Pilot trial of perampanel on peritumoral hyperexcitability and clinical outcomes in newly diagnosed high-grade glioma

Background Glutamatergic neuron-glioma synaptogenesis and peritumoral hyperexcitability promote glioma growth in a positive feedback loop. The objective of this study was to evaluate the feasibility and estimated effect sizes of the AMPA-R antagonist, perampanel, on intraoperative electrophysiologic hyperexcitability and clinical outcomes. Methods An open-label trial was performed comparing perampanel to standard of care (SOC) in patients undergoing resection of newly-diagnosed radiologic high-grade glioma. Perampanel was administered as a pre-operative loading dose followed by maintenance therapy until progressive disease or up to 12-months. SOC treatment involved levetiracetam for 7-days or as clinically indicated. The primary outcome of hyperexcitability was defined by intra-operative electrocorticography high frequency oscillation (HFO) rates. Seizure-freedom and overall survival (OS) were estimated by the Kaplan-Meier method. Tissue concentrations of perampanel, levetiracetam, and metabolites were measured by mass spectrometry. Results HFO rates were similar between perampanel-treated and SOC cohorts. The trial was terminated early after interim analysis for futility, and outcomes assessed in 11 patients (7 perampanel-treated, 4 SOC). Over a median 281 days of post-enrollment follow-up, 27% of patients had seizures, including 14% treated with perampanel and 50% treated with SOC. OS in perampanel-treated patients was similar to a glioblastoma reference cohort (p=0.81). Glutamate concentrations in surface biopsies were positively correlated with HFO rates in adjacent electrode contacts and were not significantly associated with treatment assignment or drug concentrations. Conclusions A peri-operative loading regimen of perampanel was safe and well-tolerated, with similar peritumoral hyperexcitability as in levetiracetam-treated patients. Maintenance anti-glutamatergic therapy was not observed to impact survival outcomes.

Evaluation of the safety, accuracy, and helpfulness of the GPT-4.0 Large Language Model in neurosurgery

BACKGROUND

Although prior work demonstrated the surprising accuracy of Large Language Models (LLMs) on neurosurgery board-style questions, their use in day-to-day clinical situations warrants further investigation. This study assessed GPT-4.0's responses to common clinical questions across various subspecialties of neurosurgery.

METHODS

A panel of attending neurosurgeons formulated 35 general neurosurgical questions spanning neuro-oncology, spine, vascular, functional, pediatrics, and trauma. All questions were input into GPT-4.0 with a prespecified, standard prompt. Responses were evaluated by two attending neurosurgeons, each on a standardized scale for accuracy, safety, and helpfulness. Citations were indexed and evaluated against identifiable database references.

RESULTS

GPT-4.0 responses were consistent with current medical guidelines and accounted for recent advances in the field 92.8 % and 78.6 % of the time respectively. Neurosurgeons reported GPT-4.0 responses providing unrealistic information or potentially risky information 14.3 % and 7.1 % of the time respectively. Assessed on 5-point scales, responses suggested that GPT-4.0 was clinically useful (4.0 ± 0.6), relevant (4.7 ± 0.3), and coherent (4.9 ± 0.2). The depth of clinical responses varied (3.7 ± 0.6), and "red flag" symptoms were missed 7.1 % of the time. Moreover, GPT-4.0 cited 86 references (2.46 citations per answer), of which only 50 % were deemed valid, and 77.1 % of responses contained at least one inappropriate citation.

CONCLUSION

Current general LLM technology can offer generally accurate, safe, and helpful neurosurgical information, but may not fully evaluate medical literature or recent field advances. Citation generation and usage remains unreliable. As this technology becomes more ubiquitous, clinicians will need to exercise caution when dealing with it in practice.

Elevated risk of recurrence and retreatment for silent pituitary adenomas

PURPOSE

Pituitary adenomas are the most common tumor of the pituitary gland and comprise nearly 15% of all intracranial masses. These tumors are stratified into functional or silent categories based on their pattern of hormone expression and secretion. Preliminary evidence supports differential clinical outcomes between some functional pituitary adenoma (FPA) subtypes and silent pituitary adenoma (SPA) subtypes.

METHODS

We collected and analyzed the medical records of all patients undergoing resection of SPAs or FPAs from a single high-volume neurosurgeon between 2007 and 2018 at Brigham and Women's Hospital. Descriptive statistics and the Mantel-Cox log-rank test were used to identify differences in outcomes between these cohorts, and multivariate logistic regression was used to identify predictors of radiographic recurrence for SPAs.

RESULTS

Our cohort included 88 SPAs and 200 FPAs. The majority of patients in both cohorts were female (48.9% of SPAs and 63.5% of FPAs). SPAs were larger in median diameter than FPAs (2.1 cm vs. 1.2 cm, p < 0.001). The most frequent subtypes of SPA were gonadotrophs (55.7%) and corticotrophs (30.7%). Gross total resection (GTR) was achieved in 70.1% of SPA resections and 86.0% of FPA resections (p < 0.001). SPAs had a higher likelihood of recurring (hazard ratio [HR] 3.2, 95% confidence interval [95%CI] 1.6-7.2) and a higher likelihood of requiring retreatment for recurrence (HR 2.5; 95%CI 1.0-6.1). Subset analyses revealed that recurrence and retreatment were more both likely for subtotally resected SPAs than subtotally resected FPAs, but this pattern was not observed in SPAs and FPAs after GTR. Among SPAs, recurrence was associated with STR (odds ratio [OR] 9.3; 95%CI 1.4-64.0) and younger age (OR 0.92 per year; 95%CI 0.88-0.98) in multivariable analysis. Of SPAs that recurred, 12 of 19 (63.2%) were retreated with repeat surgery (n = 11) or radiosurgery (n = 1), while the remainder were observed (n = 7).There were similar rates of recurrence across different SPA subtypes.

CONCLUSION

Patients undergoing resection of SPAs should be closely monitored for disease recurrence through more frequent clinical follow-up and diagnostic imaging than other adenomas, particularly among patients with STR and younger patients. Several patients can be observed after radiographic recurrence, and the decision to retreat should be individualized. Longitudinal clinical follow-up of SPAs, including an assessment of symptoms, endocrine function, and imaging remains critical.

Inaugural Results of the Individualized Screening Trial of Innovative Glioblastoma Therapy: A Phase II Platform Trial for Newly Diagnosed Glioblastoma Using Bayesian Adaptive Randomization

PURPOSE

The Individualized Screening Trial of Innovative Glioblastoma Therapy (INSIGhT) is a phase II platform trial that uses response adaptive randomization and genomic profiling to efficiently identify novel therapies for phase III testing. Three initial experimental arms (abemaciclib [a cyclin-dependent kinase [CDK]4/6 inhibitor], neratinib [an epidermal growth factor receptor [EGFR]/human epidermal growth factor receptor 2 inhibitor], and CC-115 [a deoxyribonucleic acid-dependent protein kinase/mammalian target of rapamycin inhibitor]) were simultaneously evaluated against a common control arm. We report the results for each arm and examine the feasibility and conduct of the adaptive platform design.

PATIENTS AND METHODS

Patients with newly diagnosed O6-methylguanine-DNA methyltransferase-unmethylated glioblastoma were eligible if they had tumor genotyping to identify prespecified biomarker subpopulations of dominant glioblastoma signaling pathways (EGFR, phosphatidylinositol 3-kinase, and CDK). Initial random assignment was 1:1:1:1 between control (radiation therapy and temozolomide) and the experimental arms. Subsequent Bayesian adaptive randomization was incorporated on the basis of biomarker-specific progression-free survival (PFS) data. The primary end point was overall survival (OS), and one-sided P values are reported. The trial is registered with ClinicalTrials.gov (identifier: NCT02977780).

RESULTS

Two hundred thirty-seven patients were treated (71 control; 73 abemaciclib; 81 neratinib; 12 CC-115) in years 2017-2021. Abemaciclib and neratinib were well tolerated, but CC-115 was associated with ≥ grade 3 treatment-related toxicity in 58% of patients. PFS was significantly longer with abemaciclib (hazard ratio [HR], 0.72; 95% CI, 0.49 to 1.06; one-sided P = .046) and neratinib (HR, 0.72; 95% CI, 0.50 to 1.02; one-sided P = .033) relative to the control arm but there was no PFS benefit with CC-115 (one-sided P = .523). None of the experimental therapies demonstrated a significant OS benefit (P > .05).

CONCLUSION

The INSIGhT design enabled efficient simultaneous testing of three experimental agents using a shared control arm and adaptive randomization. Two investigational arms had superior PFS compared with the control arm, but none demonstrated an OS benefit. The INSIGhT design may promote improved and more efficient therapeutic discovery in glioblastoma. New arms have been added to the trial.

Positioning SUMO as an immunological facilitator of oncolytic viruses for high-grade glioma

Oncolytic viral (OV) therapies are promising novel treatment modalities for cancers refractory to conventional treatment, such as glioblastoma, within the central nervous system (CNS). Although OVs have received regulatory approval for use in the CNS, efficacy is hampered by obstacles related to delivery, under-/over-active immune responses, and the "immune-cold" nature of most CNS malignancies. SUMO, the Small Ubiquitin-like Modifier, is a family of proteins that serve as a high-level regulator of a large variety of key physiologic processes including the host immune response. The SUMO pathway has also been implicated in the pathogenesis of both wild-type viruses and CNS malignancies. As such, the intersection of OV biology with the SUMO pathway makes SUMOtherapeutics particularly interesting as adjuvant therapies for the enhancement of OV efficacy alone and in concert with other immunotherapeutic agents. Accordingly, the authors herein provide: 1) an overview of the SUMO pathway and its role in CNS malignancies; 2) describe the current state of CNS-targeted OVs; and 3) describe the interplay between the SUMO pathway and the viral lifecycle and host immune response.

