Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 2 de 2
Filtrar
Más filtros











Intervalo de año de publicación
1.
Eur Radiol ; 29(4): 1968-1977, 2019 Apr.
Artículo en Inglés | MEDLINE | ID: mdl-30324390

RESUMEN

OBJECTIVES: We wished to determine whether tumor morphology descriptors obtained from pretreatment magnetic resonance images and clinical variables could predict survival for glioblastoma patients. METHODS: A cohort of 404 glioblastoma patients (311 discoveries and 93 validations) was used in the study. Pretreatment volumetric postcontrast T1-weighted magnetic resonance images were segmented to obtain the relevant morphological measures. Kaplan-Meier, Cox proportional hazards, correlations, and Harrell's concordance indexes (c-indexes) were used for the statistical analysis. RESULTS: A linear prognostic model based on the outstanding variables (age, contrast-enhanced (CE) rim width, and surface regularity) identified a group of patients with significantly better survival (p < 0.001, HR = 2.57) with high accuracy (discovery c-index = 0.74; validation c-index = 0.77). A similar model applied to totally resected patients was also able to predict survival (p < 0.001, HR = 3.43) with high predictive value (discovery c-index = 0.81; validation c-index = 0.92). Biopsied patients with better survival were well identified (p < 0.001, HR = 7.25) by a model including age and CE volume (c-index = 0.87). CONCLUSIONS: Simple linear models based on small sets of meaningful MRI-based pretreatment morphological features and age predicted survival of glioblastoma patients to a high degree of accuracy. The partition of the population using the extent of resection improved the prognostic value of those measures. KEY POINTS: • A combination of two MRI-based morphological features (CE rim width and surface regularity) and patients' age outperformed previous prognosis scores for glioblastoma. • Prognosis models for homogeneous surgical procedure groups led to even more accurate survival prediction based on Kaplan-Meier analysis and concordance indexes.


Asunto(s)
Neoplasias Encefálicas/patología , Glioblastoma/patología , Adolescente , Adulto , Anciano , Anciano de 80 o más Años , Neoplasias Encefálicas/mortalidad , Femenino , Glioblastoma/mortalidad , Humanos , Estimación de Kaplan-Meier , Imagen por Resonancia Magnética/métodos , Imagen por Resonancia Magnética/mortalidad , Masculino , Persona de Mediana Edad , Pronóstico , Adulto Joven
2.
Eur Radiol ; 29(5): 2729, 2019 05.
Artículo en Inglés | MEDLINE | ID: mdl-30547198

RESUMEN

The original version of this article, published on 15 October 2018, unfortunately contained a mistake. The following correction has therefore been made in the original: The name of Mariano Amo-Salas and the affiliation of Ismael Herruzo were presented incorrectly.

SELECCIÓN DE REFERENCIAS
DETALLE DE LA BÚSQUEDA