The Rio Hortega University Hospital Glioblastoma dataset: a comprehensive collection of preoperative, early postoperative and recurrence MRI scans (RHUH-GBM)

Glioblastoma, a highly aggressive primary brain tumor, is associated with poor patient outcomes. Although magnetic resonance imaging (MRI) plays a critical role in diagnosing, characterizing, and forecasting glioblastoma progression, public MRI repositories present significant drawbacks, including insufficient postoperative and follow-up studies as well as expert tumor segmentations. To address these issues, we present the "R\'io Hortega University Hospital Glioblastoma Dataset (RHUH-GBM)," a collection of multiparametric MRI images, volumetric assessments, molecular data, and survival details for glioblastoma patients who underwent total or near-total enhancing tumor resection. The dataset features expert-corrected segmentations of tumor subregions, offering valuable ground truth data for developing algorithms for postoperative and follow-up MRI scans. The public release of the RHUH-GBM dataset significantly contributes to glioblastoma research, enabling the scientific community to study recurrence patterns and develop new diagnostic and prognostic models. This may result in more personalized, effective treatments and ultimately improved patient outcomes

Predicting short-term survival after gross total or near total resection in glioblastomas by machine learning-based radiomic analysis of preoperative MRI

Producción CientíficaRadiomics, in combination with artificial intelligence, has emerged as a powerful tool for the development of predictive models in neuro-oncology. Our study aims to find an answer to a clinically relevant question: is there a radiomic profile that can identify glioblastoma (GBM) patients with short-term survival after complete tumor resection? A retrospective study of GBM patients who underwent surgery was conducted in two institutions between January 2019 and January 2020, along with cases from public databases. Cases with gross total or near total tumor resection were included. Preoperative structural multiparametric magnetic resonance imaging (mpMRI) sequences were pre-processed, and a total of 15,720 radiomic features were extracted. After feature reduction, machine learning-based classifiers were used to predict early mortality (<6 months). Additionally, a survival analysis was performed using the random survival forest (RSF) algorithm. A total of 203 patients were enrolled in this study. In the classification task, the naive Bayes classifier obtained the best results in the test data set, with an area under the curve (AUC) of 0.769 and classification accuracy of 80%. The RSF model allowed the stratification of patients into low- and high-risk groups. In the test data set, this model obtained values of C-Index = 0.61, IBS = 0.123 and integrated AUC at six months of 0.761. In this study, we developed a reliable predictive model of short-term survival in GBM by applying open-source and user-friendly computational means. These new tools will assist clinicians in adapting our therapeutic approach considering individual patient characteristics

Base de datos multicéntrica de hemorragia subaracnoidea espontánea del Grupo de Trabajo de Patología Vascular de la Sociedad Española de Neurocirugía: presentación,criterios de inclusión y desarrollo de una base de datos en internet = Spontaneous Subarachnoid Haemorrhage multicenter database from the Group for the Study of Vascular Pathology of the Spanish Society for Neurosurgery: Presentation, inclusion criteria and development of an internet-based registry