Metastatic pituitary tumors: an institutional case series

PURPOSE

Pituitary carcinomas are a rare entity that respond poorly to multimodal therapy. Patients follow a variable disease course that remains ill-defined.

METHODS

We present an institutional case series of patients treated for pituitary carcinomas over a 30-year period from 1992 to 2022. A systematic review was conducted to identify prior case series of patients with pituitary carcinomas.

RESULTS

Fourteen patients with a mean age at pituitary carcinoma diagnosis of 52.5 years (standard deviation [SD] 19.4) met inclusion criteria. All 14 patients had tumor subtypes confirmed by immunohistochemistry and hormone testing, with the most common being ACTH-producing pituitary adenomas (n = 12). Patients had a median progression-free survival (PFS) of 1.4 years (range 0.7-10.0) and a median overall survival (OS) of 8.4 years (range 2.3-24.0) from pituitary adenoma diagnosis. Median PFS and OS were 0.6 years (range 0.0-2.2) and 1.5 years (range 0.1-9.6) respectively upon development of metastases. Most patients (n = 12) had locally invasive disease to the cavernous sinus, dorsum sellae dura, or sphenoid sinus prior to metastasis. Common sites of metastasis included the central nervous system, liver, lung, and bone. In a pooled analysis including additional cases from the literature, treatment of metastases with chemotherapy or a combination of radiation therapy and chemotherapy significantly prolonged PFS (p = 0.02), while failing to significantly improve OS (p = 0.14).

CONCLUSION

Pituitary carcinomas are highly recurrent, heterogenous tumors with variable responses to treatment. Multidisciplinary management with an experienced neuro-endocrine and neuro-oncology team is needed given the unrelenting nature of this disease.

Evaluation of Standard Response Assessment in Neuro-Oncology, Modified Response Assessment in Neuro-Oncology, and Immunotherapy Response Assessment in Neuro-Oncology in Newly Diagnosed and Recurrent Glioblastoma

PURPOSE

The Response Assessment in Neuro-Oncology (RANO) criteria are widely used in high-grade glioma clinical trials. We compared the RANO criteria with updated modifications (modified RANO [mRANO] and immunotherapy RANO [iRANO] criteria) in patients with newly diagnosed glioblastoma (nGBM) and recurrent GBM (rGBM) to evaluate the performance of each set of criteria and inform the development of the planned RANO 2.0 update.

MATERIALS AND METHODS

Evaluation of tumor measurements and fluid-attenuated inversion recovery (FLAIR) sequences were performed by blinded readers to determine disease progression using RANO, mRANO, iRANO, and other response assessment criteria. Spearman's correlations between progression-free survival (PFS) and overall survival (OS) were calculated.

RESULTS

Five hundred twenty-six nGBM and 580 rGBM cases were included. Spearman's correlations were similar between RANO and mRANO (0.69 [95% CI, 0.62 to 0.75] v 0.67 [95% CI, 0.60 to 0.73]) in nGBM and rGBM (0.48 [95% CI, 0.40 to 0.55] v 0.50 [95% CI, 0.42 to 0.57]). In nGBM, requirement of a confirmation scan within 12 weeks of completion of radiotherapy to determine progression was associated with improved correlations. Use of the postradiation magnetic resonance imaging (MRI) as baseline scan was associated with improved correlation compared with use of the pre-radiation MRI (0.67 [95% CI, 0.60 to 0.73] v 0.53 [95% CI, 0.42 to 0.62]). Evaluation of FLAIR sequences did not improve the correlation. Among patients who received immunotherapy, Spearman's correlations were similar among RANO, mRANO, and iRANO.

CONCLUSION

RANO and mRANO demonstrated similar correlations between PFS and OS. Confirmation scans were only beneficial in nGBM within 12 weeks of completion of radiotherapy, and there was a trend in favor of the use of postradiation MRI as the baseline scan in nGBM. Evaluation of FLAIR can be omitted. The iRANO criteria did not add significant benefit in patients who received immune checkpoint inhibitors.

Critically reading machine learning literature in neurosurgery: a reader's guide and checklist for appraising prediction models

OBJECTIVE

Machine learning (ML) has become an increasingly popular tool for use in neurosurgical research. The number of publications and interest in the field have recently seen significant expansion in both quantity and complexity. However, this also places a commensurate burden on the general neurosurgical readership to appraise this literature and decide if these algorithms can be effectively translated into practice. To this end, the authors sought to review the burgeoning neurosurgical ML literature and to develop a checklist to help readers critically review and digest this work.

METHODS

The authors performed a literature search of recent ML papers in the PubMed database with the terms "neurosurgery" AND "machine learning," with additional modifiers "trauma," "cancer," "pediatric," and "spine" also used to ensure a diverse selection of relevant papers within the field. Papers were reviewed for their ML methodology, including the formulation of the clinical problem, data acquisition, data preprocessing, model development, model validation, model performance, and model deployment.

RESULTS

The resulting checklist consists of 14 key questions for critically appraising ML models and development techniques; these are organized according to their timing along the standard ML workflow. In addition, the authors provide an overview of the ML development process, as well as a review of key terms, models, and concepts referenced in the literature.

CONCLUSIONS

ML is poised to become an increasingly important part of neurosurgical research and clinical care. The authors hope that dissemination of education on ML techniques will help neurosurgeons to critically review new research better and more effectively integrate this technology into their practices.

Endoscopic endonasal transclival approach for a ventral brainstem epidermoid cyst: illustrative case

BACKGROUND

Intracranial epidermoid cysts are benign, slow-growing malformations that most commonly arise at the skull base. Maximizing resection of the cyst contents and the capsule reduces long-term recurrence but can be made difficult by cyst wall adherence to critical neurovascular structures. Expanded endonasal approaches (EEAs) offer an alternative to traditional open transcranial approaches for accessible epidermoid cysts. In this case report, the authors demonstrate a transclival EEA for a large, ventral brainstem epidermoid cyst.

OBSERVATIONS

A 41-year-old woman who presented with progressive headaches, diplopia, malaise, and fatigue was found to have a 4.7-cm midline, ventral brainstem epidermoid cyst. She underwent an expanded endonasal transclival approach that exposed the brainstem from the level of the dorsum sella to the tip of the basion. A near-total resection was completed with removal of all cyst contents and most of the capsular wall. Reconstruction was completed with Duragen, an autologous fat graft, and a nasoseptal flap. Postoperatively, she had a partial left cranial nerve VI palsy that remained stable 8 weeks after surgery.

LESSONS

The expanded endoscopic transclival approach can facilitate effective resection of midline, ventral epidermoid cysts.

Positioning Transclival Tumor-Treating Fields for the Treatment of Diffuse Intrinsic Pontine Gliomas

Diffuse intrinsic pontine glioma (DIPG) carries an extremely poor prognosis, with 2-year survival rates of <10% despite the maximal radiation therapy. DIPG cells have previously been shown to be sensitive to low-intensity electric fields in vitro. Accordingly, we sought to determine if the endoscopic endonasal (EE) implantation of an electrode array in the clivus would be feasible for the application of tumor-treating fields (TTF) in DIPG. Anatomic constraints are the main limitation in pediatric EE approaches. In our Boston Children’s Hospital’s DIPG cohort, we measured the average intercarotid distance (1.68 ± 0.36 cm), clival width (1.62 ± 0.19 cm), and clival length from the base of the sella (1.43 ± 0.69 cm). Using a linear regression model, we found that only clival length and sphenoid pneumatization were significantly associated with age (R2 = 0.568, p = 0.005 *; R2 = 0.605, p = 0.0002 *). Critically, neither of these parameters represent limitations to the implantation of a device within the dimensions of those currently available. Our findings confirm that the anatomy present within this age group is amenable to the placement of a 2 × 1 cm electrode array in 94% of patients examined. Our work serves to demonstrate the feasibility of implantable transclival devices for the provision of TTFs as a novel adjunctive therapy for DIPG.

Modeling of intracranial tumor treating fields for the treatment of complex high-grade gliomas

Increasing the intensity of tumor treating fields (TTF) within a tumor bed improves clinical efficacy, but reaching sufficiently high field intensities to achieve growth arrest remains challenging due in part to the insulating nature of the cranium. Using MRI-derived finite element models (FEMs) and simulations, we optimized an exhaustive set of intracranial electrode locations to obtain maximum TTF intensities in three clinically challenging high-grade glioma (HGG) cases (i.e., thalamic, left temporal, brainstem). Electric field strengths were converted into therapeutic enhancement ratios (TER) to evaluate the predicted impact of stimulation on tumor growth. Concurrently, conventional transcranial configurations were simulated/optimized for comparison. Optimized intracranial TTF were able to achieve field strengths that have previously been shown capable of inducing complete growth arrest, in 98-100% of the tumor volumes using only 0.54-0.64 A current. The reconceptualization of TTF as a targeted, intracranial therapy has the potential to provide a meaningful survival benefit to patients with HGG and other brain tumors, including those in surgically challenging, deep, or anatomically eloquent locations which may preclude surgical resection. Accordingly, such an approach may ultimately represent a paradigm shift in the use of TTFs for the treatment of brain cancer.

