ArticleNeuro-oncology advances
Comparison of volumetric and 2D-based response methods in the PNOC-001 pediatric low-grade glioma clinical trial.
Article in Neuro-oncology advances. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 9 citations in OpenAlex.
- Multidimensional deep learning for grading and prognostic assessment of intrahepatic mass-forming cholangiocarcinoma.Insights into imaging · 2026Article
- Segmenting with confidence through uncertainty quantification for brain tumor imaging.NPJ digital medicine · 2026Article
- AI-powered segmentation and prognosis with missing MRI in pediatric brain tumors.NPJ precision oncology · 2026Article
- Matched MRI, Segmentations, and Histopathologic Images of Brain Metastases from Primary Lung Cancer.Scientific data · 2026Article
- Response Assessment in Neuro-Oncology (RANO) 2009-2025: Broad scope and implementation-A progress report.Neuro-oncology · 2025Review
- The use of minor response, volumetric assessment, and growth rate kinetics as endpoints in grade 1-3 glioma clinical trials: a RANO perspective.Neuro-oncology · 2025Review
- Applications of artificial intelligence and advanced imaging in pediatric diffuse midline glioma.Neuro-oncology · 2025Review
- Enhancing RAPNO: the need for standardized imaging heuristics and volumetric assessment.Neuroradiology · 2025Article
- Enhancing efficiency in pediatric brain tumor segmentation using a pathologically diverse single-center clinical dataset.Neuro-oncology advancesArticle
- Diagnosing growth in low-grade gliomas with and without artificial intelligence-measured longitudinal volume measurements: A retrospective observational study.Neuro-oncology advancesArticle
- Deep learning volumetrics reveal distinct clinical trajectories for pediatric low-grade gliomas under surveillance: A multicenter study.Neuro-oncology advancesArticle
Corrections and comments
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Authors and funding
23 authors at 10 institutions in 3 countries.
Funding
Abstract
Background: Although response in pediatric low-grade glioma (pLGG) includes volumetric assessment, more simplified 2D-based methods are often used in clinical trials. The study's purpose was to compare volumetric to 2D methods. Methods: An expert neuroradiologist performed solid and whole tumor (including cyst and edema) volumetric measurements on MR images using a PACS-based manual segmentation tool in 43 pLGG participants (213 total follow-up images) from the Pacific Pediatric Neuro-Oncology Consortium (PNOC-001) trial. Classification based on changes in volumetric and 2D measurements of solid tumor were compared to neuroradiologist visual response assessment using the Brain Tumor Reporting and Data System (BT-RADS) criteria for a subset of 65 images using receiver operating characteristic (ROC) analysis. Longitudinal modeling of solid tumor volume was used to predict BT-RADS classification in 54 of the 65 images. Results: There was a significant difference in ROC area under the curve between 3D solid tumor volume and 2D area (0.96 vs 0.78, Conclusions: Volumetric analysis of solid tumor was significantly better than 2D measurements in classifying tumor progression as determined by BT-RADS criteria and will enable more comprehensive clinical management.
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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.