Literature DB >> 27329522

Glioblastoma: does the pre-treatment geometry matter? A postcontrast T1 MRI-based study.

Julián Pérez-Beteta1, Alicia Martínez-González2, David Molina2, Mariano Amo-Salas2, Belén Luque2, Elena Arregui3, Manuel Calvo3, José M Borrás3, Carlos López3, Marta Claramonte3, Juan A Barcia4, Lidia Iglesias4, Josué Avecillas4, David Albillo5, Miguel Navarro5, José M Villanueva5, Juan C Paniagua5, Juan Martino6, Carlos Velásquez6, Beatriz Asenjo7, Manuel Benavides7, Ismael Herruzo7, María Del Carmen Delgado8, Ana Del Valle8, Anthony Falkov9, Philippe Schucht10, Estanislao Arana11, Luis Pérez-Romasanta5, Víctor M Pérez-García2.   

Abstract

BACKGROUND: The potential of a tumour's volumetric measures obtained from pretreatment MRI sequences of glioblastoma (GBM) patients as predictors of clinical outcome has been controversial. Mathematical models of GBM growth have suggested a relation between a tumour's geometry and its aggressiveness.
METHODS: A multicenter retrospective clinical study was designed to study volumetric and geometrical measures on pretreatment postcontrast T1 MRIs of 117 GBM patients. Clinical variables were collected, tumours segmented, and measures computed including: contrast enhancing (CE), necrotic, and total volumes; maximal tumour diameter; equivalent spherical CE width and several geometric measures of the CE "rim". The significance of the measures was studied using proportional hazards analysis and Kaplan-Meier curves.
RESULTS: Kaplan-Meier and univariate Cox survival analysis showed that total volume [p = 0.034, Hazard ratio (HR) = 1.574], CE volume (p = 0.017, HR = 1.659), spherical rim width (p = 0.007, HR = 1.749), and geometric heterogeneity (p = 0.015, HR = 1.646) were significant parameters in terms of overall survival (OS). Multivariable Cox analysis for OS provided the later two parameters as age-adjusted predictors of OS (p = 0.043, HR = 1.536 and p = 0.032, HR = 1.570, respectively).
CONCLUSION: Patients with tumours having small geometric heterogeneity and/or spherical rim widths had significantly better prognosis. These novel imaging biomarkers have a strong individual and combined prognostic value for GBM patients. KEY POINTS: • Three-dimensional segmentation on magnetic resonance images allows the study of geometric measures. • Patients with small width of contrast enhancing areas have better prognosis. • The irregularity of contrast enhancing areas predicts survival in glioblastoma patients.

Entities:  

Keywords:  Contrast enhancement; GBM geometry; Glioblastoma; Mathematical model; Predictors of survival

Mesh:

Substances:

Year:  2016        PMID: 27329522     DOI: 10.1007/s00330-016-4453-9

Source DB:  PubMed          Journal:  Eur Radiol        ISSN: 0938-7994            Impact factor:   5.315


  19 in total

1.  Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group.

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

Review 2.  Imaging Genomics in Gliomas.

Authors:  Pascal O Zinn; Zeeshan Mahmood; Mohamed G Elbanan; Rivka R Colen
Journal:  Cancer J       Date:  2015 May-Jun       Impact factor: 3.360

Review 3.  Radiogenomics and imaging phenotypes in glioblastoma: novel observations and correlation with molecular characteristics.

Authors:  Benjamin M Ellingson
Journal:  Curr Neurol Neurosci Rep       Date:  2015-01       Impact factor: 5.081

4.  Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques.

Authors:  Luke Macyszyn; Hamed Akbari; Jared M Pisapia; Xiao Da; Mark Attiah; Vadim Pigrish; Yingtao Bi; Sharmistha Pal; Ramana V Davuluri; Laura Roccograndi; Nadia Dahmane; Maria Martinez-Lage; George Biros; Ronald L Wolf; Michel Bilello; Donald M O'Rourke; Christos Davatzikos
Journal:  Neuro Oncol       Date:  2015-07-16       Impact factor: 12.300

5.  Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities.

Authors:  Haruka Itakura; Achal S Achrol; Lex A Mitchell; Joshua J Loya; Tiffany Liu; Erick M Westbroek; Abdullah H Feroze; Scott Rodriguez; Sebastian Echegaray; Tej D Azad; Kristen W Yeom; Sandy Napel; Daniel L Rubin; Steven D Chang; Griffith R Harsh; Olivier Gevaert
Journal:  Sci Transl Med       Date:  2015-09-02       Impact factor: 17.956

6.  The importance of tumor volume in the prognosis of patients with glioblastoma: comparison of computerized volumetry and geometric models.