Reduced time to imaging, length of stay, and hospital charges following implementation of a novel postoperative pathway for craniotomy

OBJECTIVE

The authors created a postoperative postanesthesia care unit (PACU) pathway to bypass routine intensive care unit (ICU) admissions of patients undergoing routine craniotomies, to improve ICU resource utilization and reduce overall hospital costs and lengths of stay while maintaining quality of care and patient satisfaction. In the present study, the authors evaluated this novel PACU-to-floor clinical pathway for a subset of patients undergoing craniotomy with a case time under 5 hours and blood loss under 500 ml.

METHODS

A single-institution retrospective cohort study was performed to compare 202 patients enrolled in the PACU-to-floor pathway and 193 historical controls who would have met pathway inclusion criteria. The pathway cohort consisted of all adult supratentorial brain tumor cases from the second half of January 2021 to the end of January 2022 that met the study inclusion criteria. Control cases were selected from the beginning of January 2020 to halfway through January 2021. The authors also discuss common themes of similar previously published pathways and the logistical and clinical barriers overcome for successful PACU pathway implementation.

RESULTS

Pathway enrollees had a median age of 61 years (IQR 49-69 years) and 53% were female. Age, sex, pathology, and American Society of Anesthesiologists physical status distributions were similar between pathway and control patients (p > 0.05). Most of the pathway cases (96%) were performed on weekdays, and 31% had start times before noon. Nineteen percent of pathway patients had 30-day readmissions, most frequently for headache (16%) and syncope (10%), whereas 18% of control patients had 30-day readmissions (p = 0.897). The average time to MRI was 6 hours faster for pathway patients (p < 0.001) and the time to inpatient physical therapy and/or occupational therapy evaluation was 4.1 hours faster (p = 0.046). The average total length of stay was 0.7 days shorter for pathway patients (p = 0.02). A home discharge occurred in 86% of pathway cases compared to 81% of controls (p = 0.225). The average total hospitalization charges were $13,448 lower for pathway patients, representing a 7.4% decrease (p = 0.0012, adjusted model). Seven pathway cases were escalated to the ICU postoperatively because of attending physician preference (2 cases), agitation (1 case), and new postoperative neurological deficits (4 cases), resulting in a 96.5% rate of successful discharge from the pathway. In bypassing the ICU, critical care resource utilization was improved by releasing 0.95 ICU days per patient, or 185 ICU days across the cohort.

CONCLUSIONS

The featured PACU-to-floor pathway reduces the stay of postoperative craniotomy patients and does not increase the risk of early hospital readmission.

An expanded role for surgery in grade 3 1p/19q co-deleted oligodendroglioma

Background

Grade 3 1p/19q co-deleted oligodendroglioma is an uncommon primary CNS tumor with a high rate of progression and recurrence. This study examines the benefit of surgery after progression and identifies predictors of survival.

Methods

This is a single-institution retrospective cohort study of consecutive adult patients with anaplastic or grade 3 1p/19q co-deleted oligodendroglioma diagnosed between 2001 and 2020.

Results

Eighty patients with 1p/19q co-deleted grade 3 oligodendroglioma were included. The median age was 47 years (interquartile range 38-56) and 38.8% were women. All patients underwent surgery, including gross total resection (GTR) for 26.3% of patients, subtotal resection (STR) for 70.0% of patients, and biopsy for 3.8% of patients. Forty-three cases (53.8%) progressed at a median of 5.6 years, and the median overall survival (OS) was 14.1 years. Among 43 cases of progression or recurrence, 21 (48.8%) underwent another resection. Patients who underwent a second operation had improved OS (P = .041) and survival after progression/recurrence (P = .012), but similar time to subsequent progression as patients who did not have repeat surgery (P = .50). Predictors of mortality at initial diagnosis included a preoperative Karnofsky Performance Status (KPS) under 80 (hazard ratio [HR] 5.4; 95% CI 1.5-19.2), an STR or biopsy rather than GTR (HR 4.1; 95% CI 1.2-14.2), and a persistent postoperative neurologic deficit (HR 4.0; 95% CI 1.2-14.1).

Conclusions

Repeat surgery is associated with increased survival, but not time to subsequent progression for progressing or recurrent 1p/19q co-deleted grade 3 oligodendrogliomas recur. Mortality is associated with a preoperative KPS under 80, lack of GTR, and persistent postoperative neurologic deficits after the initial surgery.

Evaluating hematologic parameters in newly diagnosed and recurrent glioblastoma: Prognostic utility and clinical trial implications of myelosuppression

BACKGROUND

Glioblastoma (GBM) patients are treated with radiation therapy, chemotherapy, and corticosteroids, which can cause myelosuppression. To understand the relative prognostic utility of blood-based biomarkers in GBM and its implications for clinical trial design, we examined the incidence, predictors, and prognostic value of lymphopenia, neutrophil-to-lymphocyte ratio (NLR), and platelet count during chemoradiation (CRT) and recurrence.

METHODS

This cohort study included 764 newly diagnosed glioblastoma patients treated from 2005 to 2019 with blood counts prior to surgery, within 6 weeks of CRT, and at first recurrence available for automatic extraction from the medical record. Logistic regression was used to evaluate exposures and Kaplan-Meier was used to evaluate outcomes.

RESULTS

Among the cohort, median age was 60.3 years; 87% had Karnofsky performance status ≥ 70, 37.5% had gross total resection, and 90% received temozolomide (TMZ). During CRT, 37.8% (248/656) of patients developed grade 3 or higher lymphopenia. On multivariable analysis (MVA), high NLR during CRT remained an independent predictor for inferior survival (Adjusted Hazard Ratio [AHR] = 1.57, 95% CI = 1.14-2.15) and shorter progression-free survival (AHR = 1.42, 95% CI = 1.05-1.90). Steroid use was associated with lymphopenia (OR = 2.66,1.20-6.00) and high NLR (OR = 3.54,2.08-6.11). Female sex was associated with lymphopenia (OR = 2.33,1.03-5.33). At first recurrence, 28% of patients exhibited grade 3 or higher lymphopenia. High NLR at recurrence was associated with worse subsequent survival on MVA (AHR = 1.69, 95% CI = 1.25-2.27).

CONCLUSIONS

High NLR is associated with worse outcomes in newly diagnosed and recurrent glioblastoma. Appropriate eligibility criteria and accounting and reporting of blood-based biomarkers are important in the design and interpretation of newly diagnosed and recurrent glioblastoma trials.

Cerebellar High-Grade Glioma: A Translationally Oriented Review of the Literature

World Health Organization (WHO) grade 4 gliomas of the cerebellum are rare entities whose understanding trails that of their supratentorial counterparts. Like supratentorial high-grade gliomas (sHGG), cerebellar high-grade gliomas (cHGG) preferentially affect males and prognosis is bleak; however, they are more common in a younger population. While current therapy for cerebellar and supratentorial HGG is the same, recent molecular analyses have identified features and subclasses of cerebellar tumors that may merit individualized targeting. One recent series of cHGG included the subclasses of (1) high-grade astrocytoma with piloid features (HGAP, ~31% of tumors); (2) H3K27M diffuse midline glioma (~8%); and (3) isocitrate dehydrogenase (IDH) wildtype glioblastoma (~43%). The latter had an unusually low-frequency of epidermal growth factor receptor (EGFR) and high-frequency of platelet-derived growth factor receptor alpha (PDGFRA) amplification, reflecting a different composition of methylation classes compared to supratentorial IDH-wildtype tumors. These new classifications have begun to reveal insights into the pathogenesis of HGG in the cerebellum and lead toward individualized treatment targeted toward the appropriate subclass of cHGG. Emerging therapeutic strategies include targeting the mitogen-activated protein kinases (MAPK) pathway and PDGFRA, oncolytic virotherapy, and immunotherapy. HGGs of the cerebellum exhibit biological differences compared to sHGG, and improved understanding of their molecular subclasses has the potential to advance treatment.

BIOM-37. EVALUATION OF TEMPORALIS MUSCLE THICKNESS WITH TOXICITY AND SURVIVAL IN GLIOBLASTOMA PATIENTS RECEIVING CHEMORADIATION

BACKGROUND

Treatment-related toxicity is common in patients with glioblastoma (GBM) receiving chemotherapy and radiotherapy (RT). Temporalis muscle thickness (TMT) is a biomarker associated with sarcopenia and worse clinical outcomes in GBM, however its relation to treatment toxicity is less studied. We hypothesize that TMT may predict toxicity and survival in GBM patients.

METHODS

We reviewed consecutive patients with IDH-wildtype GBM treated from 2014-2019 at a single academic center. TMT was retrospectively assessed on T1-weighted MRI scans and dichotomized based upon previously validated sex-specific cutoff values. TMT was measured on baseline MRI scan at time of diagnosis. Cox regression multivariable analysis (MVA) was used to assess survival.

RESULTS

We evaluated 351 patients with median age of 60y (range 20-94) and median follow-up of 14mo. Most patients were male (59%), baseline KPS &gt;70 (95%), and MGMT unmethylated (55%). After maximal safe resection, most patients received standard (90%) or hypofractionated (10%) RT with concurrent systemic therapy (89%). On MVA, baseline low TMT (HR 1.93, p=0.01), age &gt;65y, baseline KPS, and MGMT-unmethylated status were associated with worse OS. On MVA, baseline low TMT (HR 1.95, p=0.01), age &gt;65y, MGMT-unmethylated status, and discontinuing systemic therapy were associated with worse profession-free survival (PFS). 21 patients did not complete anticipated treatment course of chemoradiation and adjuvant systemic therapy due to toxicity, primarily thrombocytopenia, associated with worse OS on MVA (HR 1.99, p&lt; 0.01). Low TMT was associated with higher risk of stopping treatment due to adverse events (OR 5.25, p&lt; 0.01) independent of age, sex, extent of resection, RT dose on MVA.