Introducción. La hemorragia subaracnoidea (HSA) continúa siendo una de las enfermedades de interés neuroquirúrgico de más alta morbilidad y mortalidad. Su estudio es clave a la hora de mejorar la atención de estos enfermos en nuestro medio. Con este fin el Grupo de Trabajo de Patología Vascular de la SENEC decidió la creación de una base de datos multicéntrica para su estudio. Material y métodos. Se incluyen en esta base de datos todos los casos de hemorragia subaracnoidea espontánea ingresados en los centros participantes de forma prospectiva desde Noviembre del año 2004 hasta Noviembre del 2007. Se decidieron de forma consensuada los campos a recoger incluyendo edad, antecedentes personales, características clínicas, características radiológicas y del aneurisma, tipo de tratamiento y complicaciones de la enfermedad, evolución según la escala de evolución de Glasgow (GOS) al alta y a los seis meses así como el resultado angiográfico del tratamiento. Todos los campos se recogieron en un formulario rellenable a través de una página web segura. Resultados. En los tres años en los que ha estado activa la base se han recogido un total de 1149 casos de HSA espontánea recogidos por 14 centros participantes. Se ha estimado que es necesario aproximadamente un tiempo de 3.4 minutos para rellenar cada caso. En cuanto a sus características generales la serie es similar a otras series hospitalarias no seleccionadas. La edad media de los enfermos incluidos es de unos 55 años y la relación mujer:hombre 4:3. En cuanto a la gravedad del sagrado inicial un 32% de los enfermos se encontraba en mal grado clínico (WFNS = 4 ó 5). El 5% de los pacientes fallecieron antes de realizarse una angiografía que confirmara el origen aneurismático del sangrado. Se confirmó el origen aneurismático en el 76% de los pacientes mientras que en el 19% no se encontró ninguna lesión vascular responsable del sangrado, siendo clasificados como HSA idiopática. En los pacientes en los que se detectó un aneurisma su tratamiento fue endovascular en el 47% de los casos, quirúrgico en el 39, mixto en el 3% y no recibieron tratamiento de su aneurisma el 11% de los pacientes por fallecimiento precoz. En cuanto a su evolución, la mortalidad global de la serie se sitúa en el 22%. Sólo el 40% de los enfermos con HSA aneurismática presentaron una buena evolución (GOS=5). Conclusiones. La HSA espontánea continúa siendo una enfermedad con alta morbilidad y mortalidad. Esta base de datos puede ser un instrumento para conocer mejor sus características en nuestro medio y mejorar sus resultados, ya que se trata de una serie multicéntrica hospitalaria no seleccionada. Sería pues recomendable que esta base constituyera el germen de un registro nacional de HSA espontánea. Introduction. Subarachnoid haemorrhage is one of the most severe neurosurgical diseases. Its study is crucial for improving the care of these patients in our environment. With this goal the Group for the Study of Neurovascular Pathology of the Spanish Society for Neurosurgery (SENEC) decided to create a multicenter registry for the study of this disease. Materials and methods. In this database we have prospectively included all cases with spontaneous subarachnoid haemorrhage admitted to the participant hospitals from November 2004 to November 2007. The fields to be included in the database were selected by consensus, including age, past medical history, clinical characteristics at admission, radiological characteristics including presence or absence of an aneurysm and its size and location, type and complications of the aneurysm treatment, outcome assessed by the Glasgow Outcome Scale (GOS) at discharge and six months after the bleeding as well as the angiographic result of the aneurysm treatment. All fields were collected by means of an electronic form posted in secure web page. Results. During the three years of study a total of 1149 patients have been included by 14 Hospitals. The time needed to fill in a patient in the registry is approximately 3.4 minutes. This series of patients with spontaneous SAH is similar to other non-selected in-hospital series of SAH. The mean age of the patients is 55 years and there is a 4:3 female to male ratio. In relation to the severity of the bleeding 32% of the patients were in poor clinical grade at admission (WFNS 4 or 5). 5% of the patients died before angiography could be performed. An aneurysm was confirmed as the origin of the bleeding in 76% of the patients (aSAH), while in 19% of the patients no lesion was found in the angiographic studies and were thus classified as idiopathic subarachnoid hemorrhage (ISAH). Of those patients with aSAH, 47% were treated endovascularly, 39% surgically, 3% received a combined treatment and 11% did not receive any treatment for their aneurysm because of early death. Regarding outcome, there is a 22% mortality in the series. Only 40% of the patients with aSAH reached a good outcome at discharge (GOS = 5). Conclusions. Spontaneous SAH continues to be a disease with high morbidity and mortality. This database can be an ideal instrument for improving the knowledge about this disease in our environment and to achieve better results. It would be desirable that this database could in the future be the origin of a national registry of spontaneous SAH