Authors:  Georgios Iliadis; Panagiotis Selviaridis; Anna Kalogera-Fountzila; Anna Fragkoulidi; Dimos Baltas; Nikolaos Tselis; Athanasios Chatzisotiriou; Despina Misailidou; Nikolaos Zamboglou; George Fountzilas
Journal:  Strahlenther Onkol       Date:  2009-11-10       Impact factor: 3.621

7.  MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set.

Authors:  David A Gutman; Lee A D Cooper; Scott N Hwang; Chad A Holder; Jingjing Gao; Tarun D Aurora; William D Dunn; Lisa Scarpace; Tom Mikkelsen; Rajan Jain; Max Wintermark; Manal Jilwan; Prashant Raghavan; Erich Huang; Robert J Clifford; Pattanasak Mongkolwat; Vladimir Kleper; John Freymann; Justin Kirby; Pascal O Zinn; Carlos S Moreno; Carl Jaffe; Rivka Colen; Daniel L Rubin; Joel Saltz; Adam Flanders; Daniel J Brat
Journal:  Radiology       Date:  2013-02-07       Impact factor: 11.105

8.  Imaging descriptors improve the predictive power of survival models for glioblastoma patients.

Authors:  Maciej Andrzej Mazurowski; Annick Desjardins; Jordan Milton Malof
Journal:  Neuro Oncol       Date:  2013-02-07       Impact factor: 12.300

9.  Identifying the survival subtypes of glioblastoma by quantitative volumetric analysis of MRI.

Authors:  Zhe Zhang; Haihui Jiang; Xuzhu Chen; Jiwei Bai; Yong Cui; Xiaohui Ren; Xiaolin Chen; Junmei Wang; Wei Zeng; Song Lin
Journal:  J Neurooncol       Date:  2014-05-15       Impact factor: 4.130

10.  A novel volume-age-KPS (VAK) glioblastoma classification identifies a prognostic cognate microRNA-gene signature.

Authors:  Pascal O Zinn; Pratheesh Sathyan; Bhanu Mahajan; John Bruyere; Monika Hegi; Sadhan Majumder; Rivka R Colen
Journal:  PLoS One       Date:  2012-08-03       Impact factor: 3.240

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  12 in total

1.  A mesoscopic simulator to uncover heterogeneity and evolutionary dynamics in tumors.

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Journal:  PLoS Comput Biol       Date:  2021-02-10       Impact factor: 4.475

2.  Morphologic Features on MR Imaging Classify Multifocal Glioblastomas in Different Prognostic Groups.

Authors:  J Pérez-Beteta; D Molina-García; M Villena; M J Rodríguez; C Velásquez; J Martino; B Meléndez-Asensio; Á Rodríguez de Lope; R Morcillo; J M Sepúlveda; A Hernández-Laín; A Ramos; J A Barcia; P C Lara; D Albillo; A Revert; E Arana; V M Pérez-García
Journal:  AJNR Am J Neuroradiol       Date:  2019-03-28       Impact factor: 3.825

3.  Contrast enhancement predicting survival in integrated molecular subtypes of diffuse glioma: an observational cohort study.

Authors:  Johann-Martin Hempel; Cornelia Brendle; Benjamin Bender; Georg Bier; Marco Skardelly; Irina Gepfner-Tuma; Franziska Eckert; Ulrike Ernemann; Jens Schittenhelm
Journal:  J Neurooncol       Date:  2018-04-17       Impact factor: 4.130

4.  A three-dimensional computational analysis of magnetic resonance images characterizes the biological aggressiveness in malignant brain tumours.

Authors:  J Pérez-Beteta; A Martínez-González; V M Pérez-García
Journal:  J R Soc Interface       Date:  2018-12-21       Impact factor: 4.118

5.  Geometrical Measures Obtained from Pretreatment Postcontrast T1 Weighted MRIs Predict Survival Benefits from Bevacizumab in Glioblastoma Patients.

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

6.  Lack of robustness of textural measures obtained from 3D brain tumor MRIs impose a need for standardization.

Authors:  David Molina; Julián Pérez-Beteta; Alicia Martínez-González; Juan Martino; Carlos Velasquez; Estanislao Arana; Víctor M Pérez-García
Journal:  PLoS One       Date:  2017-06-06       Impact factor: 3.240

7.  Preoperative albumin-to-globulin ratio and prognostic nutrition index predict prognosis for glioblastoma.

Authors:  Wen-Zhe Xu; Feng Li; Zhen-Kuan Xu; Xuan Chen; Bin Sun; Jing-Wei Cao; Yu-Guang Liu
Journal:  Onco Targets Ther       Date:  2017-02-08       Impact factor: 4.147

8.  Prognostic models based on imaging findings in glioblastoma: Human versus Machine.

Authors:  David Molina-García; Luis Vera-Ramírez; Julián Pérez-Beteta; Estanislao Arana; Víctor M Pérez-García
Journal:  Sci Rep       Date:  2019-04-12       Impact factor: 4.379

9.  Evolutionary dynamics at the tumor edge reveal metabolic imaging biomarkers.

Authors:  Juan Jiménez-Sánchez; Jesús J Bosque; Germán A Jiménez Londoño; David Molina-García; Álvaro Martínez; Julián Pérez-Beteta; Carmen Ortega-Sabater; Antonio F Honguero Martínez; Ana M García Vicente; Gabriel F Calvo; Víctor M Pérez-García
Journal:  Proc Natl Acad Sci U S A       Date:  2021-02-09       Impact factor: 11.205

10.  Non-standard radiotherapy fractionations delay the time to malignant transformation of low-grade gliomas.

Authors:  Araceli Henares-Molina; Sebastien Benzekry; Pedro C Lara; Marcial García-Rojo; Víctor M Pérez-García; Alicia Martínez-González
Journal:  PLoS One       Date:  2017-06-01       Impact factor: 3.240

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