Evidence map›Paper›PMID 39987704›Full record

ArticleClinical neurology and neurosurgery2025

A novel predictive model utilizing retinal microstructural features for estimating survival outcome in patients with glioblastoma.

Rebekah Smith, Ranjit Sapkota, Bhavna Antony, Jinger Sun, Orwa Aboud, Orin Bloch, Megan Daly, Ruben Fragoso, Glenn Yiu, Yin Allison Liu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Clinical neurology and neurosurgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Rebekah SmithSchool of Medicine, University of California, Davis, USA.
Ranjit SapkotaInstitute of Innovation, Science & Sustainability, Federation University Australia, Mt Helen, Australia.
Bhavna AntonyInstitute of Innovation, Science & Sustainability, Federation University Australia, Mt Helen, Australia.
Jinger SunDepartment of Radiation Oncology, University of California, Davis, USA.
Orwa AboudDepartment of Neurology, University of California, Davis, USA; Comprehensive Cancer Center, University of California, Davis, USA; Department of Neurological Surgery, University of California, Davis, USA.
Orin BlochComprehensive Cancer Center, University of California, Davis, USA; Department of Neurological Surgery, University of California, Davis, USA.
Megan DalyDepartment of Radiation Oncology, University of California, Davis, USA; Comprehensive Cancer Center, University of California, Davis, USA.
Ruben FragosoDepartment of Radiation Oncology, University of California, Davis, USA; Comprehensive Cancer Center, University of California, Davis, USA.
Glenn YiuDepartment of Ophthalmology & Vision Science, University of California, Davis, USA.
Yin Allison LiuDepartment of Neurology, University of California, Davis, USA; Department of Neurological Surgery, University of California, Davis, USA; Department of Ophthalmology & Vision Science, University of California, Davis, USA. Electronic address: aycliu@ucdavis.edu.

Funding

UC Davis Paul Calabresi K12 Clinical Oncology Research Career Development ProgramK12CA138464 · NCI · UNIVERSITY OF CALIFORNIA AT DAVIS · PI BIRKELAND, ANDREW CHARLES, LARA, PRIMO N. · 2011 to 2025
$11.4M
NCI NIH HHS K12 CA138464
6 · The paper itself

Abstract

purposeGlioblastoma is a highly aggressive brain tumor with poor prognosis despite surgery and chemoradiation. The visual sequelae of glioblastoma have not been well characterized. This study assessed visual outcomes in glioblastoma patients through neuro-ophthalmic exams, imaging of the retinal microstructures/microvasculature, and perimetry.

methodsA total of 19 patients with glioblastoma (9 male, 10 female, average age at diagnosis 69 years) were enrolled. Tumor characteristic, neuro-ophthalmic exam data, Optical Coherence Tomography (OCT) and OCT-Angiography data of all patient eyes were analyzed using Microsoft Excel and a Machine Learning algorithm.

resultsBest-corrected visual acuity ranged from 20/20 - 20/50. Occipital tumors showed worse visual fields than frontal tumors (mean deviation -14.9 and -0.23, respectively, p < 0.0001). Those with overall survival (OS)< 15 months demonstrated thinner retinal nerve fiber layer and ganglion cell complex (p < 0.0001) and enlarged foveal avascular zone starting from 4 months post-diagnosis (p = 0.006). There was no significant difference between eyes ipsilateral and contralateral to radiation fields (average doses were 1370 cGy and 1180 cGy, respectively, p = 0.42). A machine learning algorithm using retinal microstructure and visual fields predicted patients with long (≥15 months) progression-free and overall survival with 78 % accuracy.

conclusionGlioblastoma patients frequently present with visual field defects despite normal visual acuity. Patients with poor survival duration demonstrated significant retinal thinning and decreased microvascular density. A machine learning algorithm predicted survival though further validation is warranted.

Indexed as

Brain NeoplasmsGlioblastomaRetinaAdultAgedAged, 80 and overFemaleHumansMachine LearningMaleMiddle AgedTomography, Optical CoherenceVisual AcuityVisual FieldsGlioblastomaMachine learningRetinaSurvival prediction

Identifiers

PMID39987704
PMCPMC11911018

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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.