Enhanced predictive modelling of toxicities in gynecologic cancer patients treated with high dose rate brachytherapy using SyedNeblett or Fletcher-Suit-Delclos tandem and ovoid applicators: a machine learning perspective

• Autor: Portocarrero Bonifaz, Andres
• Formato: tesis doctoral
• Fecha de Publicación: 2024
• Institución: Pontificia Universidad Católica del Perú
• Repositorio: PUCP-Tesis
• Lenguaje: inglés
• OAI Identifier: oai:tesis.pucp.edu.pe:20.500.12404/29960
• Enlace del recurso: http://hdl.handle.net/20.500.12404/29960
• Nivel de acceso: acceso abierto
• Materia: Inteligencia artificial; Cáncer--Radioterapia; Braquiterapia por radioisótopos; https://purl.org/pe-repo/ocde/ford#1.03.00

This doctoral thesis presents an advanced predictive modeling approach for assessing toxicities in gynecologic cancer patients treated with high-dose-rate (HDR) brachytherapy. Using machine learning algorithms such as Support Vector Machines, Random Forest, and Neural Networks, the study aims to enhance the accuracy of toxicity predictions, thereby allowing the clinician to optimize treatment plans and improving patient outcomes. This research focuses on patients treated with SyedNeblett or Fletcher-Suit-Delclos tandem and ovoid applicators, commonly used in brachytherapy procedures. Objectives include comparing dosimetric profiles and associated toxicities between the two applicator types, investigating the predictive value of non-dosimetric factors, evaluating the performance of various machine learning models against traditional statistical methods, and identifying the most effective predictive model through rigorous cross-validation and feature selection techniques. A comprehensive dataset, one of the most sizeable in this topic, serves as the basis for training and testing the models. By integrating demographic, treatment, and tumor-related data, the study aims to develop ML models that offer a superior performance compared to existing methods. The findings highlight the potential of machine learning to revolutionize brachytherapy planning by providing physicians with precise, patient-specific risk assessments, ultimately enhancing the quality of care for gynecologic cancer patients. This research not only advances the field of radiation oncology but also contributes valuable insights into the integration of machine learning in clinical practice, paving the way for more effective and personalized cancer treatments.