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Lundy P, Domino J, Ryken T, Fouke S, McCracken DJ, Ormond DR, Olson JJ. The role of imaging for the management of newly diagnosed glioblastoma in adults: a systematic review and evidence-based clinical practice guideline update. J Neurooncol 2020; 150:95-120. [DOI: 10.1007/s11060-020-03597-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 08/08/2020] [Indexed: 12/11/2022]
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Predicting Survival in Glioblastoma Patients Using Diffusion MR Imaging Metrics-A Systematic Review. Cancers (Basel) 2020; 12:cancers12102858. [PMID: 33020420 PMCID: PMC7600641 DOI: 10.3390/cancers12102858] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Revised: 09/28/2020] [Accepted: 10/01/2020] [Indexed: 12/20/2022] Open
Abstract
Simple Summary An accurate survival analysis is crucial for disease management in glioblastoma (GBM) patients. Due to the ability of the diffusion MRI techniques of providing a quantitative assessment of GBM tumours, an ever-growing number of studies aimed at investigating the role of diffusion MRI metrics in survival prediction of GBM patients. Since the role of diffusion MRI in prediction and evaluation of survival outcomes has not been fully addressed and results are often controversial or unsatisfactory, we performed this systematic review in order to collect, summarize and evaluate all studies evaluating the role of diffusion MRI metrics in predicting survival in GBM patients. We found that quantitative diffusion MRI metrics provide useful information for predicting survival outcomes in GBM patients, mainly in combination with other clinical and multimodality imaging parameters. Abstract Despite advances in surgical and medical treatment of glioblastoma (GBM), the medium survival is about 15 months and varies significantly, with occasional longer survivors and individuals whose tumours show a significant response to therapy with respect to others. Diffusion MRI can provide a quantitative assessment of the intratumoral heterogeneity of GBM infiltration, which is of clinical significance for targeted surgery and therapy, and aimed at improving GBM patient survival. So, the aim of this systematic review is to assess the role of diffusion MRI metrics in predicting survival of patients with GBM. According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, a systematic literature search was performed to identify original articles since 2010 that evaluated the association of diffusion MRI metrics with overall survival (OS) and progression-free survival (PFS). The quality of the included studies was evaluated using the QUIPS tool. A total of 52 articles were selected. The most examined metrics were associated with the standard Diffusion Weighted Imaging (DWI) (34 studies) and Diffusion Tensor Imaging (DTI) models (17 studies). Our findings showed that quantitative diffusion MRI metrics provide useful information for predicting survival outcomes in GBM patients, mainly in combination with other clinical and multimodality imaging parameters.
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Qin L, Li A, Qu J, Reinshagen K, Li X, Cheng SC, Bryant A, Young GS. Normalization of ADC does not improve correlation with overall survival in patients with high-grade glioma (HGG). J Neurooncol 2018; 137:313-319. [PMID: 29383647 DOI: 10.1007/s11060-017-2719-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2017] [Accepted: 12/13/2017] [Indexed: 12/29/2022]
Abstract
Mixed reports leave uncertainty about whether normalization of apparent diffusion coefficient (ADC) to a within-subject white matter reference is necessary for assessment of tumor cellularity. We tested whether normalization improves the previously reported correlation of resection margin ADC with 15-month overall survival (OS) in HGG patients. Spin-echo echo-planar DWI was retrieved from 3 T MRI acquired between maximal resection and radiation in 37 adults with new-onset HGG (25 glioblastoma; 12 anaplastic astrocytoma). ADC maps were produced with the FSL DTIFIT tool (Oxford Centre for Functional MRI). 3 neuroradiologists manually selected regions of interest (ROI) in normal appearing white matter (NAWM) and in non-enhancing tumor (NT) < 2 cm from the margin of residual enhancing tumor or resection cavity. Normalized ADC (nADC) was computed as the ratio of absolute NT ADC to NAWM ADC. Reproducibility of nADC and absolute ADC among the readers' ROI was assessed using intra-class correlation coefficient (ICC) and within-subject coefficient of variation (wCV). Correlations of ADC and nADC with OS were compared using receiver operating characteristics (ROC) analysis. A p value 0.05 was considered statistically significant. Both mean ADC and nADC differed significantly between patients subgrouped by 15-month OS (p = 0.0014 and 0.0073 respectively). wCV and ICC among the readers were similar for absolute and normalized ADC. In ROC analysis of correlation with OS, nADC did not perform significantly better than absolute ADC. Normalization does not significantly improve the correlation of absolute ADC with OS in HGG, suggesting that normalization is not necessary for clinical or research ADC analysis in HGG patients.
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Affiliation(s)
- Lei Qin
- Department of Imaging, Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Angie Li
- Department of Radiology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA.,The Robert Larner, M.D. College of Medicine, University of Vermont, Burlington, VT, USA
| | - Jinrong Qu
- Department of Radiology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA.,Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Katherine Reinshagen
- Department of Radiology, Harvard Medical School, Boston, MA, USA.,Department of Radiology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA.,Department of Radiology, Massachusetts Eye and Ear Infirmary, Boston, MA, USA
| | - Xiang Li
- Department of Radiology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA.,Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Su-Chun Cheng
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Annie Bryant
- Department of Imaging, Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Behavioral Neuroscience, Northeastern University, Boston, MA, USA
| | - Geoffrey S Young
- Department of Imaging, Dana-Farber Cancer Institute, Boston, MA, USA. .,Department of Radiology, Harvard Medical School, Boston, MA, USA. .,Department of Radiology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA.
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