CONCLUSION

Baseline low TMT was associated with worse PFS and OS, and it was associated with treatment interruption due to treatment toxicity in GBM patients. While further validation is needed, TMT may help identify patients who will benefit from aggressive symptom management or treatment deintensification.

CTIM-25. NEOADJUVANT ANTI-PD1 IMMUNOTHERAPY FOR SURGICALLY ACCESSIBLE RECURRENT GLIOBLASTOMA: CLINICAL AND MOLECULAR OUTCOMES OF A STAGE 2 SINGLE-ARM EXPANSION COHORT

Glioblastoma is immunologically “cold”, with little clinical response to single-agent immune-checkpoint inhibitors (ICI). Our previous randomized study of neoadjuvant and adjuvant pembrolizumab versus adjuvant pembrolizumab in recurrent glioblastoma identified a molecular signature for ICI and suggested that neoadjuvant pembrolizumab improved survival (PMID30742122). We extended these original findings with a single-arm expansion cohort of glioblastoma patients at first or second relapse undergoing surgical resection for recurrent disease. Eligibility was identical to the original study. Patients receiving &gt; 4mg dexamethasone, or with prior immunotherapy or anti-angiogenic therapy were excluded. The primary objective was to determine the effect of neoadjuvant pembrolizumab on cell cycle/cancer proliferation genes. Secondary objectives were PFS-6 and OS. Twenty-five patients received neoadjuvant pembrolizumab followed by tumor resection and adjuvant pembrolizumab until progression or unacceptable toxicity. The median age of participants was 60 years and median KPS was 90. Ninety-six percent of tumors were IDH wild-type and 48% were MGMT promoter methylated. Neoadjuvant pembrolizumab continued to induce alterations in the tumor microenvironment (T-cell/IFN upregulation, cell cycle downregulation; molecular responders (MR)), but the proportion of MR in this trial was 36% compared with 70% in the original treatment cohort. Median OS was 6.8 months. There were no CTCAE grade 4-5 AEs attributable to pembrolizumab. Further analysis of prognostic differences between stage 1 and 2 subjects is underway, as are additional immune and genotypic correlates to further define molecular changes induced by pembrolizumab. Anti-PD1 monotherapy triggers an intra-tumoral genetic signature associated with interferon-γ response and suppression of cell cycle/cancer proliferation in recurrent glioblastoma, though does not extend OS. This surgical window-of-opportunity paradigm allows for attribution of molecular effects to single agents. Future work will evaluate the differences in patient population, tumor characteristics, and address additional immune checkpoints and/or signaling pathways that can be targeted to augment the effectiveness of anti-PD1 in glioblastoma.

NIMG-59. EVALUATION OF THE RESPONSE ASSESSMENT CRITERIA IN NEWLY DIAGNOSED AND RECURRENT GLIOBLASTOMA

BACKGROUND

We sought to compare the Response Assessment in Neuro-Oncology (RANO), modified RANO (mRANO), and immunotherapy RANO (iRANO) in a large population of patients with newly diagnosed (nGBM) and recurrent (rGBM) glioblastoma.

METHODS

Bidimensional measurements of enhancing disease and FLAIR sequence evaluation were performed by two independent readers on brain MRIs of consecutive patients with IDH-wildtype nGBM and rGBM treated at a single institution. Discrepancies were evaluated by a third reader. Dates of disease progression (PD) were identified using RANO, mRANO, iRANO, and other response assessment criteria variations. Spearman’s correlations between PFS and OS were calculated using iterative multiple imputations for censored observations.

RESULTS

526 nGBM and 580 rGBM cases were included. Spearman’s correlations were not significantly different between RANO and mRANO in nGBM (0.69 [95% CI 0.62 to 0.75] vs. 0.67 [0.60, 0.73]) and rGBM (0.48 [0.40, 0.55] vs. 0.50 [0.42, 0.57]). Evaluation of FLAIR did not improve the correlation in patients who received antiangiogenic therapy. Acquisition of confirmation scans was associated with increased correlation only when PD was identified within 12 weeks of completion of radiation in nGBM. The use of the post-radiation MRI as a baseline was associated with increased correlation compared to use of the pre-radiation MRI in nGBM (0.67 [0.60, 0.73] vs. 0.53 [0.42, 0.62]). The correlation with iRANO was similar to RANO and mRANO among 98 patients with nGBM and 175 patients with rGBM who received immunotherapy.

CONCLUSIONS

RANO and mRANO demonstrated similar correlations between PFS and OS. The evaluation of FLAIR can be omitted, while confirmation scans were only beneficial in nGBM in the first 12 weeks after completion of radiation. There was a trend in favor of the post-radiation MRI as the baseline scan in nGBM. The use of iRANO criteria did not add a significant benefit in patients who received immunotherapy.

Deep neural networks allow expert-level brain meningioma segmentation and present potential for improvement of clinical practice

Accurate brain meningioma segmentation and volumetric assessment are critical for serial patient follow-up, surgical planning and monitoring response to treatment. Current gold standard of manual labeling is a time-consuming process, subject to inter-user variability. Fully-automated algorithms for meningioma segmentation have the potential to bring volumetric analysis into clinical and research workflows by increasing accuracy and efficiency, reducing inter-user variability and saving time. Previous research has focused solely on segmentation tasks without assessment of impact and usability of deep learning solutions in clinical practice. Herein, we demonstrate a three-dimensional convolutional neural network (3D-CNN) that performs expert-level, automated meningioma segmentation and volume estimation on MRI scans. A 3D-CNN was initially trained by segmenting entire brain volumes using a dataset of 10,099 healthy brain MRIs. Using transfer learning, the network was then specifically trained on meningioma segmentation using 806 expert-labeled MRIs. The final model achieved a median performance of 88.2% reaching the spectrum of current inter-expert variability (82.6–91.6%). We demonstrate in a simulated clinical scenario that a deep learning approach to meningioma segmentation is feasible, highly accurate and has the potential to improve current clinical practice.

Survival Prediction After Neurosurgical Resection of Brain Metastases: A Machine Learning Approach

BACKGROUND

Current prognostic models for brain metastases (BMs) have been constructed and validated almost entirely with data from patients receiving up-front radiotherapy, leaving uncertainty about surgical patients.

OBJECTIVE

To build and validate a model predicting 6-month survival after BM resection using different machine learning algorithms.

METHODS

An institutional database of 1062 patients who underwent resection for BM was split into an 80:20 training and testing set. Seven different machine learning algorithms were trained and assessed for performance; an established prognostic model for patients with BM undergoing radiotherapy, the diagnosis-specific graded prognostic assessment, was also evaluated. Model performance was assessed using area under the curve (AUC) and calibration.

RESULTS

The logistic regression showed the best performance with an AUC of 0.71 in the hold-out test set, a calibration slope of 0.76, and a calibration intercept of 0.03. The diagnosis-specific graded prognostic assessment had an AUC of 0.66. Patients were stratified into regular-risk, high-risk and very high-risk groups for death at 6 months; these strata strongly predicted both 6-month and longitudinal overall survival ( P < .0005). The model was implemented into a web application that can be accessed through http://brainmets.morethanml.com .

CONCLUSION

We developed and internally validated a prediction model that accurately predicts 6-month survival after neurosurgical resection for BM and allows for meaningful risk stratification. Future efforts should focus on external validation of our model.

Clinical utility of targeted next-generation sequencing assay in IDH-wildtype glioblastoma for therapy decision-making

BACKGROUND

Targeted gene NGS testing is available through many academic institutions and commercial entities and is increasingly incorporated in practice guidelines for glioblastoma (GBM). This single-center retrospective study aimed to evaluate the clinical utility of incorporating NGS results in the management of GBM patients at a clinical trials-focused academic center.

METHODS

We identified 1011 consecutive adult patients with pathologically confirmed GBM (IDHwt or IDHmut) who had somatic tumor sequencing (Oncopanel, ~500 cancer gene panel) at DFCI from 2013-2019. Clinical records of all IDHwt GBM patients were reviewed to capture clinical trial enrollment and off-label targeted therapy use based on NGS results.

RESULTS

Of the 557 IDHwt GBM patients with sequencing, 182 entered clinical trials at diagnosis (32.7%) and 213 (38.2%) entered after recurrence. Sequencing results for 130 patients (23.3%) were utilized for clinical trial enrollment for either targeted therapy indications (6.9 % upfront and 27.7% at recurrent clinical trials and 3.1% for off-label targeted therapy) or exploratory studies (55.4% upfront and 6.9% recurrent clinical trials). Median overall survival was 20.1 months with no survival difference seen between patients enrolled in clinical trials compared to those who were not, in a posthoc analysis.

CONCLUSIONS

While NGS testing has become essential for improved molecular diagnostics, our study illustrates that targeted gene panels remain underutilized for selecting therapy in GBM-IDHwt. Targeted therapy and clinical trial design remain to be improved to help leverage the potential of NGS in clinical care.

