Distinguishing Extravascular from Intravascular Ferumoxytol Pools within the Brain: Proof of Concept in Patients with Treated Glioblastoma.

BACKGROUND AND PURPOSE: Glioblastoma-associated macrophages are a major constituent of the immune response to therapy and are known to engulf the iron-based MR imaging contrast agent, ferumoxytol. Current ferumoxytol MR imaging techniques for localizing macrophages are confounded by contaminating intravascular signal. The aim of this study was to assess the utility of a newly developed MR imaging technique, segregation and extravascular localization of ferumoxytol imaging, for differentiating extravascular-from-intravascular ferumoxytol contrast signal at a delayed 24-hour imaging time point. MATERIALS AND METHODS: Twenty-three patients with suspected post-chemoradiotherapy glioblastoma progression underwent ferumoxytol-enhanced SWI. Segregation and extravascular localization of ferumoxytol imaging maps were generated as the voxelwise difference of the delayed (24 hours) from the early (immediately after administration) time point SWI maps. Continuous segregation and extravascular localization of ferumoxytol imaging map values were separated into positive and negative components. Image-guided biologic correlation was performed. RESULTS: Negative segregation and extravascular localization of ferumoxytol imaging values correlated with early and delayed time point SWI values, demonstrating that intravascular signal detected in the early time point persists into the delayed time point. Positive segregation and extravascular localization of ferumoxytol imaging values correlated only with delayed time point SWI values, suggesting successful detection of the newly developed extravascular signal. CONCLUSIONS: Segregation and extravascular localization of ferumoxytol MR imaging improves on current techniques by eliminating intrinsic tissue and intravascular ferumoxytol signal and may inform glioblastoma outcomes by serving as a more specific metric of macrophage content compared with uncorrected T1 and SWI techniques.

Segmentation editing improves efficiency while reducing inter-expert variation and maintaining accuracy for normal brain tissues in the presence of space-occupying lesions.

Image segmentation has become a vital and often rate-limiting step in modern radiotherapy treatment planning. In recent years, the pace and scope of algorithm development, and even introduction into the clinic, have far exceeded evaluative studies. In this work we build upon our previous evaluation of a registration driven segmentation algorithm in the context of 8 expert raters and 20 patients who underwent radiotherapy for large space-occupying tumours in the brain. In this work we tested four hypotheses concerning the impact of manual segmentation editing in a randomized single-blinded study. We tested these hypotheses on the normal structures of the brainstem, optic chiasm, eyes and optic nerves using the Dice similarity coefficient, volume, and signed Euclidean distance error to evaluate the impact of editing on inter-rater variance and accuracy. Accuracy analyses relied on two simulated ground truth estimation methods: simultaneous truth and performance level estimation and a novel implementation of probability maps. The experts were presented with automatic, their own, and their peers' segmentations from our previous study to edit. We found, independent of source, editing reduced inter-rater variance while maintaining or improving accuracy and improving efficiency with at least 60% reduction in contouring time. In areas where raters performed poorly contouring from scratch, editing of the automatic segmentations reduced the prevalence of total anatomical miss from approximately 16% to 8% of the total slices contained within the ground truth estimations. These findings suggest that contour editing could be useful for consensus building such as in developing delineation standards, and that both automated methods and even perhaps less sophisticated atlases could improve efficiency, inter-rater variance, and accuracy.

Comparison of manual and automatic segmentation methods for brain structures in the presence of space-occupying lesions: a multi-expert study.

The purpose of this work was to characterize expert variation in segmentation of intracranial structures pertinent to radiation therapy, and to assess a registration-driven atlas-based segmentation algorithm in that context. Eight experts were recruited to segment the brainstem, optic chiasm, optic nerves, and eyes, of 20 patients who underwent therapy for large space-occupying tumors. Performance variability was assessed through three geometric measures: volume, Dice similarity coefficient, and Euclidean distance. In addition, two simulated ground truth segmentations were calculated via the simultaneous truth and performance level estimation algorithm and a novel application of probability maps. The experts and automatic system were found to generate structures of similar volume, though the experts exhibited higher variation with respect to tubular structures. No difference was found between the mean Dice similarity coefficient (DSC) of the automatic and expert delineations as a group at a 5% significance level over all cases and organs. The larger structures of the brainstem and eyes exhibited mean DSC of approximately 0.8-0.9, whereas the tubular chiasm and nerves were lower, approximately 0.4-0.5. Similarly low DSCs have been reported previously without the context of several experts and patient volumes. This study, however, provides evidence that experts are similarly challenged. The average maximum distances (maximum inside, maximum outside) from a simulated ground truth ranged from (-4.3, +5.4) mm for the automatic system to (-3.9, +7.5) mm for the experts considered as a group. Over all the structures in a rank of true positive rates at a 2 mm threshold from the simulated ground truth, the automatic system ranked second of the nine raters. This work underscores the need for large scale studies utilizing statistically robust numbers of patients and experts in evaluating quality of automatic algorithms.