ArticleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference2023
Automatic Visual Acuity Loss Prediction in Children with Optic Pathway Gliomas using Magnetic Resonance Imaging.
Article in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 6 citations in OpenAlex.
- Pediatric Optic Pathway Gliomas: Diagnosis, Management, and Outcomes.International ophthalmology clinics · 2026Review
- Adaptable Segmentation Pipeline for Diverse Brain Tumors with Radiomic-guided Subtyping and Lesion-Wise Model Ensemble.Segmentation, classification, and synthesis for brain tumors and traumatic brain injuries : MICCAI 2025 Challenges: BraTS-Lighthouse 2025 and AIMS-TBI 2025, held in conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025,... · 2026Article
- Improving Pre-trained Adult Glioma Segmentation Models using only Post-processing Techniques.Segmentation, classification, and synthesis for brain tumors and traumatic brain injuries : MICCAI 2025 Challenges: BraTS-Lighthouse 2025 and AIMS-TBI 2025, held in conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025,... · 2026Article
- Consensus on Malignant and Benign Tumors in Pediatric Patients with Neurofibromatosis Type 1: On Behalf of the Brazilian Society of Pediatric Oncology (SOBOPE).Current oncology (Toronto, Ont.) · 2025Article
- MRI-based, three-dimensionally assessed tumor burden and growth velocity to predict visual acuity deterioration in optic pathway glioma - results of a retrospective longitudinal analysis.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2025Article
- ENHANCING GENERALIZABILITY IN BRAIN TUMOR SEGMENTATION: MODEL ENSEMBLE WITH ADAPTIVE POST-PROCESSING.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
- Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance Imaging.Brain tumor segmentation, and cross-modality domain adaptation for medical image segmentation : MICCAI challenges, BraTS 2023 and CrossMoDA 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12 and 8, 2024 : proc... · 2024Article
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
Abstract
Children with optic pathway gliomas (OPGs), a low-grade brain tumor associated with neurofibromatosis type 1 (NF1-OPG), are at risk for permanent vision loss. While OPG size has been associated with vision loss, it is unclear how changes in size, shape, and imaging features of OPGs are associated with the likelihood of vision loss. This paper presents a fully automatic framework for accurate prediction of visual acuity loss using multi-sequence magnetic resonance images (MRIs). Our proposed framework includes a transformer-based segmentation network using transfer learning, statistical analysis of radiomic features, and a machine learning method for predicting vision loss. Our segmentation network was evaluated on multi-sequence MRIs acquired from 75 pediatric subjects with NF1-OPG and obtained an average Dice similarity coefficient of 0.791. The ability to predict vision loss was evaluated on a subset of 25 subjects with ground truth using cross-validation and achieved an average accuracy of 0.8. Analyzing multiple MRI features appear to be good indicators of vision loss, potentially permitting early treatment decisions.Clinical relevance- Accurately determining which children with NF1-OPGs are at risk and hence require preventive treatment before vision loss remains challenging, towards this we present a fully automatic deep learning-based framework for vision outcome prediction, potentially permitting early treatment decisions.
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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.