Intracranial complications of hypercoagulability and superinfection in the setting of COVID-19: illustrative cases

BACKGROUND

Hypercoagulability with thrombosis and associated inflammation has been well-documented in COVID-19, and catastrophic cerebral venous sinus thromboses (CVSTs) have been described. Another COVID-19–related complication is bacterial superinfection, including sinusitis. Here, the authors reported three cases of COVID-19–associated sinusitis, meningitis, and CVST and summarized the literature about septic intracranial thrombotic events as a cause of headache and fever in COVID-19.

OBSERVATIONS

The authors described three adolescent patients with no pertinent past medical history and no prior COVID-19 vaccinations who presented with subacute headaches, photosensitivity, nausea, and vomiting after testing positive for COVID-19. Imaging showed subdural collections, CVST, cerebral edema, and severe sinus disease. Two patients had decline in mental status and progression of neurological symptoms. In all three, emergency cranial and sinonasal washouts uncovered pus that grew polymicrobial cultures. After receiving broad-spectrum antimicrobials and various additional treatments, including two of three patients receiving anticoagulation, all patients eventually became neurologically intact with varying ongoing sequelae.

LESSONS

These cases demonstrated similar original presentations among previously healthy adolescents with COVID-19 infections, concurrent sinusitis precipitating CVST, and subdural empyemas. Better recognition and understanding of the multisystem results of severe acute respiratory syndrome coronavirus 2 and the complicated sequelae allows for proper treatment.

Tumor-Associated Macrophages/Microglia in Glioblastoma Oncolytic Virotherapy: A Double-Edged Sword

Oncolytic virotherapy is a rapidly progressing field that uses oncolytic viruses (OVs) to selectively infect malignant cells and cause an antitumor response through direct oncolysis and stimulation of the immune system. Despite demonstrated pre-clinical efficacy of OVs in many cancer types and some favorable clinical results in glioblastoma (GBM) trials, durable increases in overall survival have remained elusive. Recent evidence has emerged that tumor-associated macrophage/microglia (TAM) involvement is likely an important factor contributing to OV treatment failure. It is prudent to note that the relationship between TAMs and OV therapy failures is complex. Canonically activated TAMs (i.e., M1) drive an antitumor response while also inhibiting OV replication and spread. Meanwhile, M2 activated TAMs facilitate an immunosuppressive microenvironment thereby indirectly promoting tumor growth. In this focused review, we discuss the complicated interplay between TAMs and OV therapies in GBM. We review past studies that aimed to maximize effectiveness through immune system modulation-both immunostimulatory and immunosuppressant-and suggest future directions to maximize OV efficacy.

BIOM-30. BLOOD COUNTS THROUGH TREATMENT COURSE IN NEWLY DIAGNOSED GLIOBLASTOMA PATIENTS RECEIVING CHEMORADIATION

BACKGROUND

Glioblastoma (GBM) patients are treated with radiation therapy (RT), temozolomide, and corticosteroids which can affect hematologic and immunologic parameters. We examined lymphocytes, neutrophil-to-lymphocyte ratio and platelet measurements and their association with progression-free survival (PFS) overall survival (OS).

METHODS

We identified 759 newly diagnosed adult GBM patients treated at our institution in the temozolomide (TMZ) era with blood counts that could be automatically extracted from the electronic medical record during chemoradiation (CRT, defined as within 42 days of RT) and at first recurrence. Linear regression and Cox modeling were used to evaluate outcomes.

RESULTS

Median age was 60.3 years; 87% had KPS ≥ 70, 37.5% had gross total resection, and 90% received TMZ. Prior to RT, 56.4% (375/665) patients had a lymphocyte measurement &lt; 1.0 × 1000 cells [K]/μL. Within 42 days of CRT, 81.7% (536/656) had a lymphocyte measurement &lt; 1.0 K/μL, 37.8% (248/656) &lt; 0.5 K/μL. 10.7% (58/544) patients developed grade 2 or higher neutropenia, 9.1% (50/547) patients developed grade 2 or higher thrombocytopenia. On multivariable analysis (MVA), older age (AHR1.03, p&lt; 0.001), unmethylated MGMT status (AHR2.56,p&lt; 0.001), lower RT dose (&lt;54Gy, AHR 3.45, p&lt; 0.001), male sex (AHR1.45, p=0.02), non-gross total resection (AHR1.63, p&lt; 0.001), lymphopenia during CRT (AHR0.63, p=0.008) and higher NLR during CRT (AHR1.02, p=0.001) were significantly associated with worse OS.

Older age (AHR1.01, p=0.02), unmethylated MGMT status (AHR2.44, p&lt; 0.001), lower RT dose (AHR1.82, p=0.02), higher NLR during CRT (AHR1.03, p &lt; 0.001) were significantly associated with worse PFS on MVA. At first recurrence, median lymphocyte count was 0.7 K/μL with 74% (348/468) patients &lt; 1.0 K/μL and 27% &lt; 0.5 K/μL.

CONCLUSION

Lymphopenia and higher neutrophil-to-lymphocyte ratio are associated with inferior outcomes. Persistent lymphopenia at time of first recurrence may have implications for clinical trial eligibility and immunotherapy approaches in recurrent GBM.

CTNI-40. EVALUATING FEASIBILITY AND EFFICIENCY OF PHASE II ADAPTIVE PLATFORM TRIAL DESIGNS BASED ON THE INDIVIDUALIZED SCREENING TRIAL OF INNOVATIVE GLIOBLASTOMA THERAPY (INSIGhT) EXPERIENCE

BACKGROUND

The Individualized Screening Trial of Innovative Glioblastoma Therapy (INSIGhT) is a phase II platform trial with Bayesian adaptive randomization and deep genomic profiling to more efficiently test experimental agents in newly diagnosed glioblastoma and to prioritize therapies for late-stage testing.

METHODS

In the ongoing INSIGhT trial, patients with newly diagnosed MGMT-unmethylated glioblastoma are randomized to the control arm or one of three experimental therapy arms (CC-115, abemaciclib, and neratinib). The control arm therapy is radiotherapy with concomitant and adjuvant temozolomide, and primary endpoint is overall survival. Randomization has been adapted based on Bayesian estimation of biomarker-specific probability of treatment impact on progression-free survival (PFS). All tumors undergo detailed molecular sequencing, and this is facilitated with the companion ALLELE protocol. To evaluate feasibility of this approach, we assessed the status of this ongoing trial.

RESULTS

Since INSIGhT was activated 4.3 years ago, it has expanded to include 12 sites across the United States. A total of 247 patients have been enrolled. Randomization probabilities have been repeatedly adjusted over time based upon early PFS results to alter the randomization ratio from standard 1:1:1:1 randomization. All three arms have completed accrual and efficacy estimates are available based upon comparison to the common control arm in context of relevant biomarkers. There are 87 patients alive and in follow-up, and there are ongoing plans to add additional arms to evaluate further treatments in the future.

CONCLUSION

The INSIGhT trial demonstrates that a multi-center Bayesian adaptive platform trial is a feasible and effective approach to help prioritize therapies and biomarkers for newly diagnosed GBM. The trial has maintained robust accrual, and the simultaneous testing of multiple agents, sharing a common control arm and adaptive randomization serve as features to increase trial efficiency relative to traditional clinical trial designs.

NIMG-24. RANO CRITERIA DETECTS EARLY PROGRESSION SOONER THAN MODIFIED RANO CRITERIA IN PATIENTS WITH NEWLY DIAGNOSED GLIOBLASTOMA

BACKGROUND

Accurate response criteria are crucial for determining treatment efficacy. The response assessment in neuro-oncology (RANO) criteria was developed to standardize response assessment in neuro-oncology. Modified RANO (m-RANO) criteria were recently proposed to address some limitations of the initial criteria including the use of a post-radiation baseline and an additional scan to confirm progression. We sought to identify differences in the association of progression-free survival (PFS) and overall survival (OS) using RANO and m-RANO criteria.

METHODS

We conducted a retrospective review of newly diagnosed glioblastoma (GBM) patients treated at Dana-Farber Cancer Institute from January 2013 until December 2019. Patients with available clinical and imaging data obtained before initiation of treatment, after radiation completion and at intervals of 1 to 3 months were included. MRIs were evaluated by two independent readers, and PD dates determined using RANO and m-RANO criteria.

RESULTS

552 patients were included. 97.1% of the tumors were IDH wild-type. MGMT promoter was unmethylated in 51.4%, methylated in 35.1% and undetermined in 8.5%. Median OS among patients was 18.1 months. 72 patients (13%) did not have PD at the end of the study. 83 patients had treatment change while being clinically stable and without a confirmation scan and were excluded from the final analysis. PFS was 8.2 months with RANO and 8.4 months with mRANO. Difference in PD dates between RANO and m-RANO was detected in 76 patients (14%), where PFS was 3.5 months with RANO and 5.1 months with m-RANO. These patients had a worse median OS than those with identical RANO and m-RANO PD dates (15.2 vs. 22.4 months, p&lt; 0.0001).

CONCLUSION

RANO and m-RANO criteria resulted in identical PFS for most patients. 14% of patients had discordant PD dates and a worse prognosis. These patients progressed early, and their PD was identified sooner with RANO criteria.

