ArticleAJNR. American journal of neuroradiology2015
Parametric Response Mapping of Apparent Diffusion Coefficient as an Imaging Biomarker to Distinguish Pseudoprogression from True Tumor Progression in Peptide-Based Vaccine Therapy for Pediatric Diffuse Intrinsic Pontine Glioma.
Article in AJNR. American journal of neuroradiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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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
27 citing papers in PubMed, 48 citations in OpenAlex.
- Advanced ADC Histogram, Perfusion, and Permeability Metrics Show an Association with Survival and Pseudoprogression in Newly Diagnosed Diffuse Intrinsic Pontine Glioma: A Report from the Pediatric Brain Tumor Consortium.AJNR. American journal of neuroradiology · 2020Trial
- Treatment-induced lesions in newly diagnosed glioblastoma patients undergoing chemoradiotherapy and heat-shock protein vaccine therapy.Journal of neuro-oncology · 2020Trial
- Quantifying radiation therapy response using apparent diffusion coefficient (ADC) parametric mapping of pediatric diffuse intrinsic pontine glioma: a report from the pediatric brain tumor consortium.Journal of neuro-oncology · 2019Trial
- Peptide Vaccines for Pediatric High-Grade Glioma and Diffuse Midline Glioma: Current Progress and Future Perspectives.Vaccines · 2025Review
- Diffusion and contrast-enhancement MRI phenotypes associated with immune checkpoint inhibitor exposure and with immune cell infiltration in brain metastases.Neuro-oncology · 2025Article
- Post-chemo-radiotherapy response and pseudo-progression evaluation on glioma cell types by multi-parametric magnetic resonance imaging: a prospective study.BMC medical imaging · 2023Article
- Differentiating tumour progression from pseudoprogression in glioblastoma patients: a monoexponential, biexponential, and stretched-exponential model-based DWI study.BMC medical imaging · 2023Article
- Article
- Establishing Imaging Biomarkers of Host Immune System Efficacy during Glioblastoma Therapy Response: Challenges, Obstacles and Future Perspectives.Metabolites · 2022Article
- The current landscape of immunotherapy for pediatric brain tumors.Nature cancer · 2022Review
- Conventional and Advanced Imaging Techniques in Post-treatment Glioma Imaging.Frontiers in radiology · 2022Review
- Physiological Imaging Methods for Evaluating Response to Immunotherapies in Glioblastomas.International journal of molecular sciences · 2021Review
- A Position Statement on the Utility of Interval Imaging in Standard of Care Brain Tumour Management: Defining the Evidence Gap and Opportunities for Future Research.Frontiers in oncology · 2021Review
- Advanced Imaging Techniques for Differentiating Pseudoprogression and Tumor Recurrence After Immunotherapy for Glioblastoma.Frontiers in immunology · 2021Review
- Centrally Reduced Diffusion Sign for Differentiation between Treatment-Related Lesions and Glioma Progression: A Validation Study.AJNR. American journal of neuroradiology · 2020Article
- Restriction Spectrum Imaging Differentiates True Tumor Progression From Immune-Mediated Pseudoprogression: Case Report of a Patient With Glioblastoma.Frontiers in oncology · 2020Article
- Machine learning and glioma imaging biomarkers.Clinical radiology · 2020Review
- Review
- DWI and DCE-MRI approaches for differentiating reversibly electroporated penumbra from irreversibly electroporated ablation zones in a rabbit liver model.American journal of cancer research · 2019Article
- Current and Future Imaging Methods for Evaluating Response to Immunotherapy in Neuro-Oncology.Theranostics · 2019Review
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
Abstract
background and purposeImmune response to cancer therapy may result in pseudoprogression, which can only be identified retrospectively and may disrupt an effective therapy. This study assesses whether serial parametric response mapping (a voxel-by-voxel method of image analysis also known as functional diffusion mapping) analysis of ADC measurements following peptide-based vaccination may help prospectively distinguish progression from pseudoprogression in pediatric patients with diffuse intrinsic pontine gliomas. MATERIALS AND
methodsFrom 2009 to 2012, 21 children, 4-18 years of age, with diffuse intrinsic pontine gliomas were enrolled in a serial peptide-based vaccination protocol following radiation therapy. DWI was acquired before immunotherapy and at 6-week intervals during vaccine treatment. Pseudoprogression was identified retrospectively on the basis of clinical and radiographic findings, excluding DWI. Parametric response mapping was used to analyze 96 scans, comparing ADC measures at multiple time points (from the first vaccine to up to 12 weeks after the vaccine was halted) with prevaccine baseline values. Log-transformed fractional increased ADC, fractional decreased ADC, and parametric response mapping ratio (fractional increased ADC/fractional decreased ADC) were compared between patients with and without pseudoprogression, by using generalized estimating equations with inverse weighting by cluster size.
resultsMedian survival was 13.1 months from diagnosis (range, 6.4-24.9 months). Four of 21 children (19%) were assessed as experiencing pseudoprogression. Patients with pseudoprogression had higher fitted average log-transformed parametric response mapping ratios (P = .01) and fractional decreased ADCs (P = .0004), compared with patients without pseudoprogression.
conclusionsSerial parametric response mapping of ADC, performed at multiple time points of therapy, may distinguish pseudoprogression from true progression in patients with diffuse intrinsic pontine gliomas treated with peptide-based vaccination.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.