Abstract

BACKGROUND

NF1-OPGs are amorphous tumors involving either single or multiple locations (optic nerve, chiasm, tract) along the anterior visual pathway (AVP). In this prospective study, we investigated how volumetric MRI measures of the AVP as well as other clinical variables are associated with treatment decisions in children with newly diagnosed NF1-OPGs.

METHODS

Children with newly diagnosed NF1-OPG whose MRI included a T1-weighted volumetric sequence without significant artifact at their enrollment visit were eligible for inclusion. All subjects underwent a quantitative ophthalmic exam to determine if visual acuity (VA) was normal. The neuro-oncologist/NF1 expert determined whether the subject would be observed or undergo treatment at that baseline visit. Volumetric MRI analysis was automatically performed using a deep learning network that measured AVP volume (mm3). Non-parametric group comparisons and multivariable logistic regression models evaluated the impact of age at enrollment, sex, NF1 inheritance type, AVP volume, and VA on the decision for immediate treatment with chemotherapy versus observation.

RESULTS

One-hundred twenty-three subjects met inclusion criteria. Subjects assigned to observation (N=112, 44% female) and subjects immediately treated with chemotherapy (N=11, 80% female) at enrollment were of similar age (2.7 and 2.8 years, respectively) and inheritance (p > 0.05). Abnormal VA was present more often in the treatment group (46%) compared to the observation group (17%, p <0.001). AVP volume was significantly greater in the treatment group (4,181.1mm3) compared to the observation group (1,819.8mm3, p <0.001). AVP volume, sex, and VA reached significance in univariable regression, however, in the multivariable regression model only the AVP volume (p < 0.001) was significantly associated with treatment initiation.

DISCUSSION

Children with greater NF1-OPG AVP volumes are treated more often compared with those with lower volumes. Volumetric measures of NF1-OPGs are a valuable metric in understanding treatment patterns and are positioned to help inform clinical decision making.

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