Krishna, Chandan

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.

BACKGROUND: Minimally invasive surgical techniques have reinvigorated the role of surgical options for spontaneous intracranial hematomas; however, they are limited by the lack of real-time feedback on the extent of hematoma evacuation. OBJECTIVE: To describe the development of a MRI-guided catheter-based aspiration system, the ClearPoint Pursuit Neuroaspiration Device (ClearPoint Neuro) and validation in phantom models. METHODS: In this preclinical experimental trial, 8 phantom brains with skull models were created to simulate an intracranial hematoma with 2 clot sizes, 30 cc (small clot) and 60 cc (large clot). After registration, the aspiration catheter (Pursuit device) was aligned to the desired planned trajectory. The aspiration of the clot was performed under real-time MRI scan in 3 orthogonal views. The primary end point was reduction of the clot volume to less than 15 cc or 70% of the original clot volume. RESULTS: Successful completion of clot evacuation was achieved in all models. The average postaspiration clot volume was 9.5 cc (8.7 cc for small clots and 10.2 cc for large clots). The average percentage reduction of clot volume was 76.3% (range 58.7%-85.2%). The average total procedure time (from frame registration to final postaspiration clot assessment) was 50 min. The average aspiration time was 6.9 min. CONCLUSION: This preclinical trial confirms the feasibility and efficacy of MRI-guided aspiration under real-time image guidance in simulation models for intracranial hematoma. Clinical use of the system in patients would further validate its efficacy and safety.

Abstract This is the case of an 86-yr-old gentleman who presented with left facial pain exacerbated by eating, drinking, chewing, and shaving (distribution: V2, V3). The patient was diagnosed with trigeminal neuralgia and was refractory to medications. Imaging showed a superior cerebellar artery (SCA) loop adjacent to the trigeminal nerve root entry zone and a decision to perform a microvascular decompression of the fifth nerve was presented to the patient. After patient informed consent was obtained, a standard 3 cm × 3 cm retrosigmoid craniotomy was performed with the patient in a supine head turned position and in reverse Trendelenburg. The arachnoid bands tethering the SCA to the trigeminal nerve were sharply divided. A slit was then made in the tentorium and a 3 mm fenestrated clip was then used to secure the transposed SCA away from the trigeminal nerve. The SCA proximal to this was slightly patulous in its course so a small amount of a fibrin glue was also used to secure the more proximal SCA to the tentorium. The patient was symptom-free postoperatively and no longer required medical therapy. Additionally, imaging was consistent with adequate separation of the nerve from adjacent vessels. 1-5

Abstract INTRODUCTION The quantification of intratumoral heterogeneity – through radiomics-based approaches - can help resolve the regionally distinct genetic drug targets that may co-exist within a single Glioblastoma (GBM) tumor. While this offers potential diagnostic value under the paradigm of individualized oncology, clinical decision-making must also consider the degree of uncertainty associated with each model. In this study, we evaluate the performance of a novel machine-learning (ML) algorithm, called Gaussian Process (GP) modeling, that can quantify the impact of multiple sources of uncertainty in ML model development and prediction accuracy, including variabilities in the copy number measurement, radiomics features, training sample characteristics, and training sample size. METHOD We collected 95 image-localized biopsies from 25 primary GBM patients. We coregistered stereotactic locations with preoperative multi-parametric MRI features (conventional MRI, DSC perfusion, Diffusion Tensor Imaging) to generate spatially matched pairs of MRI and copy number variants (CNV) for for each biopsy. We developed a Gaussian Process (GP) model to predict CNV for Epidermal Growth Factor Receptor (EGFR) based on MRI radiomic features in each patient. We used leave-one-patient-out cross validation to quantify prediction accuracy and model uncertainty. Spatial prediction and uncertainty (p-value) maps were overlaid to help visualize regional genetic variation of EGFR and uncertainty of the radiomic predictions. RESULT: The initial GP radiomics model for EGFR amplification (CNV > 3.5) produced a sensitivity of 0.8 and specificity of 0.8. Samples/regions associated with high uncertainty (p-value >0.05) correlated with either 1) extrapolation of radiomic features from the training set-defined feature space or 2) insufficient training samples in the feature space. CONCLUSION We present a ML-based model that quantifies spatial genetic heterogeneity in GBM, while also estimating model uncertainties that result from multi-source data variabilities. This approach lays the groundwork for prospective clinical integration of modeling-based diagnostic approaches in the paradigm of individualized medicine.