CTNI-05. PRELIMINARY RESULTS OF THE NERATINIB ARM IN THE INDIVIDUALIZED SCREENING TRIAL OF INNOVATIVE GLIOBLASTOMA THERAPY (INSIGHT): A PHASE II PLATFORM TRIAL USING BAYESIAN ADAPTIVE RANDOMIZATION

BACKGROUND

EGFR is amplified in over 50% of glioblastoma and 20-30% have EGFRvIII mutations. Neratinib is a potent inhibitor of EGFR/HER2 approved for metastatic HER2+ breast cancer. To efficiently evaluate the potential impact of neratinib on overall survival (OS) in newly-diagnosed glioblastoma and to simultaneously develop information regarding potential genomic biomarker associations, neratinib was included as an arm on the Individualized Screening Trial of Innovative Glioblastoma Therapy (INSIGhT) trial. INSIGhT is a phase II platform trial using response adaptive randomization and deep genomic profiling to more efficiently test experimental agents in MGMT unmethylated glioblastoma and accelerate identification of novel therapies for phase III testing. Initial randomization was equal between neratinib, control, and two other experimental arms but subsequent randomization was adapted based on efficacy as determined by progression-free survival (PFS). We report preliminary results for the neratinib arm.

METHODS

Patients with newly diagnosed MGMT-unmethylated glioblastoma were randomized to receive either radiotherapy with concomitant and adjuvant temozolomide or standard radiochemotherapy followed by adjuvant neratinib (240 mg daily). Treatment continued until progression or development of unacceptable toxicities. The primary endpoint was OS. Association between neratinib efficacy and EGFR amplification was also investigated.

RESULTS

There were 144 patients (70 control; 74 neratinib). Neratinib was reasonably well-tolerated with no new toxicity signals identified. PFS was compared (HR 0.84; p=0.38, logrank test – not significant) between the neratinib (median 6.05 months) and control (median 5.82 months) arms. For patients EGFR pathway activation the PFS HR was 0.53 (p-value=0.03 – significant, median PFS: neratinib, 6.21 months, control, 5.26 months). However, there was no significant improvement in OS in EGFR amplified/mutated patients (HR 1.05; p-value 0.87) between neratinib (median 14.2) compared to the control arm (median 14.6).

CONCLUSION

Neratinib prolonged PFS in the EGFR positive subpopulation but there was no overall PFS benefit, or any OS improvement.

Deep Learning for Adjacent Segment Disease at Preoperative MRI for Cervical Radiculopathy

Background Patients who undergo surgery for cervical radiculopathy are at risk for developing adjacent segment disease (ASD). Identifying patients who will develop ASD remains challenging for clinicians. Purpose To develop and validate a deep learning algorithm capable of predicting ASD by using only preoperative cervical MRI in patients undergoing single-level anterior cervical diskectomy and fusion (ACDF). Materials and Methods In this Health Insurance Portability and Accountability Act-compliant study, retrospective chart review was performed for 1244 patients undergoing single-level ACDF in two tertiary care centers. After application of inclusion and exclusion criteria, 344 patients were included, of whom 60% (n = 208) were used for training and 40% for validation (n = 43) and testing (n = 93). A deep learning-based prediction model with 48 convolutional layers was designed and trained by using preoperative T2-sagittal cervical MRI. To validate model performance, a neuroradiologist and neurosurgeon independently provided ASD predictions for the test set. Validation metrics included accuracy, areas under the curve, and F1 scores. The difference in proportion of wrongful predictions between the model and clinician was statistically tested by using the McNemar test. Results A total of 344 patients (median age, 48 years; interquartile range, 41-58 years; 182 women) were evaluated. The model predicted ASD on the 93 test images with an accuracy of 88 of 93 (95%; 95% CI: 90, 99), sensitivity of 12 of 15 (80%; 95% CI: 60, 100), and specificity of 76 of 78 (97%; 95% CI: 94, 100). The neuroradiologist and neurosurgeon provided predictions with lower accuracy (54 of 93; 58%; 95% CI: 48, 68), sensitivity (nine of 15; 60%; 95% CI: 35, 85), and specificity (45 of 78; 58%; 95% CI: 56, 77) compared with the algorithm. The McNemar test on the contingency table demonstrated that the proportion of wrongful predictions was significantly lower by the model (test statistic, 2.000; P < .001). Conclusion A deep learning algorithm that used only preoperative cervical T2-weighted MRI outperformed clinical experts at predicting adjacent segment disease in patients undergoing surgery for cervical radiculopathy. © RSNA, 2021 An earlier incorrect version appeared online. This article was corrected on September 22, 2021.

Automating Clinical Chart Review: An Open-Source Natural Language Processing Pipeline Developed on Free-Text Radiology Reports From Patients With Glioblastoma

PURPOSE

The aim of this study was to develop an open-source natural language processing (NLP) pipeline for text mining of medical information from clinical reports. We also aimed to provide insight into why certain variables or reports are more suitable for clinical text mining than others.

MATERIALS AND METHODS

Various NLP models were developed to extract 15 radiologic characteristics from free-text radiology reports for patients with glioblastoma. Ten-fold cross-validation was used to optimize the hyperparameter settings and estimate model performance. We examined how model performance was associated with quantitative attributes of the radiologic characteristics and reports.

RESULTS

In total, 562 unique brain magnetic resonance imaging reports were retrieved. NLP extracted 15 radiologic characteristics with high to excellent discrimination (area under the curve, 0.82 to 0.98) and accuracy (78.6% to 96.6%). Model performance was correlated with the inter-rater agreement of the manually provided labels (ρ = 0.904; P < .001) but not with the frequency distribution of the variables of interest (ρ = 0.179; P = .52). All variables labeled with a near perfect inter-rater agreement were classified with excellent performance (area under the curve > 0.95). Excellent performance could be achieved for variables with only 50 to 100 observations in the minority group and class imbalances up to a 9:1 ratio. Report-level classification accuracy was not associated with the number of words or the vocabulary size in the distinct text documents.

CONCLUSION

This study provides an open-source NLP pipeline that allows for text mining of narratively written clinical reports. Small sample sizes and class imbalance should not be considered as absolute contraindications for text mining in clinical research. However, future studies should report measures of inter-rater agreement whenever ground truth is based on a consensus label and use this measure to identify clinical variables eligible for text mining.

Therapeutic cancer vaccines for pediatric malignancies: advances, challenges, and emerging technologies

Though outcomes for pediatric cancer patients have significantly improved over the past several decades, too many children still experience poor outcomes and survivors suffer lifelong, debilitating late effects after conventional chemotherapy, radiation, and surgical treatment. Consequently, there has been a renewed focus on developing novel targeted therapies to improve survival outcomes. Cancer vaccines are a promising type of immunotherapy that leverage the immune system to mediate targeted, tumor-specific killing through recognition of tumor antigens, thereby minimizing off-target toxicity. As such, cancer vaccines are orthogonal to conventional cancer treatments and can therefore be used alone or in combination with other therapeutic modalities to maximize efficacy. To date, cancer vaccination has remained largely understudied in the pediatric population. In this review, we discuss the different types of tumor antigens and vaccine technologies (dendritic cells, peptides, nucleic acids, and viral vectors) evaluated in clinical trials, with a focus on those used in children. We conclude with perspectives on how advances in combination therapies, tumor antigen (eg, neoantigen) selection, and vaccine platform optimization can be translated into clinical practice to improve outcomes for children with cancer.

SURG-02. SURVIVAL PREDICTION AFTER NEUROSURGICAL RESECTION OF BRAIN METASTASES: A MACHINE LEARNING APPROACH

INTRODUCTION

Survival prediction in brain metastases (BMs) remains challenging. Current prognostic models have been created and validated almost completely with data from patients receiving radiotherapy only, leaving uncertainty about surgical patients. Therefore, the aim of this study was to build and validate a model predicting 6-month survival after BM resection using different machine learning (ML) algorithms.

METHODS

An institutional database of 1062 patients who underwent resection for BM was split into a 80:20 training and testing set. Seven different ML algorithms were trained and assessed for performance. Moreover, an ensemble model was created incorporating random forest, adaptive boosting, gradient boosting, and logistic regression algorithms. Five-fold cross validation was used for hyperparameter tuning. Model performance was assessed using area under the receiver-operating curve (AUC) and calibration and was compared against the diagnosis-specific graded prognostic assessment (ds-GPA); the most established prognostic model in BMs.

RESULTS

The ensemble model showed superior performance with an AUC of 0.81 in the hold-out test set, a calibration slope of 1.14, and a calibration intercept of -0.08, outperforming the ds-GPA (AUC 0.68). Patients were stratified into high-, medium- and low-risk groups for death at 6 months; these strata strongly predicted both 6-months and longitudinal overall survival (p &lt; 0.001).

CONCLUSIONS

We developed and internally validated an ensemble ML model that accurately predicts 6-month survival after neurosurgical resection for BM, outperforms the most established model in the literature, and allows for meaningful risk stratification. Future efforts should focus on external validation of our model.

CTNI-11. CC-115 IN NEWLY DIAGNOSED MGMT UNMETHYLATED GLIOBLASTOMA IN THE INDIVIDUALIZED SCREENING TRIAL OF INNOVATIVE GLIOBLASTOMA THERAPY (INSIGHT): A PHASE II RANDOMIZED BAYESIAN ADAPTIVE PLATFORM TRIAL

BACKGROUND

CC-115 is an oral, CNS-penetrant, selective inhibitor of mammalian target of rapamycin kinase (mTOR) and deoxyribonucleic acid-dependent protein kinase (DNA-PK). Both targets are important in glioblastoma; PI3K/Akt/mTOR signaling is hyperactive in most glioblastomas, and DNA-PK is integral to repair of radiotherapy-mediated DNA damage. To investigate CC-115 in newly diagnosed glioblastoma and explore potential genomic biomarker associations, CC-115 was evaluated in the Individualized Screening Trial of Innovative Glioblastoma Therapy (INSIGhT) trial, an adaptive platform trial designed to efficiently test experimental agents.

