OBJECTIVE: : The main objective of this retrospective work was the study of three-dimensional (3D) heterogeneity measures of post-contrast pre-operative MR images acquired with T1 weighted sequences of patients with glioblastoma (GBM) as predictors of clinical outcome. METHODS: : 79 patients from 3 hospitals were included in the study. 16 3D textural heterogeneity measures were computed including run-length matrix (RLM) features (regional heterogeneity) and co-occurrence matrix (CM) features (local heterogeneity). The significance of the results was studied using Kaplan-Meier curves and Cox proportional hazards analysis. Correlation between the variables of the study was assessed using the Spearman's correlation coefficient. RESULTS: : Kaplan-Meyer survival analysis showed that 4 of the 11 RLM features and 4 of the 5 CM features considered were robust predictors of survival. The median survival differences in the most significant cases were of over 6 months. CONCLUSION: : Heterogeneity measures computed on the post-contrast pre-operative T1 weighted MR images of patients with GBM are predictors of survival. ADVANCES IN KNOWLEDGE:: Texture analysis to assess tumour heterogeneity has been widely studied. However, most works develop a two-dimensional analysis, focusing only on one MRI slice to state tumour heterogeneity. The study of fully 3D heterogeneity textural features as predictors of clinical outcome is more robust and is not dependent on the selected slice of the tumour.
OBJECTIVE: : The main objective of this retrospective work was the study of three-dimensional (3D) heterogeneity measures of post-contrast pre-operative MR images acquired with T1 weighted sequences of patients with glioblastoma (GBM) as predictors of clinical outcome. METHODS: : 79 patients from 3 hospitals were included in the study. 16 3D textural heterogeneity measures were computed including run-length matrix (RLM) features (regional heterogeneity) and co-occurrence matrix (CM) features (local heterogeneity). The significance of the results was studied using Kaplan-Meier curves and Cox proportional hazards analysis. Correlation between the variables of the study was assessed using the Spearman's correlation coefficient. RESULTS: : Kaplan-Meyer survival analysis showed that 4 of the 11 RLM features and 4 of the 5 CM features considered were robust predictors of survival. The median survival differences in the most significant cases were of over 6 months. CONCLUSION: : Heterogeneity measures computed on the post-contrast pre-operative T1 weighted MR images of patients with GBM are predictors of survival. ADVANCES IN KNOWLEDGE:: Texture analysis to assess tumour heterogeneity has been widely studied. However, most works develop a two-dimensional analysis, focusing only on one MRI slice to state tumour heterogeneity. The study of fully 3D heterogeneity textural features as predictors of clinical outcome is more robust and is not dependent on the selected slice of the tumour.
Authors: Patrick Y Wen; David R Macdonald; David A Reardon; Timothy F Cloughesy; A Gregory Sorensen; Evanthia Galanis; John Degroot; Wolfgang Wick; Mark R Gilbert; Andrew B Lassman; Christina Tsien; Tom Mikkelsen; Eric T Wong; Marc C Chamberlain; Roger Stupp; Kathleen R Lamborn; Michael A Vogelbaum; Martin J van den Bent; Susan M Chang Journal: J Clin Oncol Date: 2010-03-15 Impact factor: 44.544
Authors: Juan Jiménez-Sánchez; Álvaro Martínez-Rubio; Anton Popov; Julián Pérez-Beteta; Youness Azimzade; David Molina-García; Juan Belmonte-Beitia; Gabriel F Calvo; Víctor M Pérez-García Journal: PLoS Comput Biol Date: 2021-02-10 Impact factor: 4.475
Authors: David Molina; Julián Pérez-Beteta; Alicia Martínez-González; Juan M Sepúlveda; Sergi Peralta; Miguel J Gil-Gil; Gaspar Reynes; Ana Herrero; Ramón De Las Peñas; Raquel Luque; Jaume Capellades; Carmen Balaña; Víctor M Pérez-García Journal: PLoS One Date: 2016-08-24 Impact factor: 3.240
Authors: Ana María Garcia-Vicente; David Molina; Julián Pérez-Beteta; Mariano Amo-Salas; Alicia Martínez-González; Gloria Bueno; María Jesús Tello-Galán; Ángel Soriano-Castrejón Journal: Ann Nucl Med Date: 2017-09-08 Impact factor: 2.668