Delineation and segmentation of cerebral tumors by mapping blood-brain barrier disruption with dynamic contrast-enhanced CT and tracer kinetics modeling–a feasibility study

Dynamic contrast-enhanced (DCE) imaging is a promising approach for in vivo assessment of tissue microcirculation. Twenty patients with clinical and routine computed tomography (CT) evidence of intracerebral neoplasm were examined with DCE-CT imaging. Using a distributed-parameter model for tracer kinetics modeling of DCE-CT data, voxel-level maps of cerebral blood flow (F), intravascular blood volume (v _i) and intravascular mean transit time (t ₁) were generated. Permeability-surface area product (PS), extravascular extracellular blood volume (v _e) and extraction ratio (E) maps were also calculated to reveal pathologic locations of tracer extravasation, which are indicative of disruptions in the blood-brain barrier (BBB). All maps were visually assessed for quality of tumor delineation and measurement of tumor extent by two radiologists. Kappa (κ) coefficients and their 95% confidence intervals (CI) were calculated to determine the interobserver agreement for each DCE-CT map. There was a substantial agreement for the tumor delineation quality in the F, v _e and t ₁ maps. The agreement for the quality of the tumor delineation was excellent for the v _i, PS and E maps. Concerning the measurement of tumor extent, excellent and nearly excellent agreement was achieved only for E and PS maps, respectively. According to these results, we performed a segmentation of the cerebral tumors on the base of the E maps. The interobserver agreement for the tumor extent quantification based on manual segmentation of tumor in the E maps vs. the computer-assisted segmentation was excellent (κ = 0.96, CI: 0.93–0.99). The interobserver agreement for the tumor extent quantification based on computer segmentation in the mean images and the E maps was substantial (κ = 0.52, CI: 0.42–0.59). This study illustrates the diagnostic usefulness of parametric maps associated with BBB disruption on a physiology-based approach and highlights the feasibility for automatic segmentation of cerebral tumors.