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PURPOSE: Optic pathway gliomas (OPGs) are diagnosed based on typical MR features and require careful monitoring with serial MRI. Reliable, serial radiological comparison of OPGs is a difficult task, where accuracy becomes very important for clinical decisions on treatment initiation and results. Current radiological methodology usually includes linear measurements that are limited in terms of precision and reproducibility. METHOD: We present a method that enables semiautomated segmentation and internal classification of OPGs using a novel algorithm. Our method begins with co-registration of the different sequences of an MR study so that T1 and T2 slices are realigned. The follow-up studies are then re-sliced according to the baseline study. The baseline tumor is segmented, with internal components classified into solid non-enhancing, solid-enhancing, and cystic components, and the volume is calculated. Tumor demarcation is then transferred onto the next study and the process repeated. Numerical values are correlated with clinical data such as treatment and visual ability. RESULTS: We have retrospectively implemented our method on 24 MR studies of three OPG patients. Clinical case reviews are presented here. The volumetric results have been correlated with clinical data and their implications are also discussed. CONCLUSIONS: The heterogeneity of OPGs, the long course, and the young age of the patients are all driving the demand for more efficient and accurate means of tumor follow-up. This method may allow better understanding of the natural history of the tumor and provide a more advanced means of treatment evaluation.

Original publication




Journal article


Childs Nerv Syst

Publication Date





1265 - 1272


Algorithms, Child, Preschool, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Optic Nerve Glioma