Publicação
Survival Analysis for Late Stage Lung Cancer
| Resumo: | The prevalence of cancer, one of the main causes of death globally, has been agravatting by projections of an increase in cases [1]. Lung cancer, in particular, has represented the highest mortality rate among the various histologies. There are various efforts to combat diagnosis only at advanced stages, where survival rates are relatively low and the treatments applied are often palliative. Against this backdrop, a significant amount of research has been directed towards exploring innovative approaches to treating this pathology. The aim is to provide personalized and specialized therapies to patients, improving their quality of life and allowing for more effective follow-up at these stages. This study underlines the crucial importance of data analysis in improving healthcare. Its role has been vital not only in oncology, but also in other areas of medicine, allowing healthcare professionals to make more informed and effective decisions. The dissertation uses a dataset from the Puerta del Hierro Majadahonda University Hospital, a renowned Spanish oncology center, providing a comprehensive overview of the profiles of patients suffering from the condition. The main purpose of this investigation is to look into the effect of a wide set of variables, ranging from demographic to clinical, on the survival rate of these individuals. The primary focus is on patients who have advanced non-small cell lung cancer. This method provides as a link to prior research on the same dataset, which focused on the disease’s early phases with less data and features accessible [2] [3]. In order to accomplish this, specialized statistical models are used that include the Kaplan-Meier estimators, the Cox proportional hazard model, and the logrank test, which is used to compare the results of different patient groups. |
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| Autores principais: | Estêvão, Rui Miguel Gomes |
| Assunto: | Lung cancer Non-small cell cancer Kaplan-meier Logrank Cox proportional hazard model Late stages |
| Ano: | 2024 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | The prevalence of cancer, one of the main causes of death globally, has been agravatting by projections of an increase in cases [1]. Lung cancer, in particular, has represented the highest mortality rate among the various histologies. There are various efforts to combat diagnosis only at advanced stages, where survival rates are relatively low and the treatments applied are often palliative. Against this backdrop, a significant amount of research has been directed towards exploring innovative approaches to treating this pathology. The aim is to provide personalized and specialized therapies to patients, improving their quality of life and allowing for more effective follow-up at these stages. This study underlines the crucial importance of data analysis in improving healthcare. Its role has been vital not only in oncology, but also in other areas of medicine, allowing healthcare professionals to make more informed and effective decisions. The dissertation uses a dataset from the Puerta del Hierro Majadahonda University Hospital, a renowned Spanish oncology center, providing a comprehensive overview of the profiles of patients suffering from the condition. The main purpose of this investigation is to look into the effect of a wide set of variables, ranging from demographic to clinical, on the survival rate of these individuals. The primary focus is on patients who have advanced non-small cell lung cancer. This method provides as a link to prior research on the same dataset, which focused on the disease’s early phases with less data and features accessible [2] [3]. In order to accomplish this, specialized statistical models are used that include the Kaplan-Meier estimators, the Cox proportional hazard model, and the logrank test, which is used to compare the results of different patient groups. |
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