A Novel Predictive Model Utilizing Retinal Microstructural Features for Estimating Survival Outcome in Patients with Glioblastoma

TL;DR

This study shows that retinal changes can predict survival outcomes in glioblastoma patients, using machine learning for accurate predictions.

Contribution

A novel machine learning model using retinal microstructural features to estimate survival in glioblastoma patients.

Findings

Patients with poor survival had thinner retinal layers and enlarged foveal avascular zones.

A machine learning model predicted long survival with 78% accuracy using retinal features and visual fields.

Occipital tumors caused worse visual field defects compared to frontal tumors.

Abstract

Glioblastoma 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.

A total of 19 patients (9 male, 10 female, average age at diagnosis 69 years) were enrolled. Best-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.

Glioblastoma 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; further validation is warranted.

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