This study is the first to evaluate pre-diagnostic neuroimaging of pediatric brain tumors. The primary goal of this retrospective study of 14 high- and low-grade pediatric brain tumors was to identify possible predictive neuroimaging characteristics, including growth rate. Evaluation of high-grade and low-grade tumor growth demonstrated an excellent fit using the linear growth rate regression model, with R2 = 0.91 – 0.92 for both tumor subtypes. Moreover, pre-diagnostic imaging allowed specific evaluation of ADC values, which was not significantly different between pre-diagnostic and diagnostic imaging for low-grade tumors and was higher in pre-diagnostic imaging compared to diagnostic imaging for high-grade tumors.
Currently, quantitative studies of pediatric brain tumor growth is lacking. Multiple factors contribute to the sparsity of data on this topic, including a relatively lower incidence of pediatric brain tumors compared to the adult population, increased awareness of radiation safety for the pediatric population, and special considerations for pediatric MRI. Particularly, high-grade tumor growth is difficult to characterize given they are often managed immediately following diagnosis, limiting long-term treatment-naïve observation. However, several studies have assessed the growth characteristics of common high-grade tumors in adults [8–12]. For example, Ellingson et al. found that median volumetric doubling time of preoperative, treatment-naive high-grade gliomas was 21.1 days . In addition, Fan et al. demonstrated an estimated VDE of 7.0 cm/year for glioblastomas and 5.1 cm/year for all high-grade gliomas . Stensjøen et al. reported large variations in glioblastoma growth rates, with a median velocity of radial expansion (VRE) of 3.0 cm/year. The authors also noted that one-third of tumors doubled in volume between the diagnostic and preoperative scans, while another one-third were unchanged or decreased in volume . Wang et al. also reported large variations in glioblastoma growth rates in vivo but demonstrated a VRE of 3.0 cm/year .
In this study, the estimated VDE was 2.4 cm/year, lower than previously reported values in the adult population. Given the exact time of initial tumor development is unknown, this calculated VDE is likely lower than the true VDE, partially accounting for the relatively slower growth rates compared to previously reported data. Another possible explanation is that the true growth rate model instead mimics the Gompertzian growth curve. If so, previously reported values would have been calculated using tumor sizes after they had passed the inflection point on the Gompertzian growth curve, falsely elevating the reported growth rate values. Finally, it is possible that tumor growth rate may be inherently slower in the pediatric population – a theory supported by the high-grade tumor reference, which demonstrated a VDE of 1.8 cm/year.
Conversely, previously reported low-grade growth characteristics are limited. Contemporaneous studies focus predominantly on analysis of adult low-grade tumor growth, including meningiomas [13–15] and low-grade gliomas  and demonstrate a wide range of growth patterns and rates. For example, Nakasu et al. reported volume doubling times of meningiomas ranging between 111 days and 91,400 days. Additionally, the authors noted the fastest growth rates in atypical meningiomas, intermediate growth rates in benign, noncalcified meningiomas, and slowest growth rates in calcified meningiomas. In comparison, the estimated volume doubling time in our study was 908 days, which was comparable to the intermediate growth rates presented by Nakasu et al. . Given the excellent fit of low-grade tumor group to the linear growth model, the estimated minimum VDE calculated in this study, 0.4 cm/year, likely better estimates the true growth rate for low-grade tumors, further supported by analysis of the low-grade ependymoma growth rate after diagnosis but before treatment, also 0.4 cm/year.
Though tumor growth rates have only been described in the adult population thus far, ADC values have been used to characterize brain tumors in both pediatric and adult populations. For example, Novak et al. focused on the classification of pediatric brain tumors using ADC values. The authors showed characteristic ADC values for ependymomas were 1.126 ± 0.155 × 10−3 mm2/s and 0.870 ± 0.154×10−3 mm2/s for medulloblastomas . Similarly, Abdulaziz et al. demonstrated ADC values ranging between 0.225–1.240 x 10−3 mm2/s for ependymal tumors, 0.107–1.571 × 10−3 mm2/s for embryonal tumors, 0.5220–0.7840 × 10−3 mm2/s for other astrocytic tumors, and 0.1530–0.8160 × 10−3 mm2/s for meningiomas . Yet these prior studies have not used pre-diagnostic imaging to characterize brain tissue at the site of subsequent tumor growth. In this study, the high-grade tumors demonstrated ADC values compatible with previously reported values.
Notably, this study did not demonstrate a lower mean ADC value in the pre-diagnostic neuroimaging studies of the high-grade tumors compared to the low-grade tumors. However, there was a small sample size in each tumor subtype, limiting analysis and the ability to draw definite conclusions about the utility of pre-diagnostic ADC values in characterizing brain tumors. Future studies with a larger sample size may further validate or negate this point.
Pediatric patients with brain tumors rarely receive brain imaging before diagnosis, limiting full characterization of different tumor subtypes. Thus far, adult tumor subtypes have been characterized using growth rate models and ADC values of neuroimaging already demonstrating macroscopic tumor. Prior studies do not evaluate ADC values of the corresponding brain tissue on pre-diagnostic imaging. This study is the first to demonstrate that both linear and exponential growth rate models can be used to estimate the growth rate of pediatric brain tumors. In addition, this study analyzed the use of ADC values to characterize high- and low-grade tumor subtypes in both pre-diagnostic and diagnostic imaging. Overall, this study demonstrates increased opportunity to identify tissue that is at risk for tumor development with the use of advanced diagnostic imaging and possibly allows for an increased chance of early, curative management.