Literature DB >> 32328652

Volumetric analysis of IDH-mutant lower-grade glioma: a natural history study of tumor growth rates before and after treatment.

Raymond Y Huang1, Robert J Young2, Benjamin M Ellingson3, Harini Veeraraghavan4, Wei Wang5, Florent Tixier4, Hyemin Um4, Rasheed Nawaz1, Tracy Luks6, John Kim7, Elizabeth R Gerstner8, David Schiff9, Katherine B Peters10, Ingo K Mellinghoff11, Susan M Chang12, Timothy F Cloughesy13, Patrick Y Wen14.   

Abstract

BACKGROUND: Lower-grade gliomas (LGGs) with isocitrate dehydrogenase 1 and/or 2 (IDH1/2) mutations have long survival times, making evaluation of treatment efficacy difficult. We investigated the volumetric growth rate of IDH mutant gliomas before and after treatment with established glioma therapies to determine whether a significant change in growth rate could be documented and perhaps be used in the future to evaluate treatment response to investigational agents in LGG trials.
METHODS: In this multicenter retrospective study, 230 adult patients with IDH1/2 mutated LGGs (World Health Organization grade II or III) undergoing surgery, radiation, or chemotherapy for progressive non-enhancing tumor were identified. Subjects were required to have 3 MRI scans containing T2/fluid attenuated inversion recovery imaging spanning a minimum of 6 months prior to treatment. A mixed-effect model was used to estimate tumor growth prior to treatment. A subset of 95 patients who received chemotherapy, radiotherapy, or chemoradiotherapy and had 2 posttreatment imaging time points available were evaluated for change in pre- and posttreatment volumetric growth rates using a piecewise mixed model.
RESULTS: The pretreatment volumetric growth rate across all 230 patients was 27.37%/180 days (95% CI: [23.36%, 31.51%]). In the 95 patients with both pre- and posttreatment scans available, there was a significant difference in volumetric growth rates before (26.63%/180 days, 95% CI: [19.31%, 34.40%]) and after treatment (-15.24% /180 days, 95% CI: [-21.37%, -8.62%]) (P < 0.0001). The growth rates for patient subgroup with 1p/19q codeletion (N = 118) was significantly slower than the rate of the 1p/19q non-codeleted group (N = 68) (22.84% vs 35.49%, P = 0.0108).
CONCLUSION: In this study, we evaluated the growth rates of IDH mutant gliomas before and after standard therapy. Further study is needed to establish whether a change in growth rate is associated with patient survival and its use as a surrogate endpoint in clinical trials for IDH mutant LGGs.
© The Author(s) 2020. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Keywords:  IDH; LGG; MRI; growth rate; lower grade gliomas; volume

Mesh:

Substances:

Year:  2020        PMID: 32328652      PMCID: PMC7746936          DOI: 10.1093/neuonc/noaa105

Source DB:  PubMed          Journal:  Neuro Oncol        ISSN: 1522-8517            Impact factor:   12.300


  21 in total

1.  Assessment of intra-observer variability in measurement of high-grade brain tumors.

Authors:  James M Provenzale; Michael C Mancini
Journal:  J Neurooncol       Date:  2012-03-10       Impact factor: 4.130

2.  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

3.  ACTIVE LEARNING GUIDED INTERACTIONS FOR CONSISTENT IMAGE SEGMENTATION WITH REDUCED USER INTERACTIONS.

Authors:  Harini Veeraraghavan; James V Miller
Journal:  Proc IEEE Int Symp Biomed Imaging       Date:  2011-06-09

4.  Comparison of supervised MRI segmentation methods for tumor volume determination during therapy.

Authors:  M Vaidyanathan; L P Clarke; R P Velthuizen; S Phuphanich; A M Bensaid; L O Hall; J C Bezdek; H Greenberg; A Trotti; M Silbiger
Journal:  Magn Reson Imaging       Date:  1995       Impact factor: 2.546

5.  Random-effects models for longitudinal data.

Authors:  N M Laird; J H Ware
Journal:  Biometrics       Date:  1982-12       Impact factor: 2.571

6.  Long-term efficacy of early versus delayed radiotherapy for low-grade astrocytoma and oligodendroglioma in adults: the EORTC 22845 randomised trial.

Authors:  M J van den Bent; D Afra; O de Witte; M Ben Hassel; S Schraub; K Hoang-Xuan; P-O Malmström; L Collette; M Piérart; R Mirimanoff; A B M F Karim
Journal:  Lancet       Date:  2005 Sep 17-23       Impact factor: 79.321

7.  Continuous growth of mean tumor diameter in a subset of grade II gliomas.

Authors:  Emmanuel Mandonnet; Jean-Yves Delattre; Marie-Laure Tanguy; Kristin R Swanson; Antoine F Carpentier; Hugues Duffau; Philippe Cornu; Rémy Van Effenterre; Ellsworth C Alvord; Laurent Capelle
Journal:  Ann Neurol       Date:  2003-04       Impact factor: 10.422

Review 8.  Response assessment in neuro-oncology (a report of the RANO group): assessment of outcome in trials of diffuse low-grade gliomas.

