Citations

OBJECTIVE Aneurysm occlusion has been used as surrogate marker of aneurysm treatment efficacy. Aneurysm occlusion scales are used to evaluate the outcome of endovascular aneurysm treatment and to monitor recurrence. These scales, however, require subjective interpretation of imaging data, which can reduce the utility and reliability of these scales and the validity of clinical studies regarding aneurysm occlusion rates. Use of a core lab with independent blinded reviewers has been implemented to enhance the validity of occlusion rate assessments in clinical trials. The degree of agreement between core labs and treating physicians has not been well studied with prospectively collected data. METHODS In this study, the authors analyzed data from the Hydrogel Endovascular Aneurysm Treatment (HEAT) trial to assess the interrater agreement between the treating physician and the blinded core lab. The HEAT trial included 600 patients across 46 sites with intracranial aneurysms treated with coiling. The treating site and the core lab independently reviewed immediate postoperative and follow-up imaging (3–12 and 18–24 months, respectively) using the Raymond-Roy occlusion classification (RROC) scale, Meyer scale, and recanalization survey. A post hoc analysis was performed to calculate interrater reliability using Cohen’s kappa. Further analysis was performed to assess whether degree of agreement varied on the basis of various factors, including scale used, timing of imaging, size of the aneurysm, imaging modality, location of the aneurysm, dome-to-neck ratio, and rupture status. RESULTS Minimal interrater agreement was noted between the core lab reviewers and the treating physicians for assessing aneurysm occlusion using the RROC grading scale (k = 0.39, 95% CI 0.38–0.40) and Meyer scale (k = 0.23, 95% CI 0.14–0.38). The degree of agreement between groups was slightly better but still weak for assessing recanalization (k = 0.45, 95% CI 0.38–0.52). Factors that significantly improved degree of agreement were scales with fewer variables, greater time to follow-up, imaging modality (digital subtraction angiography), and wide-neck aneurysms. CONCLUSIONS Assessment of aneurysm treatment outcome with commonly used aneurysm occlusion scales suffers from risk of poor interrater agreement. This supports the use of independent core labs for validation of outcome data to minimize reporting bias. Use of outcome tools with fewer point categories is likely to provide better interrater reliability. Therefore, the outcome assessment tools are ideal for clinical outcome assessment provided that they are sensitive enough to detect a clinically significant change.

Abstract Characterizing intra- and inter-tumoral heterogeneity of glioblastoma has historically relied on discrete classifications of malignant cell populations leaving immune and other cell populations, known to exist admixed with the malignant tumor cells, relatively neglected. Manifold learning algorithms can manage deconvolving multiple cell populations and are often used to track cell state transitions in single cell transcriptomics. We applied a manifold learning approach to TCGA microarray data (Nf525) and bulk transcriptomics of 134 image localized biopsies across 30 patients with primary and 9 with recurrent glioblastoma to further elucidate how to organize biopsies across a spectrum of possible tissue states. The algorithm revealed a low-dimensional manifold graph for which each biopsy lives across 3 polarizing tissue states - one that is associated with diffusely invaded brain, one that is enriched in mesenchymal genes, and one that is enriched in classical proliferative tumor signatures. We deconvolved the bulk transcriptomics of the image-localized biopsies to reveal the relative abundance of 18 malignant, immune, and other cell subpopulations in each biopsy. Overlaying the cellular decomposition onto the manifold graph visualizing the tissue state distributions revealed that transitions between states correlate with changes in cellular cohabitation composition. The tumor cellular cohabitation ecologies have the lowest diversity, as inferred by ecological measures such as Shannon entropy and evenness, at the distal poles of the graph when compared to the transitional arms. Further, we found that the relationship between imaging appearance of contrast enhancement on T1-weighted MRI and the biopsy cellular composition varies with sex and primary vs recurrent biopsy status. The limited spectrum of possible tissue states revealed by the manifold learning is suggestive of a limited continuum along which tumor and non-tumoral cell populations can cohabitate. Such a limited low-dimensional biological space may constrain the dynamics of tumor biology in a predictable manner.

Abstract Background Magnetic Resonance Imaging (MRI) is the mainstay for neurosurgical oncology but not for informing us about glioma biology. An obstacle to developing MR-based glioma biomarkers is the absence of rigorous correlation between MRI features and glioma biology as assessed in multi-regional biopsies, within and across patients. Methods We directly addressed this obstacle by collating a unique cohort of 202 MRI-localized biopsies from 58 patients. We define a low-dimensional transcriptional pseudotime continuum along which heterogeneous high-grade glioma (HGG) samples organize both within and across patients. Results We observe three polarized transcriptional tissue states: infiltrated brain, immune/inflammatory, and proliferative associated with patterns of cohabitation of cellular subpopulations. The states and deconvolved populations show correlation with enhancement status on T1Gd MRI. Moreover, discrete MRI habitats, regions sharing common imaging features, defined as combinations of high or low signal intensity across multiparametric MRI revealed 14 MRI habitats. We order the MRI habitats according to the average pseudotime on the transcriptional continuum. We find that MRI habitats with low pseudotime (associated with early tumor development and diffusely invaded brain tissue) localized at the periphery of the tumor whilst high pseudotime either proliferative or immune/inflammatory states were towards the core of the lesion. We find that composition of MRI habitats are impacted by MGMT status and patient sex. Conclusion This suggests that ongoing aggregation of MRI-localized biopsies may augment our projection of biology onto MRI habitats to support tracking of the evolution of cellular ecologies within and across each patient’s tumor over time. Key Points Trajectory inference of HGG samples reveals a continuum connecting three polarized tissue states reflecting transitions in cellular population ecologies and gene set enrichment Combining multiparametric MRI features into regional MRI habitats reveal clinically-distinct patterns of cell co-habitation and tissue state MGMT methylation status shifts the cellular patterns of cohabitation within MRI habitats Importance of the Study Regional heterogeneity is a defining characteristic of high-grade glioma (HGG). Mapping the transcriptomic biology of multiregionally sampled MRI-localized biopsies from high-grade gliomas onto a low-dimensional space reveals a continuum between three primary tissue states: infiltrated brain, immune/inflammatory, and proliferative. Each state is associated with distinct cell cohabitation ecologies. Transitions between the tissue states coordinate with changes in tumor composition, revealing potential direct connections between tumor biology shifts and their manifestation on clinical imaging.