Literature DB >> 33436737

Precise enhancement quantification in post-operative MRI as an indicator of residual tumor impact is associated with survival in patients with glioblastoma.

Alonso Garcia-Ruiz1, Pablo Naval-Baudin2, Marta Ligero1, Albert Pons-Escoda2,3, Jordi Bruna3,4, Gerard Plans3,5, Nahum Calvo2, Monica Cos2, Carles Majós2,3, Raquel Perez-Lopez6,7.   

Abstract

Glioblastoma is the most common primary brain tumor. Standard therapy consists of maximum safe resection combined with adjuvant radiochemotherapy followed by chemotherapy with temozolomide, however prognosis is extremely poor. Assessment of the residual tumor after surgery and patient stratification into prognostic groups (i.e., by tumor volume) is currently hindered by the subjective evaluation of residual enhancement in medical images (magnetic resonance imaging [MRI]). Furthermore, objective evidence defining the optimal time to acquire the images is lacking. We analyzed 144 patients with glioblastoma, objectively quantified the enhancing residual tumor through computational image analysis and assessed the correlation with survival. Pathological enhancement thickness on post-surgical MRI correlated with survival (hazard ratio: 1.98, p < 0.001). The prognostic value of several imaging and clinical variables was analyzed individually and combined (radiomics AUC 0.71, p = 0.07; combined AUC 0.72, p < 0.001). Residual enhancement thickness and radiomics complemented clinical data for prognosis stratification in patients with glioblastoma. Significant results were only obtained for scans performed between 24 and 72 h after surgery, raising the possibility of confounding non-tumor enhancement in very early post-surgery MRI. Regarding the extent of resection, and in agreement with recent studies, the association between the measured tumor remnant and survival supports maximal safe resection whenever possible.

Entities:  

Year:  2021        PMID: 33436737      PMCID: PMC7804103          DOI: 10.1038/s41598-020-79829-3

Source DB:  PubMed          Journal:  Sci Rep        ISSN: 2045-2322            Impact factor:   4.379


  45 in total

1.  3D Slicer as an image computing platform for the Quantitative Imaging Network.

Authors:  Andriy Fedorov; Reinhard Beichel; Jayashree Kalpathy-Cramer; Julien Finet; Jean-Christophe Fillion-Robin; Sonia Pujol; Christian Bauer; Dominique Jennings; Fiona Fennessy; Milan Sonka; John Buatti; Stephen Aylward; James V Miller; Steve Pieper; Ron Kikinis
Journal:  Magn Reson Imaging       Date:  2012-07-06       Impact factor: 2.546

2.  Evaluating intensity normalization on MRIs of human brain with multiple sclerosis.

Authors:  Mohak Shah; Yiming Xiao; Nagesh Subbanna; Simon Francis; Douglas L Arnold; D Louis Collins; Tal Arbel
Journal:  Med Image Anal       Date:  2010-12-25       Impact factor: 8.545

Review 3.  CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2011-2015.

Authors:  Quinn T Ostrom; Haley Gittleman; Gabrielle Truitt; Alexander Boscia; Carol Kruchko; Jill S Barnholtz-Sloan
Journal:  Neuro Oncol       Date:  2018-10-01       Impact factor: 12.300

4.  Immediate post-operative MRI suggestive of the site and timing of glioblastoma recurrence after gross total resection: a retrospective longitudinal preliminary study.

Authors:  Thibault Smets; Tévi Morel Lawson; Cécile Grandin; Aleksandar Jankovski; Christian Raftopoulos
Journal:  Eur Radiol       Date:  2013-01-12       Impact factor: 5.315

5.  High-grade glioma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up.

Authors:  R Stupp; M Brada; M J van den Bent; J-C Tonn; G Pentheroudakis
Journal:  Ann Oncol       Date:  2014-04-29       Impact factor: 32.976

6.  Radiomics based on multicontrast MRI can precisely differentiate among glioma subtypes and predict tumour-proliferative behaviour.

Authors:  Changliang Su; Jingjing Jiang; Shun Zhang; Jingjing Shi; Kaibin Xu; Nanxi Shen; Jiaxuan Zhang; Li Li; Lingyun Zhao; Ju Zhang; Yuanyuan Qin; Yong Liu; Wenzhen Zhu
Journal:  Eur Radiol       Date:  2018-10-12       Impact factor: 5.315

7.  Evaluating the prognostic factors effective on the outcome of patients with glioblastoma multiformis: does maximal resection of the tumor lengthen the median survival?

Authors:  Faramarz Allahdini; Abbass Amirjamshidi; Mohammad Reza-Zarei; Morteza Abdollahi
Journal:  World Neurosurg       Date:  2009-10-21       Impact factor: 2.104

Review 8.  The definition of primary and secondary glioblastoma.

Authors:  Hiroko Ohgaki; Paul Kleihues
Journal:  Clin Cancer Res       Date:  2012-12-03       Impact factor: 12.531

9.  Computational Radiomics System to Decode the Radiographic Phenotype.

Authors:  Joost J M van Griethuysen; Andriy Fedorov; Chintan Parmar; Ahmed Hosny; Nicole Aucoin; Vivek Narayan; Regina G H Beets-Tan; Jean-Christophe Fillion-Robin; Steve Pieper; Hugo J W L Aerts
Journal:  Cancer Res       Date:  2017-11-01       Impact factor: 12.701

10.  A Fully-Automatic Multiparametric Radiomics Model: Towards Reproducible and Prognostic Imaging Signature for Prediction of Overall Survival in Glioblastoma Multiforme.

Authors:  Qihua Li; Hongmin Bai; Yinsheng Chen; Qiuchang Sun; Lei Liu; Sijie Zhou; Guoliang Wang; Chaofeng Liang; Zhi-Cheng Li
Journal:  Sci Rep       Date:  2017-10-30       Impact factor: 4.379

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  2 in total

1.  The Impact of Postoperative Tumor Burden on Patients With Brain Metastases.

Authors:  Amir Kaywan Aftahy; Melanie Barz; Nicole Lange; Lea Baumgart; Cem Thunstedt; Mario Antonio Eller; Benedikt Wiestler; Denise Bernhardt; Stephanie E Combs; Philipp J Jost; Claire Delbridge; Friederike Liesche-Starnecker; Bernhard Meyer; Jens Gempt
Journal:  Front Oncol       Date:  2022-05-04       Impact factor: 5.738

2.  Advances in the In Vivo Quantitative and Qualitative Imaging Characterization of Gliomas.

Authors:  Pierpaolo Alongi; Ignazio Gaspare Vetrano
Journal:  Cancers (Basel)       Date:  2022-07-08       Impact factor: 6.575

  2 in total

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