Authors:  M J van den Bent; J S Wefel; D Schiff; M J B Taphoorn; K Jaeckle; L Junck; T Armstrong; A Choucair; A D Waldman; T Gorlia; M Chamberlain; B G Baumert; M A Vogelbaum; D R Macdonald; D A Reardon; P Y Wen; S M Chang; A H Jacobs
Journal:  Lancet Oncol       Date:  2011-04-05       Impact factor: 41.316

9.  Low-grade gliomas: six-month tumor growth predicts patient outcome better than admission tumor volume, relative cerebral blood volume, and apparent diffusion coefficient.

Authors:  Gisele Brasil Caseiras; Olga Ciccarelli; Daniel R Altmann; Christopher E Benton; Daniel J Tozer; Paul S Tofts; Tarek A Yousry; Jeremy Rees; Adam D Waldman; Hans Rolf Jäger
Journal:  Radiology       Date:  2009-09-29       Impact factor: 11.105

10.  Reliability of tumor volume estimation from MR images in patients with malignant glioma. Results from the American College of Radiology Imaging Network (ACRIN) 6662 Trial.

Authors:  Birgit B Ertl-Wagner; Jeffrey D Blume; Donald Peck; Jayaram K Udupa; Benjamin Herman; Anthony Levering; Ilona M Schmalfuss
Journal:  Eur Radiol       Date:  2008-10-17       Impact factor: 5.315

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  7 in total

Review 1.  Future development of chimeric antigen receptor T cell therapies for patients suffering from malignant glioma.

Authors:  Payal B Watchmaker; Maggie Colton; Psalm L Pineo-Cavanaugh; Hideho Okada
Journal:  Curr Opin Oncol       Date:  2022-07-19       Impact factor: 3.915

Review 2.  Novel Clinical Trial Designs in Neuro-Oncology.

Authors:  Anurag Saraf; Lorenzo Trippa; Rifaquat Rahman
Journal:  Neurotherapeutics       Date:  2022-08-15       Impact factor: 6.088

3.  Hypothetical generalized framework for a new imaging endpoint of therapeutic activity in early phase clinical trials in brain tumors.

Authors:  Benjamin M Ellingson; Elizabeth R Gerstner; Andrew B Lassman; Caroline Chung; Howard Colman; Patricia E Cole; David Leung; Joshua E Allen; Manmeet S Ahluwalia; Jerrold Boxerman; Matthew Brown; Jonathan Goldin; Edjah Nduom; Islam Hassan; Mark R Gilbert; Ingo K Mellinghoff; Michael Weller; Susan Chang; David Arons; Clair Meehan; Wendy Selig; Kirk Tanner; W K Alfred Yung; Martin van den Bent; Patrick Y Wen; Timothy F Cloughesy
Journal:  Neuro Oncol       Date:  2022-08-01       Impact factor: 13.029

Review 4.  Surveillance imaging frequency in adult patients with lower-grade (WHO Grade 2 and 3) gliomas.

Authors:  Jasmin Jo; Martin J van den Bent; Burt Nabors; Patrick Y Wen; David Schiff
Journal:  Neuro Oncol       Date:  2022-07-01       Impact factor: 13.029

Review 5.  Isocitrate Dehydrogenase Mutant Grade II and III Glial Neoplasms.

Authors:  Ingo K Mellinghoff; Susan M Chang; Kurt A Jaeckle; Martin van den Bent
Journal:  Hematol Oncol Clin North Am       Date:  2021-10-25       Impact factor: 2.861

6.  Real-time PACS-integrated longitudinal brain metastasis tracking tool provides comprehensive assessment of treatment response to radiosurgery.

Authors:  Gabriel Cassinelli Petersen; Khaled Bousabarah; Tej Verma; Marc von Reppert; Leon Jekel; Ayyuce Gordem; Benjamin Jang; Sara Merkaj; Sandra Abi Fadel; Randy Owens; Antonio Omuro; Veronica Chiang; Ichiro Ikuta; MingDe Lin; Mariam S Aboian
Journal:  Neurooncol Adv       Date:  2022-07-26

7.  AI-Driven Image Analysis in Central Nervous System Tumors-Traditional Machine Learning, Deep Learning and Hybrid Models.

Authors:  A V Krauze; Y Zhuge; R Zhao; E Tasci; K Camphausen
Journal:  J Biotechnol Biomed       Date:  2022-01-10
  7 in total

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