ArticleNeuro-oncology advances
Predicting pediatric optic pathway glioma progression using advanced magnetic resonance image analysis and machine learning.
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 12 papers.
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Who cites it
12 citing papers in PubMed, 14 citations in OpenAlex.
- Pediatric Optic Pathway Gliomas: Diagnosis, Management, and Outcomes.International ophthalmology clinics · 2026Review
- Cognition and behavior in neurofibromatosis type 1: report and perspective from the Cognition and Behavior in NF1 (CABIN) Task Force.Genes & development · 2025Article
- Review
- Applications of machine learning to MR imaging of pediatric low-grade gliomas.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2024Review
- Low-grade glioma in children with neurofibromatosis type 1: surveillance, treatment indications, management, and future directions.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2024Review
- Applications of Artificial Intelligence for Pediatric Cancer Imaging.AJR. American journal of roentgenology · 2024Review
- Radiomics and artificial intelligence applications in pediatric brain tumors.World journal of pediatrics : WJP · 2024Review
- Visual Deficits and Diagnostic and Therapeutic Strategies for Neurofibromatosis Type 1: Bridging Science and Patient-Centered Care.Vision (Basel, Switzerland) · 2024Review
- Automatic Visual Acuity Loss Prediction in Children with Optic Pathway Gliomas using Magnetic Resonance Imaging.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2023Article
- A new era for optic pathway glioma: A developmental brain tumor with life-long health consequences.Frontiers in pediatrics · 2023Review
- Harnessing multimodal data integration to advance precision oncology.Nature reviews. Cancer · 2022Review
- Radiomics and radiogenomics in pediatric neuro-oncology: A review.Neuro-oncology advancesReview
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundOptic pathway gliomas (OPGs) are low-grade tumors of the white matter of the visual system with a highly variable clinical course. The aim of the study was to generate a magnetic resonance imaging (MRI)-based predictive model of OPG tumor progression using advanced image analysis and machine learning techniques.
methodsWe performed a retrospective case-control study of OPG patients managed between 2009 and 2015 at an academic children's hospital. Progression was defined as radiographic tumor growth or vision decline. To generate the model, optic nerves were manually highlighted and optic radiations (ORs) were segmented using diffusion tractography tools. For each patient, intensity distributions were obtained from within the segmented regions on all imaging sequences, including derivatives of diffusion tensor imaging (DTI). A machine learning algorithm determined the combination of features most predictive of progression.
resultsNineteen OPG patients with progression were matched to 19 OPG patients without progression. The mean time between most recent follow-up and most recently analyzed MRI was 3.5 ± 1.7 years. Eighty-three MRI studies and 532 extracted features were included. The predictive model achieved an accuracy of 86%, sensitivity of 89%, and specificity of 81%. Fractional anisotropy of the ORs was among the most predictive features (area under the curve 0.83,
conclusionsOur findings show that image analysis and machine learning can be applied to OPGs to generate a MRI-based predictive model with high accuracy. As OPGs grow along the visual pathway, the most predictive features relate to white matter changes as detected by DTI, especially within ORs.
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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.