variables derivadas) ................................................................................... 40 4.1 Error Absoluto Medio (MAE) ....................................................................................................... 41 4.2 Error Cuadrático Medio (MSE)..................................................................................................... 41 5. Variable independiente principal ....................................................................................................... 41 Metodología .................................................................................................................................. 43 Enfoque de la investigación ................................................................................................................... 43 Diseño de la investigación ...................................................................................................................... 44 Tipo de estudio ....................................................................................................................................... 44 Fases de la investigación ................................................................................................................... 45 Población y muestra ............................................................................................................................... 48 Criterios de Inclusión y Exclusión............................................................................................................ 49 Limitantes de los Datos Abiertos ............................................................................................................ 50 Diseño y validación del instrumento de medición.................................................................................. 51 Procedimientos y técnicas de análisis de la información ....................................................................... 51 Técnicas de análisis: ........................................................................................................................... 52 Trabajo de Campo .......................................................................................................................... 53 Enfoque con datos de HNSCC-3DCT-RT .................................................................................................. 53 Procesamiento de los datos ................................................................................................................... 60 Integración horizontal por “Patient ID”............................................................................................. 61 Normalización y limpieza adicional ................................................................................................... 61 Construcción de variables derivadas ................................................................................................. 61 Longitudinalización del peso ............................................................................................................. 62 Perspectiva desde Ciencia de Datos .................................................................................................. 62 Análisis de resultados ............................................................................................................................. 63 Número de fracciones (Number of fx)............................................................................................... 66 Dosis por fracción (Dose/fx – Gy) ...................................................................................................... 67 Distribución del volumen tumoral (PTV Volume – cm³).................................................................... 69 Relación entre dosis total y número de fracciones ........................................................................... 71 Distribución del número de arcos VMAT .......................................................................................... 72 Modelamiento Supervisado y Resultados .............................................................................................. 73 Configuración e hiperparámetros .......................................................................................................... 74 Resultados .............................................................................................................................................. 74 Interpretación en relación con el OE2 .................................................................................................... 76 Enfoque con datos Open-KBP ................................................................................................................. 77 Fuente de Datos: Open-KBP ................................................................................................................... 78 Configuración del Entorno de Trabajo ................................................................................................... 79 Desactivación del Módulo network_functions.................................................................................. 80 Gestión de Versiones de Keras .......................................................................................................... 80 Verificación de Disponibilidad de GPU .............................................................................................. 80 Preparación y Exploración de los Datos ................................................................................................. 80 Modos de Operación del DataLoader................................................................................................ 81 Arquitectura del Modelo UNet3D ................................................................................................... 82 Justificación de la elección ..................................................................................................................... 82 Estructura Detallada de la Red .............................................................................................................. 82 Normalización: Instance Normalization ................................................................................................. 83 Canales de Entrada ................................................................................................................................ 83 Métricas de Evaluación .......................................................................................................................... 83 Error Absoluto Medio (MAE) - Dose Score ........................................................................................ 84 Error Cuadrático Medio (MSE) .......................................................................................................... 84 Inicialización del Modelo ........................................................................................................................ 84 Bucle de Entrenamiento ......................................................................................................................... 85 Configuración General del Entrenamiento............................................................................................. 85 Perdida Ponderada para PTVs ............................................................................................................... 86 Normalización del CT.............................................................................................................................. 87 Persistencia y Recuperación en Google Drive ........................................................................................ 87 Flujo del Bucle de Entrenamiento por Epoca ......................................................................................... 87 Función de Carga Dinámica de Pacientes .............................................................................................. 88 Reconstrucción de volúmenes Sparse .................................................................................................... 88 Manejo Robusto de Casos Especiales..................................................................................................... 88 Inferencia y Evaluación en Validación .................................................................................................... 88 Flujo de Inferencia por Paciente ............................................................................................................. 89 Análisis de Resultados – Modelo de Predicción de Dosis ....................................................................... 89 Resultados del Entrenamiento .......................................................................................................... 89 Evaluación en Pacientes de Validación (40 pacientes) .......................................................................... 90 Evaluación en Conjunto de Prueba (100 pacientes) ............................................................................... 91 Conclusiones.................................................................................................................................. 92 Discusión ....................................................................................................................................... 94 Bloque 1. Comparación metodológica de los pipelines ......................................................................... 94 Bloque 2. Interpretación de resultados en contexto .............................................................................. 98 Bloque 3. Verificación de hipótesis ........................................................................................................ 99 Bloque 4. El ecosistema de datos en salud como condicionante del modelamiento ........................... 101 Bloque 5. Limitaciones del estudio y su impacto en los resultados ..................................................... 102 Conclusiones y Trabajo Futuro ...................................................................................................... 104 Conclusiones ......................................................................................................................................... 104 Trabajo futuro ............................................................................................................................. 107 Referencias .................................................................................................................................. 110spa
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dc.identifier.instnameinstname:Universidad Eanspa
dc.identifier.localBDM-MGP
dc.identifier.reponamereponame:Repositorio Institucional Biblioteca Digital Minervaspa
dc.identifier.repourlrepourl:https://repository.ean.edu.co/
dc.identifier.urihttps://hdl.handle.net/10882/19290
dc.language.isospa
dc.publisher.facultyFacultad de Ingenieríaspa
dc.publisher.programMaestría en Ciencias de Datosspa
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dc.rights.creativecommonsAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
dc.rights.licenseAtribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)
dc.rights.localAbierto (Texto Completo)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.armarcAprendizaje automático (Inteligencia artificial)spa
dc.subject.armarcInnovaciones en medicinaspa
dc.subject.armarcTecnología médicaspa
dc.subject.armarcCáncer -- Tratamientospa
dc.subject.armarcProcesamiento de datosspa
dc.subject.proposalRadioterapia imrtspa
dc.subject.proposalAprendizaje automáticospa
dc.subject.proposalCáncer de cabeza y cuellospa
dc.subject.proposalRepresentación de datosspa
dc.subject.proposalDeep learningeng
dc.subject.proposalFeature engineeringeng
dc.subject.proposalImrt radiotherapyeng
dc.subject.proposalMachine learningeng
dc.subject.proposalHead and neck cancereng
dc.subject.proposalData representationeng
dc.subject.proposalAprendizaje profundospa
dc.titleEvaluación de representaciones de datos tabular y volumétrica en el modelamiento predictivo aplicado a la planificación radioterapéutica del cáncer de cabeza y cuellospa
dc.titleEvaluation of tabular and volumetric data representations in predictive modeling applied to radiotherapy planning for head and neck cancereng
dc.typeTrabajo de grado - Maestríaspa
dc.type.coarhttp://purl.org/coar/resource_type/c_bdcc
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.contentText
dc.type.driverinfo:eu-repo/semantics/masterThesis
dc.type.otherTrabajo de grado - Maestría
dc.type.redcolhttp://purl.org/redcol/resource_type/TM
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dspace.entity.typePublication
person.affiliation.nameMaestría en Ciencias de Datos
person.affiliation.nameMaestría en Ciencias de Datos
person.affiliation.nameMaestría en Ciencias de Datos
relation.isDirectorOfPublicationca1cbf41-89bb-40b7-9da6-8bb160a2aed3
relation.isDirectorOfPublication.latestForDiscoveryca1cbf41-89bb-40b7-9da6-8bb160a2aed3
relation.isReviewerOfPublication659e8282-959f-4b03-9754-e2fa4eb7ac7c
relation.isReviewerOfPublication.latestForDiscovery659e8282-959f-4b03-9754-e2fa4eb7ac7c

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