Combined cytotoxic and immune-stimulatory gene therapy for primary adult high-grade glioma: a phase 1, first-in-human trial.

BACKGROUND: High-grade gliomas have a poor prognosis and do not respond well to treatment. Effective cancer immune responses depend on functional immune cells, which are typically absent from the brain. This study aimed to evaluate the safety and activity of two adenoviral vectors expressing HSV1-TK (Ad-hCMV-TK) and Flt3L (Ad-hCMV-Flt3L) in patients with high-grade glioma. METHODS: In this dose-finding, first-in-human trial, treatment-naive adults aged 18-75 years with newly identified high-grade glioma that was evaluated per immunotherapy response assessment in neuro-oncology criteria, and a Karnofsky Performance Status score of 70 or more, underwent maximal safe resection followed by injections of adenoviral vectors expressing HSV1-TK and Flt3L into the tumour bed. The study was conducted at the University of Michigan Medical School, Michigan Medicine (Ann Arbor, MI, USA). The study included six escalating doses of viral particles with starting doses of 1×1010 Ad-hCMV-TK viral particles and 1×109 Ad-hCMV-Flt3L viral particles (cohort A), and then 1×1011 Ad-hCMV-TK viral particles and 1×109 Ad-hCMV-Flt3L viral particles (cohort B), 1×1010 Ad-hCMV-TK viral particles and 1×1010 Ad-hCMV-Flt3L viral particles (cohort C), 1×1011 Ad-hCMV-TK viral particles and 1×1010 Ad-hCMV-Flt3L viral particles (cohort D), 1×1010 Ad-hCMV-TK viral particles and 1×1011 Ad-hCMV-Flt3L viral particles (cohort E), and 1×1011 Ad-hCMV-TK viral particles and 1×1011 Ad-hCMV-Flt3L viral particles (cohort F) following a 3+3 design. Two 1 mL tuberculin syringes were used to deliver freehand a mix of Ad-hCMV-TK and Ad-hCMV-Flt3L vectors into the walls of the resection cavity with a total injection of 2 mL distributed as 0·1 mL per site across 20 locations. Subsequently, patients received two 14-day courses of valacyclovir (2 g orally, three times per day) at 1-3 days and 10-12 weeks after vector administration and standad upfront chemoradiotherapy. The primary endpoint was the maximum tolerated dose of Ad-hCMV-Flt3L and Ad-hCMV-TK. Overall survival was a secondary endpoint. Recruitment is complete and the trial is finished. The trial is registered with ClinicalTrials.gov, NCT01811992. FINDINGS: Between April 8, 2014, and March 13, 2019, 21 patients were assessed for eligibility and 18 patients with high-grade glioma were enrolled and included in the analysis (three patients in each of the six dose cohorts); eight patients were female and ten were male. Neuropathological examination identified 14 (78%) patients with glioblastoma, three (17%) with gliosarcoma, and one (6%) with anaplastic ependymoma. The treatment was well-tolerated, and no dose-limiting toxicity was observed. The maximum tolerated dose was not reached. The most common serious grade 3-4 adverse events across all treatment groups were wound infection (four events in two patients) and thromboembolic events (five events in four patients). One death due to an adverse event (respiratory failure) occurred but was not related to study treatment. No treatment-related deaths occurred during the study. Median overall survival was 21·3 months (95% CI 11·1-26·1). INTERPRETATION: The combination of two adenoviral vectors demonstrated safety and feasibility in patients with high-grade glioma and warrants further investigation in a phase 1b/2 clinical trial. FUNDING: Funded in part by Phase One Foundation, Los Angeles, CA, The Board of Governors at Cedars-Sinai Medical Center, Los Angeles, CA, and The Rogel Cancer Center at The University of Michigan.

Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging.

Molecular classification has transformed the management of brain tumors by enabling more accurate prognostication and personalized treatment. However, timely molecular diagnostic testing for patients with brain tumors is limited, complicating surgical and adjuvant treatment and obstructing clinical trial enrollment. In this study, we developed DeepGlioma, a rapid (<90 seconds), artificial-intelligence-based diagnostic screening system to streamline the molecular diagnosis of diffuse gliomas. DeepGlioma is trained using a multimodal dataset that includes stimulated Raman histology (SRH); a rapid, label-free, non-consumptive, optical imaging method; and large-scale, public genomic data. In a prospective, multicenter, international testing cohort of patients with diffuse glioma (n = 153) who underwent real-time SRH imaging, we demonstrate that DeepGlioma can predict the molecular alterations used by the World Health Organization to define the adult-type diffuse glioma taxonomy (IDH mutation, 1p19q co-deletion and ATRX mutation), achieving a mean molecular classification accuracy of 93.3 ± 1.6%. Our results represent how artificial intelligence and optical histology can be used to provide a rapid and scalable adjunct to wet lab methods for the molecular screening of patients with diffuse glioma.