METHODS

Adults with newly diagnosed MGMT-unmethylated glioblastoma, with genomic data available, are eligible for this ongoing trial. Patients are adaptively randomized to one of several experimental arms or the control arm: standard radiotherapy with concurrent and adjuvant temozolomide. The primary endpoint is overall survival (OS). Patients randomized to CC-115 (10mg po BID) received it concurrently with radiotherapy and as adjuvant monotherapy. As the first in-human use of CC-115 with radiation, a safety lead-in 3 + 3 design was used.

RESULTS

Twelve patients were randomized to CC-115; seven patients had possible treatment-related CTCAE grade &gt; 3 toxicity, including four pre-specified dose-limiting toxicities: liver function abnormality (n=1), hyperlipidemia (n=1), lipase elevation (n=1) and cerebral edema (n=1). There was no significant difference in progression-free survival (PFS, median 4.2 months [CC-115] vs. 5.2 months, p=0.9) or OS (median 10.1 months [CC-115] vs. 14.5 months, p=0.9) compared to the 50 patients randomized to the control arm. Based on early PFS results, randomization probability to CC-115 decreased from 25% to &lt; 10% at time of the trial arm closure.

CONCLUSION

Concurrent and adjuvant CC-115 was associated with toxicity and failed to improve PFS or OS. The INSIGhT trial design allowed for more efficient testing of CC-115, decreasing patients and resources allocated to a therapy that was discontinued due to concerns about toxicity and unfavorable risk-to-benefit ratio.

CTNI-12. PRELIMINARY RESULTS OF THE ABEMACICLIB ARM IN THE INDIVIDUALIZED SCREENING TRIAL OF INNOVATIVE GLIOBLASTOMA THERAPY (INSIGHT): A PHASE II PLATFORM TRIAL USING BAYESIAN ADAPTIVE RANDOMIZATION

BACKGROUND

The cyclin D-CDK4/6-Rb pathway is activated in most glioblastomas. Abemaciclib is a potent CDK4/6 inhibitor with good brain penetration approved for ER/PR/HER2- breast cancer. In order to efficiently evaluate the potential impact of abemaciclib on overall survival (OS) in newly diagnosed glioblastoma and to simultaneously develop information regarding potential genomic biomarker associations, abemaciclib was included as an arm on the Individualized Screening Trial of Innovative Glioblastoma Therapy (INSIGhT) trial. INSIGhT is a phase II platform trial using response adaptive randomization and deep genomic profiling to more efficiently test experimental agents in MGMT unmethylated glioblastoma and potentially accelerate identification of novel therapies for phase III testing. Initial randomization was equal between abemaciclib, control, and two other experimental arms but subsequent randomization was adapted based on efficacy as determined by progression-free survival (PFS). Ineffective arms were discontinued and new arms added by protocol amendment. We report preliminary results for the abemaciclib arm which has completed accrual.

METHODS

Patients with newly diagnosed MGMT-unmethylated glioblastoma were randomized to receive either radiotherapy with concomitant and adjuvant temozolomide at standard doses or standard radiochemotherapy followed by adjuvant abemaciclib (150–200 mg orally BID) without temozolomide. Treatment continued until progression or development of unacceptable toxicities. The primary endpoint was OS. Association between abemaciclib efficacy and cyclin D-CDK4/6-Rb pathway genomic alterations was also investigated.

RESULTS

There were 123 patients (50 control; 73 treated with abemaciclib). Abemaciclib was generally well-tolerated with no new toxicity signals identified. PFS was significantly longer (p=0.03, logrank test) with abemaciclib (median 6.31 months 95% CI [5.29, 8.18]) compared to the control arm (5.16 months 95% CI [4.37, 6.28]). 28/50 control and 36/73 abemaciclib patients remain alive.

CONCLUSION

Preliminary analysis suggests that abemaciclib increases PFS compared to control. Updated toxicity, PFS and survival data and potential genomic biomarker associations will be presented.

Survival prediction of glioblastoma patients-are we there yet? A systematic review of prognostic modeling for glioblastoma and its clinical potential

Glioblastoma is associated with a poor prognosis. Even though survival statistics are well-described at the population level, it remains challenging to predict the prognosis of an individual patient despite the increasing number of prognostic models. The aim of this study is to systematically review the literature on prognostic modeling in glioblastoma patients. A systematic literature search was performed to identify all relevant studies that developed a prognostic model for predicting overall survival in glioblastoma patients following the PRISMA guidelines. Participants, type of input, algorithm type, validation, and testing procedures were reviewed per prognostic model. Among 595 citations, 27 studies were included for qualitative review. The included studies developed and evaluated a total of 59 models, of which only seven were externally validated in a different patient cohort. The predictive performance among these studies varied widely according to the AUC (0.58-0.98), accuracy (0.69-0.98), and C-index (0.66-0.70). Three studies deployed their model as an online prediction tool, all of which were based on a statistical algorithm. The increasing performance of survival prediction models will aid personalized clinical decision-making in glioblastoma patients. The scientific realm is gravitating towards the use of machine learning models developed on high-dimensional data, often with promising results. However, none of these models has been implemented into clinical care. To facilitate the clinical implementation of high-performing survival prediction models, future efforts should focus on harmonizing data acquisition methods, improving model interpretability, and externally validating these models in multicentered, prospective fashion.

The Surgical Resection of Brainstem Glioma: Outcomes and Prognostic Factors

BACKGROUND

The management of brainstem glioma remains controversial, with increasing evidence supporting surgical resection as the primary treatment for a select subgroup of tumors. However, there remains no consensus on the specific benefits and risks, the selection of surgical candidates, and prognostic factors that may further refine surgical indications.

METHODS

A retrospective single-surgeon chart review was performed for all patients who underwent surgical treatment for radiographically suspected brainstem glioma between 2000 and 2017. Preoperative and postoperative radiographic evaluations on magnetic resonance imaging were conducted. Survival outcomes were collected, and machine-learning techniques were used for multivariate analysis.

RESULTS

Seventy-seven patients with surgical treatment of brainstem glioma were identified, with a median age of 9 years (range, 0-58 years). The cohort included 64% low-grade (I and II) and 36% high-grade (III and IV) tumors. For all patients, the 1-year and 5-year overall survival were 76.4% and 62.3%, respectively. Transient neurologic deficit was present in 34% of cases, and permanent deficit in a further 29%.

CONCLUSIONS

The radical surgical resection of brainstem gliomas can be performed with acceptable risk in well-selected cases and likely confers survival advantage for what is otherwise a rapidly and universally fatal disease. Various radiographic features are useful during patient selection and may guide treatment selection.

Natural Language Processing for Automated Quantification of Brain Metastases Reported in Free-Text Radiology Reports

PURPOSE

Although the bulk of patient-generated health data are increasing exponentially, their use is impeded because most data come in unstructured format, namely as free-text clinical reports. A variety of natural language processing (NLP) methods have emerged to automate the processing of free text ranging from statistical to deep learning-based models; however, the optimal approach for medical text analysis remains to be determined. The aim of this study was to provide a head-to-head comparison of novel NLP techniques and inform future studies about their utility for automated medical text analysis.

PATIENTS AND METHODS

Magnetic resonance imaging reports of patients with brain metastases treated in two tertiary centers were retrieved and manually annotated using a binary classification (single metastasis v two or more metastases). Multiple bag-of-words and sequence-based NLP models were developed and compared after randomly splitting the annotated reports into training and test sets in an 80:20 ratio.

RESULTS

A total of 1,479 radiology reports of patients diagnosed with brain metastases were retrieved. The least absolute shrinkage and selection operator (LASSO) regression model demonstrated the best overall performance on the hold-out test set with an area under the receiver operating characteristic curve of 0.92 (95% CI, 0.89 to 0.94), accuracy of 83% (95% CI, 80% to 87%), calibration intercept of -0.06 (95% CI, -0.14 to 0.01), and calibration slope of 1.06 (95% CI, 0.95 to 1.17).

CONCLUSION

Among various NLP techniques, the bag-of-words approach combined with a LASSO regression model demonstrated the best overall performance in extracting binary outcomes from free-text clinical reports. This study provides a framework for the development of machine learning-based NLP models as well as a clinical vignette of patients diagnosed with brain metastases.

Severe Radiation Necrosis Refractory to Surgical Resection in Patients with Melanoma and Brain Metastases Managed with Ipilimumab/Nivolumab and Brain-Directed Stereotactic Radiation Therapy

BACKGROUND

The use of targeted therapies and immune checkpoint inhibitors has drastically changed the management of patients with melanoma and brain metastases. Specifically, combination therapy with ipilimumab, a cytotoxic T-lymphocyte antigen 4 inhibitor, and nivolumab, a programmed cell death protein 1 inhibitor, has become a preferred systemic therapy option for patients with melanoma and asymptomatic brain metastases. However, the efficacy and toxicity profile of these agents in combination with brain-directed radiation therapy is not well described.

