Literature DB >> 31902040

Adding DSC PWI and DWI to BT-RADS can help identify postoperative recurrence in patients with high-grade gliomas.

Yuelong Yang1, Yunjun Yang1, Xiaoling Wu1, Yi Pan2, Dong Zhou3, Hongdan Zhang2, Yonglu Chen1, Jiayun Zhao1, Zihua Mo1, Biao Huang4.   

Abstract

BACKGROUND: The Brain Tumor Reporting and Data System (BT-RADS) category 3 is suitable for identifying cases with intermediate probability of tumor recurrence that do not meet the Response Assessment in Neuro-Oncology (RANO) criteria for progression. The aim of this study was to evaluate the added value of dynamic susceptibility contrast-enhanced perfusion-weighted imaging (DSC PWI) and diffusion-weighted imaging (DWI) to BT-RADS for differentiating tumor recurrence from non-recurrence in postoperative high-grade glioma (HGG) patients with category 3 lesions.
METHODS: Patients with BT-RADS category 3 lesions were included. The maximal relative cerebral blood volume (rCBVmax) and the mean apparent diffusion coefficient (ADCmean) values were measured. The added value of DSC PWI and DWI to BT-RADS was evaluated by receiver operating characteristic (ROC) curve analysis.
RESULTS: Fifty-one of 91 patients had tumor recurrence, and 40 patients did not. There were significant differences in rCBVmax and ADCmean between the tumor recurrence group and non-recurrence group. Compared to BT-RADS alone, the addition of DSC PWI to BT-RADS increased the area under curve (AUC) from 0.76 (95% confidence interval [CI] 0.66-0.84) to 0.90 (95% CI 0.81-0.95) for differentiating tumor recurrence from non-recurrence. The addition of DWI to BT-RADS increased the AUC from 0.76 (95% CI 0.66-0.84) to 0.88 (95% CI 0.80-0.94). The combination of BT-RADS, DSC PWI, and DWI exhibited the best diagnostic performance (AUC = 0.95; 95% CI 0.88-0.98) for differentiating tumor recurrence from non-recurrence.
CONCLUSION: Adding DSC PWI and DWI to BT-RADS can significantly improve the diagnostic performance for differentiating tumor recurrence from non-recurrence in BT-RADS category 3 lesions.

Entities:  

Keywords:  Diffusion magnetic resonance imaging; High-grade glioma; Perfusion magnetic resonance imaging; Recurrence; Structured template

Mesh:

Year:  2020        PMID: 31902040     DOI: 10.1007/s11060-019-03387-6

Source DB:  PubMed          Journal:  J Neurooncol        ISSN: 0167-594X            Impact factor:   4.130


  25 in total

1.  Differentiation of true progression from pseudoprogression in glioblastoma treated with radiation therapy and concomitant temozolomide: comparison study of standard and high-b-value diffusion-weighted imaging.

Authors:  Hee Ho Chu; Seung Hong Choi; Inseon Ryoo; Soo Chin Kim; Jeong A Yeom; Hwaseon Shin; Seung Chai Jung; A Leum Lee; Tae Jin Yoon; Tae Min Kim; Se-Hoon Lee; Chul-Kee Park; Ji-Hoon Kim; Chul-Ho Sohn; Sung-Hye Park; Il Han Kim
Journal:  Radiology       Date:  2013-10-28       Impact factor: 11.105

2.  Heterogeneity of glioblastoma with gliomatosis cerebri growth pattern on diffusion and perfusion MRI.

Authors:  Alex Förster; Stefanie Brehmer; Marcel Seiz-Rosenhagen; Iris Mildenberger; Frank A Giordano; Holger Wenz; David Reuss; Daniel Hänggi; Christoph Groden
Journal:  J Neurooncol       Date:  2018-12-18       Impact factor: 4.130

3.  Long-term control and partial remission after initial pseudoprogression of glioblastoma by anti-PD-1 treatment with nivolumab.

Authors:  Patrick Roth; Antonios Valavanis; Michael Weller
Journal:  Neuro Oncol       Date:  2017-03-01       Impact factor: 12.300

4.  Management-Based Structured Reporting of Posttreatment Glioma Response With the Brain Tumor Reporting and Data System.

Authors:  Brent D Weinberg; Ashwani Gore; Hui-Kuo G Shu; Jeffrey J Olson; Richard Duszak; Alfredo D Voloschin; Michael J Hoch
Journal:  J Am Coll Radiol       Date:  2018-03-02       Impact factor: 5.532

5.  Quantitative Improvement in Brain Tumor MRI Through Structured Reporting (BT-RADS).

Authors:  James Y Zhang; Brent D Weinberg; Ranliang Hu; Amit Saindane; Mark Mullins; Jason Allen; Michael J Hoch
Journal:  Acad Radiol       Date:  2019-08-27       Impact factor: 3.173

Review 6.  Treatment-related changes in glioblastoma: a review on the controversies in response assessment criteria and the concepts of true progression, pseudoprogression, pseudoresponse and radionecrosis.

Authors:  P D Delgado-López; E Riñones-Mena; E M Corrales-García
Journal:  Clin Transl Oncol       Date:  2017-12-07       Impact factor: 3.405

7.  Differentiating Tumor Progression from Pseudoprogression in Patients with Glioblastomas Using Diffusion Tensor Imaging and Dynamic Susceptibility Contrast MRI.

Authors:  S Wang; M Martinez-Lage; Y Sakai; S Chawla; S G Kim; M Alonso-Basanta; R A Lustig; S Brem; S Mohan; R L Wolf; A Desai; H Poptani
Journal:  AJNR Am J Neuroradiol       Date:  2015-10-08       Impact factor: 3.825

Review 8.  Radiation Necrosis, Pseudoprogression, Pseudoresponse, and Tumor Recurrence: Imaging Challenges for the Evaluation of Treated Gliomas.

Authors:  Anastasia Zikou; Chrissa Sioka; George A Alexiou; Andreas Fotopoulos; Spyridon Voulgaris; Maria I Argyropoulou
Journal:  Contrast Media Mol Imaging       Date:  2018-12-02       Impact factor: 3.161

9.  Post-operative perfusion and diffusion MR imaging and tumor progression in high-grade gliomas.

Authors:  Matthew L White; Yan Zhang; Fang Yu; Nicole Shonka; Michele R Aizenberg; Pavani Adapa; Syed A Jaffar Kazmi
Journal:  PLoS One       Date:  2019-03-18       Impact factor: 3.240

10.  Treatment-related brain tumor imaging changes: So-called "pseudoprogression" vs. tumor progression: Review and future research opportunities.

Authors:  Diem Kieu Thi Tran; Randy L Jensen
Journal:  Surg Neurol Int       Date:  2013-04-17
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  1 in total

Review 1.  Diffusion imaging could aid to differentiate between glioma progression and treatment-related abnormalities: a meta-analysis.

Authors:  Rik van den Elshout; Tom W J Scheenen; Chantal M L Driessen; Robert J Smeenk; Frederick J A Meijer; Dylan Henssen
Journal:  Insights Imaging       Date:  2022-10-04
  1 in total

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