A lung cancer diagnosis and treatment dataset with geno- and phenotypical characteristics of the patientP4-LUCAT
Belén Ríos-Sánchez,
Guillermo Vigueras,
Aaron García,
Daniel Gómez-Bravo,
Ernestina Menasalvas,
María Torrente,
Consuelo Parejo,
Fotis Aisopos,
Dimitrios Vogiatzis,
Disha Purohit,
Mariano Provencio,
María-Esther Vidal,
Alejandro Rodríguez-González
Affiliations
Belén Ríos-Sánchez
Universidad Politécnica de Madrid, Spain; Corresponding authors.
Guillermo Vigueras
Universidad Politécnica de Madrid, Spain; Corresponding authors.
Aaron García
Universidad Politécnica de Madrid, Spain
Daniel Gómez-Bravo
Universidad Politécnica de Madrid, Spain
Ernestina Menasalvas
Universidad Politécnica de Madrid, Spain
María Torrente
Medical Oncology Department, Puerta de Hierro University Hospital, Servicio Madrileño de Salud, Spain
Consuelo Parejo
Medical Oncology Department, Puerta de Hierro University Hospital, Servicio Madrileño de Salud, Spain
Fotis Aisopos
Institute of Informatics & Telecommunications, National Centre for Scientific Research “Demokritos”, Greece; The American College of Greece, Deree, Greece
Dimitrios Vogiatzis
Institute of Informatics & Telecommunications, National Centre for Scientific Research “Demokritos”, Greece; The American College of Greece, Deree, Greece
Disha Purohit
TIB-Leibniz Information for Centre for Science and Technology & Leibniz University of Hannover & L3S Research, Germany
Mariano Provencio
Medical Oncology Department, Puerta de Hierro University Hospital, Servicio Madrileño de Salud, Spain
María-Esther Vidal
TIB-Leibniz Information for Centre for Science and Technology & Leibniz University of Hannover & L3S Research, Germany
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. A greater understanding of treatment effects based on the particular conditions of the patient and the diagnosis could directly impact the healthcare system, helping to improve expectations about lung cancer as well as reducing treatment toxicities and adverse effects.
