Characterization of pediatric brain tumors using pre-diagnostic neuroimaging - Green, Shannon; Vuong, Victoria D.; Khanna, Paritosh C.; Crawford, John R. (Frontiers in Oncology, 2022)
Purpose To evaluate for predictive neuroimaging features of pediatric brain tumor development and quantify tumor growth characteristics in patients who had neuroimaging performed prior to a diagnosis of a brain tumor. Methods Retrospective review of 1098 consecutive pediatric patients at a single institution with newly diagnosed brain tumors from January 2009 to October 2021 was performed to identify patients with neuroimaging prior to the diagnosis of a brain tumor. Pre-diagnostic and diagnostic neuroimaging features (e.g., tumor size, apparent diffusion coefficient (ADC) values), clinical presentations, and neuropathology were recorded in those patients who had neuroimaging performed prior to a brain tumor diagnosis. High- and low-grade tumor sizes were fit to linear and exponential growth regression models. Results Fourteen of 1098 patients (1%) had neuroimaging prior to diagnosis of a brain tumor (8 females, mean age at definitive diagnosis 8.1 years, imaging interval 0.2-8.7 years). Tumor types included low-grade glioma (n = 4), embryonal tumors (n = 2), pineal tumors (n=2), ependymoma (n = 3), and others (n = 3). Pre-diagnostic imaging of corresponding tumor growth sites were abnormal in four cases (28%) and demonstrated higher ADC values in the region of high-grade tumor growth (p = 0.05). Growth regression analyses demonstrated R2-values of 0.92 and 0.91 using a linear model and 0.64 and 0.89 using an exponential model for high- and low-grade tumors, respectively; estimated minimum velocity of diameter expansion was 2.4 cm/year for high-grade and 0.4 cm/year for low-grade tumors. High-grade tumors demonstrated faster growth rate of diameter and solid tumor volume compared to low-grade tumors (p = 0.02, p = 0.03, respectively). Conclusions This is the first study to test feasibility in utilizing pre-diagnostic neuroimaging to demonstrate that linear and exponential growth rate models can be used to estimate pediatric brain tumor growth velocity and should be validated in a larger multi-institutional cohort.