CASE DESCRIPTION

In this case series, we highlight a series of patients with melanoma demonstrating severe radiation necrosis immediately refractory to surgical resection following brain-directed stereotactic radiation therapy with concurrent ipilimumab and nivolumab. Three patients described in this series each received stereotactic radiation therapy to a dose of 30 Gy in 5 fractions to a melanoma brain metastasis. These areas developed radiographic evidence of necrosis, which was managed surgically and progressed immediately and rapidly after resection. Re-resection, bevacizumab, steroids, and/or discontinuation of nivolumab was used to mitigate further necrosis with varying efficacy.

CONCLUSIONS

Patients with metastatic melanoma receiving brain-directed radiation therapy with concurrent ipilimumab and nivolumab are at risk for developing severe, surgically refractory radiation necrosis and should be closely followed clinically and with imaging. The exact mechanism for such severe necrosis is unknown, and future studies are needed to better understand this pathophysiology and identify optimal treatment strategies.

Fibrinolytics and Intraventricular Hemorrhage: A Systematic Review and Meta-analysis

Intraventricular hemorrhage (IVH) is an independent poor prognostic factor in subarachnoid and intra-parenchymal hemorrhage. The use of intraventricular fibrinolytics (IVF) has long been debated, and its exact effects on outcomes are unknown. A systematic review and meta-analysis were performed in accordance with the PRISMA guidelines to assess the impact of IVF after non-traumatic IVH on mortality, functional outcome, intracranial bleeding, ventriculitis, time until clearance of third and fourth ventricles, obstruction of external ventricular drains (EVD), and shunt dependency. Nineteen studies were included in the meta-analysis, totaling 1020 patients. IVF was associated with lower mortality (relative risk [RR] 0.58; 95% confidence interval [CI] 0.47-0.72), fewer EVD obstructions (RR 0.41; 95% CI 0.22-0.74), and a shorter time until clearance of the ventricles (median difference [MD] - 4.05 days; 95% CI - 5.52 to - 2.57). There was no difference in good functional outcome, RR 1.41 (95% CI 0.98-2.03), or shunt dependency, RR 0.93 (95% CI 0.70-1.22). Correction for publication bias predicted an increased risk of intracranial bleeding, RR 1.67 (95% CI 1.01-2.74) and a lower risk of ventriculitis, RR 0.68 (95% CI 0.45-1.03) in IVH patients treated with IVF. IVF was associated with improved survival, faster clearance of blood from the ventricles and fewer drain obstructions, but further research is warranted to elucidate the effects on ventriculitis, long-term functional outcomes, and re-hemorrhage.

An Online Calculator for the Prediction of Survival in Glioblastoma Patients Using Classical Statistics and Machine Learning

BACKGROUND

Although survival statistics in patients with glioblastoma multiforme (GBM) are well-defined at the group level, predicting individual patient survival remains challenging because of significant variation within strata.

OBJECTIVE

To compare statistical and machine learning algorithms in their ability to predict survival in GBM patients and deploy the best performing model as an online survival calculator.

METHODS

Patients undergoing an operation for a histopathologically confirmed GBM were extracted from the Surveillance Epidemiology and End Results (SEER) database (2005-2015) and split into a training and hold-out test set in an 80/20 ratio. Fifteen statistical and machine learning algorithms were trained based on 13 demographic, socioeconomic, clinical, and radiographic features to predict overall survival, 1-yr survival status, and compute personalized survival curves.

RESULTS

In total, 20 821 patients met our inclusion criteria. The accelerated failure time model demonstrated superior performance in terms of discrimination (concordance index = 0.70), calibration, interpretability, predictive applicability, and computational efficiency compared to Cox proportional hazards regression and other machine learning algorithms. This model was deployed through a free, publicly available software interface (https://cnoc-bwh.shinyapps.io/gbmsurvivalpredictor/).

CONCLUSION

The development and deployment of survival prediction tools require a multimodal assessment rather than a single metric comparison. This study provides a framework for the development of prediction tools in cancer patients, as well as an online survival calculator for patients with GBM. Future efforts should improve the interpretability, predictive applicability, and computational efficiency of existing machine learning algorithms, increase the granularity of population-based registries, and externally validate the proposed prediction tool.

Automatic assessment of glioma burden: a deep learning algorithm for fully automated volumetric and bidimensional measurement

BACKGROUND

Longitudinal measurement of glioma burden with MRI is the basis for treatment response assessment. In this study, we developed a deep learning algorithm that automatically segments abnormal fluid attenuated inversion recovery (FLAIR) hyperintensity and contrast-enhancing tumor, quantitating tumor volumes as well as the product of maximum bidimensional diameters according to the Response Assessment in Neuro-Oncology (RANO) criteria (AutoRANO).

METHODS

Two cohorts of patients were used for this study. One consisted of 843 preoperative MRIs from 843 patients with low- or high-grade gliomas from 4 institutions and the second consisted of 713 longitudinal postoperative MRI visits from 54 patients with newly diagnosed glioblastomas (each with 2 pretreatment "baseline" MRIs) from 1 institution.

RESULTS

The automatically generated FLAIR hyperintensity volume, contrast-enhancing tumor volume, and AutoRANO were highly repeatable for the double-baseline visits, with an intraclass correlation coefficient (ICC) of 0.986, 0.991, and 0.977, respectively, on the cohort of postoperative GBM patients. Furthermore, there was high agreement between manually and automatically measured tumor volumes, with ICC values of 0.915, 0.924, and 0.965 for preoperative FLAIR hyperintensity, postoperative FLAIR hyperintensity, and postoperative contrast-enhancing tumor volumes, respectively. Lastly, the ICCs for comparing manually and automatically derived longitudinal changes in tumor burden were 0.917, 0.966, and 0.850 for FLAIR hyperintensity volume, contrast-enhancing tumor volume, and RANO measures, respectively.

CONCLUSIONS

Our automated algorithm demonstrates potential utility for evaluating tumor burden in complex posttreatment settings, although further validation in multicenter clinical trials will be needed prior to widespread implementation.

Contemporary assessment of extent of resection in molecularly defined categories of diffuse low-grade glioma: a volumetric analysis

OBJECTIVE

While the effect of increased extent of resection (EOR) on survival in diffuse infiltrating low-grade glioma (LGG) patients is well established, there is still uncertainty about the influence of the new WHO molecular subtypes. The authors designed a retrospective analysis to assess the interplay between EOR and molecular classes.

METHODS

The authors retrospectively reviewed the records of 326 patients treated surgically for hemispheric WHO grade II LGG at Brigham and Women's Hospital and Massachusetts General Hospital (2000-2017). EOR was calculated volumetrically and Cox proportional hazards models were built to assess for predictive factors of overall survival (OS), progression-free survival (PFS), and malignant progression-free survival (MPFS).

RESULTS

There were 43 deaths (13.2%; median follow-up 5.4 years) among 326 LGG patients. Median preoperative tumor volume was 31.2 cm3 (IQR 12.9-66.0), and median postoperative residual tumor volume was 5.8 cm3 (IQR 1.1-20.5). On multivariable Cox regression, increasing postoperative volume was associated with worse OS (HR 1.02 per cm3; 95% CI 1.00-1.03; p = 0.016), PFS (HR 1.01 per cm3; 95% CI 1.00-1.02; p = 0.001), and MPFS (HR 1.01 per cm3; 95% CI 1.00-1.02; p = 0.035). This result was more pronounced in the worse prognosis subtypes of IDH-mutant and IDH-wildtype astrocytoma, for which differences in survival manifested in cases with residual tumor volume of only 1 cm3. In oligodendroglioma patients, postoperative residuals impacted survival when exceeding 8 cm3. Other significant predictors of OS were age at diagnosis, IDH-mutant and IDH-wildtype astrocytoma classes, adjuvant radiotherapy, and increasing preoperative volume.

CONCLUSIONS

The results corroborate the role of EOR in survival and malignant transformation across all molecular subtypes of diffuse LGG. IDH-mutant and IDH-wildtype astrocytomas are affected even by minimal postoperative residuals and patients could potentially benefit from a more aggressive surgical approach.

Natural and Artificial Intelligence in Neurosurgery: A Systematic Review

BACKGROUND

Machine learning (ML) is a domain of artificial intelligence that allows computer algorithms to learn from experience without being explicitly programmed.

OBJECTIVE

To summarize neurosurgical applications of ML where it has been compared to clinical expertise, here referred to as "natural intelligence."

METHODS

A systematic search was performed in the PubMed and Embase databases as of August 2016 to review all studies comparing the performance of various ML approaches with that of clinical experts in neurosurgical literature.

RESULTS

Twenty-three studies were identified that used ML algorithms for diagnosis, presurgical planning, or outcome prediction in neurosurgical patients. Compared to clinical experts, ML models demonstrated a median absolute improvement in accuracy and area under the receiver operating curve of 13% (interquartile range 4-21%) and 0.14 (interquartile range 0.07-0.21), respectively. In 29 (58%) of the 50 outcome measures for which a P-value was provided or calculated, ML models outperformed clinical experts (P < .05). In 18 of 50 (36%), no difference was seen between ML and expert performance (P > .05), while in 3 of 50 (6%) clinical experts outperformed ML models (P < .05). All 4 studies that compared clinicians assisted by ML models vs clinicians alone demonstrated a better performance in the first group.

CONCLUSION

We conclude that ML models have the potential to augment the decision-making capacity of clinicians in neurosurgical applications; however, significant hurdles remain associated with creating, validating, and deploying ML models in the clinical setting. Shifting from the preconceptions of a human-vs-machine to a human-and-machine paradigm could be essential to overcome these hurdles.