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Cited by 8Brain Tumor Detection and ClassificationRadiomics and Machine Learning in Medical ImagingMedical Image Segmentation Techniques

Automatic Visual Acuity Loss Prediction in Children with Optic Pathway Gliomas using Magnetic Resonance Imaging

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Jiang, Zhifan, Parida, Abhijeet, Anwar, Syed Muhammad, Tang, Yucheng, Roth, Holger R., Fisher, Michael J., Packer, Roger J., Avery, Robert A. +1 more·2023·2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
DOI 10.1109/embc40787.2023.10339961PMC PMC11283911
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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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  • Although a few previous works addressed pediatric brain tumors, their automatic segmentation remains challenging due to a lack of training data and variation in heterogeneous histologic sub-regions.

    “Although, a few previous works addressed pediatric brain tumors [10,14,20], their automatic segmentation remains challenging. This is due to a lack of training data and variation in heterogeneous histologic sub-regions including peritumoral edematous/invaded tissue, necrotic core, and enhancing tumor.”

    From Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance Imaging (2024) · cites this paper as [14]

    The Introduction says automatic segmentation of the anterior visual pathway in children with optic pathway gliomas is difficult and that obtaining sufficient training data for rare pediatric brain tumors is challenging. The Methods list necrotic, peritumoral edematous, and enhancing regions as labels in an adult glioblastoma dataset; the paper does not say that variation among those regions causes the pediatric segmentation difficulty. (LLM, conf=0.87)

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Cites

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    MethodSelf-citation

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Research works printed but not linked: 23

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Cited by

Sorted by citations

Authors
Jiang, Z., Parida, A., Anwar, S. M., Tang, Y., Roth, H. R., Fisher, M. J., Packer, R. J., Avery, R. A., & Linguraru, M. G.
Affiliations
  • Children's National (US)
  • Nvidia (United States) (US)
  • Children's Hospital of Philadelphia (US)
ORCID
Venue
2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Publisher
IEEE
Published
2023-07-24
Volume
2023
Pages
1-5
ISSN
2375-7477 (ISSN-L)
DOI
10.1109/embc40787.2023.10339961
PMC
PMC11283911
OpenAlex
W4389543157
Cited by
8
References
1
Citations by year
2026: 2 · 2025: 3 · 2024: 3
FWCI
2.10
Type
conference paper
Language
en
Open access
Licence
Locations
  • Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
National Cancer Institute
Topics
  • Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Keywords
optic pathway glioma, neurofibromatosis type 1, vision loss risk, pediatric brain tumors, MRI segmentation, transformer-based segmentation, radiomic features, multi-sequence MRI
MeSH terms
Child, Humans, Magnetic Resonance Imaging, Neurofibromatosis 1, Vision Disorders, Visual Acuity, Optic Nerve Glioma

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