Orringer, Daniel A.

Abstract High-grade gliomas (HGGs) nearly always recur after standard initial treatment, and the resulting mixture of recurrent tumor and treatment-induced reactive changes presents major diagnostic challenges. Anatomical imaging, such as MRI, cannot adequately distinguish progressive disease from treatment effect (pseudo-progression). Furthermore, there is marked intra-tumoral heterogeneity, such that some areas of a tumor may demonstrate necrotic treatment effect and others frank recurrence. Due to this difficulty reliably differentiating between these two clinical findings, analytic methods using multiple modalities are necessary to further our understanding of this disease process. To this end, we sought to correlate radiographic, histopathologic and molecular features of surgically sampled post-treatment suspected recurrence to identify markers distinguishing tumor growth from treatment effect. We performed Stimulated Raman Histology (SRH) imaging and highly multiplexed RNA-sequencing (PLATE-seq) on 84 MRI-localized biopsies from 39 patients with clinically suspected recurrent HGG. The SRH images were classified as recurrent tumor or gliotic/reactive tissue using a convolutional neural network trained on an independent cohort including a large set of recurrent HGG, and an automated cell-counting algorithm was used to quantify cellularity from the SRH image of each sample. Differential gene expression analysis of the PLATE-seq data was used to identify gene sets that distinguish recurrent tumor from treatment effect, and single sample gene set variation analysis (GSVA) was used to further assess the molecular and cellular composition of each MRI-localized sample. The histopathologic and molecular features of each sample were also correlated with the MRI features of the corresponding biopsy sites, and this data is currently being used to train machine learning models that predict the distribution of recurrent tumor and treatment-induced reactive changes within a patient’s radiographic lesion. These predictive radiomic models will help to guide neurosurgical sampling, and improve our ability to monitor glioma progression and response to therapy.

Abstract BACKGROUND Extent of resection (EOR) correlates with glioblastoma outcomes. Resectability and EOR depend on anatomical, clinical, and surgeon factors. Resectability likely influences outcome in and of itself, but an accurate measurement of resectability remains elusive. An understanding of resectability and the factors that influence it may provide a means to control a confounder in clinical trials and provide reference for decision making. OBJECTIVE To provide proof of concept of the use of the collective wisdom of experienced brain tumor surgeons in assessing glioblastoma resectability. METHODS We surveyed 13 academic tumor neurosurgeons nationwide to assess the resectability of newly diagnosed glioblastoma. Participants reviewed 20 cases, including digital imaging and communications in medicine-formatted pre- and postoperative magnetic resonance images and clinical vignettes. The selected cases involved a variety of anatomical locations and a range of EOR. Participants were asked about surgical goal, eg, gross total resection, subtotal resection (STR), or biopsy, and rationale for their decision. We calculated a “resectability index” for each lesion by pooling responses from all 13 surgeons. RESULTS Neurosurgeons’ individual surgical goals varied significantly ( P = .015), but the resectability index calculated from the surgeons’ pooled responses was strongly correlated with the percentage of contrast-enhancing residual tumor ( R = 0.817, P < .001). The collective STR goal predicted intraoperative decision of intentional STR documented on operative notes ( P < .01) and nonresectable residual ( P < .01), but not resectable residual. CONCLUSION In this pilot study, we demonstrate the feasibility of measuring the resectability of glioblastoma through crowdsourcing. This tool could be used to quantify resectability, a potential confounder in neuro-oncology clinical trials.