ArticleNeuro-oncology2022
Deep learning-based automatic tumor burden assessment of pediatric high-grade gliomas, medulloblastomas, and other leptomeningeal seeding tumors.
Article in Neuro-oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 39 papers.
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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.
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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
39 citing papers in PubMed, 73 citations in OpenAlex.
- A case-control study of MRI features in pediatric atypical teratoid rhabdoid tumor and medulloblastoma.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2026Article
- Dual-task deep learning model for prediction of medulloblastoma molecular subgroups with preoperative brain MRI.La Radiologia medica · 2026Article
- Ultra-High Resolution 9.4T Brain MRI Segmentation via a Newly Engineered Multi-Scale Residual Nested U-Net with Gated Attention.Bioengineering (Basel, Switzerland) · 2025Article
- AI-Driven Innovations in Neuroradiology and Neurosurgery: Scoping Review of Current Evidence and Future Directions.Cancers · 2025Review
- MWG-UNet++: Hybrid Transformer U-Net Model for Brain Tumor Segmentation in MRI Scans.Bioengineering (Basel, Switzerland) · 2025Article
- A review of deep learning for brain tumor analysis in MRI.NPJ precision oncology · 2025Review
- Precise diagnosis of pediatric posterior cranial fossa neoplasms based on 2.5D MRI deep learning.Frontiers in oncology · 2025Article
- Segmentation of Low-Grade Brain Tumors Using Mutual Attention Multimodal MRI.Sensors (Basel, Switzerland) · 2024Article
- Active Learning in Brain Tumor Segmentation with Uncertainty Sampling and Annotation Redundancy Restriction.Journal of imaging informatics in medicine · 2024Article
- Towards consistency in pediatric brain tumor measurements: Challenges, solutions, and the role of artificial intelligence-based segmentation.Neuro-oncology · 2024Review
- Advancing Pediatric Neuro-Oncology: Multi-institutional nnU-Net Segmentation of Medulloblastoma.Radiology. Artificial intelligence · 2024Article
- nnU-Net-based Segmentation of Tumor Subcompartments in Pediatric Medulloblastoma Using Multiparametric MRI: A Multi-institutional Study.Radiology. Artificial intelligence · 2024Article
- Artificial intelligence innovations in neurosurgical oncology: a narrative review.Journal of neuro-oncology · 2024Review
- Training and Comparison of nnU-Net and DeepMedic Methods for Autosegmentation of Pediatric Brain Tumors.AJNR. American journal of neuroradiology · 2024Article
- Radiomics and artificial intelligence applications in pediatric brain tumors.World journal of pediatrics : WJP · 2024Review
- Applications of Artificial Intelligence for Pediatric Cancer Imaging.AJR. American journal of roentgenology · 2024Review
- Stepwise Transfer Learning for Expert-level Pediatric Brain Tumor MRI Segmentation in a Limited Data Scenario.Radiology. Artificial intelligence · 2024Article
- Article
- NRG Oncology Assessment of Artificial Intelligence Deep Learning-Based Auto-segmentation for Radiation Therapy: Current Developments, Clinical Considerations, and Future Directions.International journal of radiation oncology, biology, physics · 2024Article
- Updated Response Assessment in Neuro-Oncology (RANO) for Gliomas.Current neurology and neuroscience reports · 2024Review
Corrections and comments
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- Erratum issued
Authors and funding
25 authors at 10 institutions in 2 countries.
Funding
Abstract
backgroundLongitudinal measurement of tumor burden with magnetic resonance imaging (MRI) is an essential component of response assessment in pediatric brain tumors. We developed a fully automated pipeline for the segmentation of tumors in pediatric high-grade gliomas, medulloblastomas, and leptomeningeal seeding tumors. We further developed an algorithm for automatic 2D and volumetric size measurement of tumors.
methodsThe preoperative and postoperative cohorts were randomly split into training and testing sets in a 4:1 ratio. A 3D U-Net neural network was trained to automatically segment the tumor on T1 contrast-enhanced and T2/FLAIR images. The product of the maximum bidimensional diameters according to the RAPNO (Response Assessment in Pediatric Neuro-Oncology) criteria (AutoRAPNO) was determined. Performance was compared to that of 2 expert human raters who performed assessments independently. Volumetric measurements of predicted and expert segmentations were computationally derived and compared.
resultsA total of 794 preoperative MRIs from 794 patients and 1003 postoperative MRIs from 122 patients were included. There was excellent agreement of volumes between preoperative and postoperative predicted and manual segmentations, with intraclass correlation coefficients (ICCs) of 0.912 and 0.960 for the 2 preoperative and 0.947 and 0.896 for the 2 postoperative models. There was high agreement between AutoRAPNO scores on predicted segmentations and manually calculated scores based on manual segmentations (Rater 2 ICC = 0.909; Rater 3 ICC = 0.851). Lastly, the performance of AutoRAPNO was superior in repeatability to that of human raters for MRIs with multiple lesions.
conclusionsOur automated deep learning pipeline demonstrates potential utility for response assessment in pediatric brain tumors. The tool should be further validated in prospective studies.
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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.