Tmod-15. Reliability of Imaging-based Measures of Tumor ‘mass-effect’– Evidence From a Computational Study
Year: 2018
Type: conference-abstract
Abstract: Elevated tumor mass-effect is associated to poor prognosis in GBM [1,2]. However, tumor mass-effect is poorly quantified in clinical practice. Recently, Steed et al. [2] proposed ‘Lateral ventricle displacement’ (LVd), defined as the change in center-of-mass position of the lateral ventricles between an undeformed reference and the tumor-bearing anatomy, as quantitative imaging measure of mass-effect. They found that the magnitude of LVd in GBM patients can be associated with overall survival. These results show the clinical importance of tumor mass-effect in GBM, warranting robust clinical measures. This study characterizes image-derived estimates of tumor mass-effect by their ability to measure mass-effect accurately and reliably. We use a mathematical model to simulate tumor growth, which allows us to control and objectively quantify ‘mass-effect’ [3]. For given simulation parameters and growth location, we compute estimates of mass-effect from anatomical deformation during the growth process. We use multiple regression analysis to evaluate the ability of different estimates to explain the tumor’s objective mass-effect, measured by the tumor-induced pressure on the skull. Preliminary results from tumor growth simulations at 15 locations across a normal brain atlas confirm the potential of LVd as predictor of tumor mass-effect (R2=0.98). The maximum left-right displacement along the midline in a selected plane, a measure for ‘midline shift’, only explains a smaller percentage of mass-effect variability in the simulated population and strongly depends on the measurement plane (R2=0.20–0.85). We will report results of an extended study that applies our analysis approach to a large and diverse population of approximately 100 simulated tumors, by varying growth location and parameters related to the tumor’s proliferative and invasive potential. References: [1] Gamburg et al. IJROBP, 2000, 48, 5: 1359–62, [2] Steed et al. Scientific Reports, 2018, 8: 2827, [3] Abler et al. Neuro-Oncology, 2017, 19, suppl 6: vi245.
Source: Neuro-Oncology
Authors: Daniel Abler, Philippe Büchler, Russell Rockne
Language: English
Subfield: Genetics
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W2900330208