This dataset comprises information about 1242 lung cancer patients collected by the Medical Oncology Department of the Puerta de Hierro University Hospital of Majadahonda in Madrid, Spain. It includes information about cancer diagnosis and treatment, as well as personal and medical data recorded during anamneses. The dataset could assist in data analysis with the aim of discovering relationships between the applied treatment(s), the evolution of the disease and the associated adverse effects.
View Article and Find Full Text PDFBackground: Current prognosis in oncology is reduced to the tumour stage and performance status, leaving out many other factors that may impact the patient´s management. Prognostic stratification of early stage non-small-cell lung cancer (NSCLC) patients with poor prognosis after surgery is of considerable clinical relevance. The objective of this study was to identify clinical factors associated with long-term overall survival in a real-life cohort of patients with stage I-II NSCLC and develop a prognostic model that identifies features associated with poor prognosis and stratifies patients by risk.
View Article and Find Full Text PDFBackground: Biological differences between the sexes have a major impact on disease and treatment outcome. In this paper, we evaluate the prognostic value of sex in stage IV non-small-cell lung cancer (NSCLC) in the context of routine clinical data, and compare this information with other external datasets.
Methods: Clinical data from stage IV NSCLC patients from Hospital Puerta de Hierro (HPH) were retrieved from electronic health records using big data analytics (N = 397).
Follicular lymphoma (FL) is an indolent but largely incurable disease. Some patients suffer histological transformation to a more aggressive subtype with poorer prognosis. This study aimed to improve our understanding of the genetics underlying FL histological transformation, and to identify genetic drivers or promoters of the transformation by elucidating the differences between FL samples from patients who did and did not transform.
View Article and Find Full Text PDFIf Electronic Health Records contain a large amount of information about the patient's condition and response to treatment, which can potentially revolutionize the clinical practice, such information is seldom considered due to the complexity of its extraction and analysis. We here report on a first integration of an NLP framework for the analysis of clinical records of lung cancer patients making use of a telephone assistance service of a major Spanish hospital. We specifically show how some relevant data, about patient demographics and health condition, can be extracted; and how some relevant analyses can be performed, aimed at improving the usefulness of the service.
View Article and Find Full Text PDFIntroduction: Malignancies are one of the causes of mortality after lung transplantation. However, little is known about lung cancer outcome after lung transplantation.
Methods: We performed a retrospective search of the lung transplantation database at our institution to identify patients diagnosed with lung cancer after lung transplantation.