ArticlePloS one2017
Differentiation of pseudoprogression and real progression in glioblastoma using ADC parametric response maps.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Discriminators of pseudoprogression and true progression in high-grade gliomas: A systematic review and meta-analysis.Scientific reports · 2022Pooled it
- Pooled it
- Magnetic Resonance Imaging Sequences and Technologies in Adaptive Radiation Therapy.International journal of radiation oncology, biology, physics · 2025Review
- Validation of daily 0.35 T diffusion-weighted MRI for MRI-guided glioblastoma radiotherapy.Medical physics · 2024Article
- Association between the Anatomical Location of Glioblastoma and Its Evaluation with Clinical Considerations: A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2024Review
- Differentiating tumour progression from pseudoprogression in glioblastoma patients: a monoexponential, biexponential, and stretched-exponential model-based DWI study.BMC medical imaging · 2023Article
- Undetected pseudoprogressions in the CeTeG/NOA-09 trial: hints from postprogression survival and MRI analyses.Journal of neuro-oncology · 2023Article
- Whole body FDG PET/MR for progression free and overall survival prediction in patients with relapsed/refractory large B-cell lymphomas undergoing CAR T-cell therapy.Cancer imaging : the official publication of the International Cancer Imaging Society · 2022Article
- Diffusion imaging could aid to differentiate between glioma progression and treatment-related abnormalities: a meta-analysis.Insights into imaging · 2022Review
- Tumor Progression and Treatment-Related Changes: Radiological Diagnosis Challenges for the Evaluation of Post Treated Glioma.Cancers · 2022Review
- Metabolic and physiologic magnetic resonance imaging in distinguishing true progression from pseudoprogression in patients with glioblastoma.NMR in biomedicine · 2022Review
- Pseudoprogression in Glioblastoma: Role of Metabolic and Functional MRI-Systematic Review.Biomedicines · 2022Review
- Article
- High-Grade Glioma Treatment Response Monitoring Biomarkers: A Position Statement on the Evidence Supporting the Use of Advanced MRI Techniques in the Clinic, and the Latest Bench-to-Bedside Developments. Part 1: Perfusion and Diffusion Techniques.Frontiers in oncology · 2022Review
- Parametric Response Mapping of FLAIR MRI Provides an Early Indication of Progression Risk in Glioblastoma.Academic radiology · 2021Article
- MR-Guided Radiotherapy for Brain and Spine Tumors.Frontiers in oncology · 2021Review
- Discriminating pseudoprogression and true progression in diffuse infiltrating glioma using multi-parametric MRI data through deep learning.Scientific reports · 2020Article
- Differentiation of Treatment-Related Effects from Glioma Recurrence Using Machine Learning Classifiers Based Upon Pre-and Post-Contrast T1WI and T2 FLAIR Subtraction Features: A Two-Center Study.Cancer management and research · 2020Article
- Response Assessment in Neuro-Oncology Criteria for Gliomas: Practical Approach Using Conventional and Advanced Techniques.AJNR. American journal of neuroradiology · 2020Review
- Review
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe purpose of this study was to investigate whether a voxel-wise analysis of apparent diffusion coefficient (ADC) values may differentiate between progressive disease (PD) and pseudoprogression (PsP) in patients with high-grade glioma using the parametric response map, a newly introduced postprocessing tool.
methodsTwenty-eight patients with proven PD and seven patients with PsP were identified in this retrospective feasibility study. For all patients ADC baseline and follow-up maps on four subsequent MRIs were available. ADC maps were coregistered on contrast enhanced T1-weighted follow-up images. Subsequently, enhancement in the follow-up contrast enhanced T1-weighted image was manually delineated and a reference region of interest (ROI) was drawn in the contralateral white matter. Both ROIs were transferred to the ADC images. Relative ADC (rADC) (baseline)/reference ROI values and rADC (follow up)/reference ROI values were calculated for each voxel within the ROI. The corresponding voxels of rADC (follow up) and rADC (baseline) were subtracted and the percentage of all voxels within the ROI that exceeded the threshold of 0.25 was quantified.
resultsrADC voxels showed a decrease of 59.2% (1st quartile (Q1) 36.7; 3rd quartile (Q3) 78.6) above 0.25 in patients with PD and 18.6% (Q1 3.04; Q3 26.5) in patients with PsP (p = 0.005). Receiver operating characteristic curve analysis showed the optimal decreasing rADC cut-off value for identifying PD of > 27.05% (area under the curve 0.844±0.065, sensitivity 0.86, specificity 0.86, p = 0.014).
conclusionThis feasibility study shows that the assessment of rADC using parametric response maps might be a promising approach to contribute to the differentiation between PD and PsP. Further research in larger patient cohorts is necessary to finally determine its clinical utility.
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