102 AI-Based Molecular Classification of Diffuse Gliomas using Rapid, Label-Free Optical Imaging.

INTRODUCTION: Molecular classification has transformed the management of brain tumors by enabling more accurate prognostication and personalized treatment. Access to timely molecular diagnostic testing for brain tumor patients is limited, complicating surgical and adjuvant treatment and obstructing clinical trial enrollment. METHODS: By combining stimulated Raman histology (SRH), a rapid, label-free, non-consumptive, optical imaging method, and deep learning-based image classification, we are able to predict the molecular genetic features used by the World Health Organization (WHO) to define the adult-type diffuse glioma taxonomy, including IDH-1/2, 1p19q-codeletion, and ATRX loss. We developed a multimodal deep neural network training strategy that uses both SRH images and large-scale, public diffuse glioma genomic data (i.e. TCGA, CGGA, etc.) in order to achieve optimal molecular classification performance. RESULTS: One institution was used for model training (University of Michigan) and four institutions (NYU, UCSF, Medical University of Vienna, and University Hospital Cologne) were included for patient enrollment in the prospective testing cohort. Using our system, called DeepGlioma, we achieved an average molecular genetic classification accuracy of 93.2% and identified the correct diffuse glioma molecular subgroup with 91.5% accuracy within 2 minutes in the operating room. DeepGlioma outperformed conventional IDH1-R132H immunohistochemistry (94.2% versus 91.4% accuracy) as a first-line molecular diagnostic screening method for diffuse gliomas and can detect canonical and non-canonical IDH mutations. CONCLUSIONS: Our results demonstrate how artificial intelligence and optical histology can be used to provide a rapid and scalable alternative to wet lab methods for the molecular diagnosis of brain tumor patients during surgery.

Metabolomic Profiles of Human Glioma Inform Patient Survival.

Aims: Targeting tumor metabolism may improve the outcomes for patients with glioblastoma (GBM). To further preclinical efforts targeting metabolism in GBM, we tested the hypothesis that brain tumors can be stratified into distinct metabolic groups with different patient outcomes. Therefore, to determine if tumor metabolites relate to patient survival, we profiled the metabolomes of human gliomas and correlated metabolic information with clinical data. Results: We found that isocitrate dehydrogenase-wildtype (IDHwt) GBMs are metabolically distinguishable from IDH mutated (IDHmut) astrocytomas and oligodendrogliomas. Survival of patients with IDHmut gliomas was expectedly more favorable than those with IDHwt GBM, and metabolic signatures can stratify IDHwt GBMs subtypes with varying prognoses. Patients whose GBMs were enriched in amino acids had improved survival, while those whose tumors were enriched for nucleotides, redox molecules, and lipid metabolites fared more poorly. These findings were recapitulated in validation cohorts using both metabolomic and transcriptomic data. Innovation: Our results suggest the existence of metabolic subtypes of GBM with differing prognoses, and further support the concept that metabolism may drive the aggressiveness of human gliomas. Conclusions: Our data show that metabolic signatures of human gliomas can inform patient survival. These findings may be used clinically to tailor novel metabolically targeted agents for GBM patients with different metabolic phenotypes. Antioxid. Redox Signal. 39, 942-956.

Subclonal evolution and expansion of spatially distinct THY1-positive cells is associated with recurrence in glioblastoma.

PURPOSE: Glioblastoma(GBM) is a lethal disease characterized by inevitable recurrence. Here we investigate the molecular pathways mediating resistance, with the goal of identifying novel therapeutic opportunities. EXPERIMENTAL DESIGN: We developed a longitudinal in vivo recurrence model utilizing patient-derived explants to produce paired specimens(pre- and post-recurrence) following temozolomide(TMZ) and radiation(IR). These specimens were evaluated for treatment response and to identify gene expression pathways driving treatment resistance. Findings were clinically validated using spatial transcriptomics of human GBMs. RESULTS: These studies reveal in replicate cohorts, a gene expression profile characterized by upregulation of mesenchymal and stem-like genes at recurrence. Analyses of clinical databases revealed significant association of this transcriptional profile with worse overall survival and upregulation at recurrence. Notably, gene expression analyses identified upregulation of TGFß signaling, and more than one-hundred-fold increase in THY1 levels at recurrence. Furthermore, THY1-positive cells represented <10% of cells in treatment-naïve tumors, compared to 75-96% in recurrent tumors. We then isolated THY1-positive cells from treatment-naïve patient samples and determined that they were inherently resistant to chemoradiation in orthotopic models. Additionally, using image-guided biopsies from treatment-naïve human GBM, we conducted spatial transcriptomic analyses. This revealed rare THY1+ regions characterized by mesenchymal/stem-like gene expression, analogous to our recurrent mouse model, which co-localized with macrophages within the perivascular niche. We then inhibited TGFBRI activity in vivo which decreased mesenchymal/stem-like protein levels, including THY1, and restored sensitivity to TMZ/IR in recurrent tumors. CONCLUSIONS: These findings reveal that GBM recurrence may result from tumor repopulation by pre-existing, therapy-resistant, THY1-positive, mesenchymal cells within the perivascular niche.

Neurosurgery residency and fellowship education in the United States: 2 decades of system development by the One Neurosurgery Summit organizations.

The purpose of this report is to chronicle a 2-decade period of educational innovation and improvement, as well as governance reform, across the specialty of neurological surgery. Neurological surgery educational and professional governance systems have evolved substantially over the past 2 decades with the goal of improving training outcomes, patient safety, and the quality of US neurosurgical care. Innovations during this period have included the following: creating a consensus national curriculum; standardizing the length and structure of neurosurgical training; introducing educational outcomes milestones and required case minimums; establishing national skills, safety, and professionalism courses; systematically accrediting subspecialty fellowships; expanding professional development for educators; promoting training in research; and coordinating policy and strategy through the cooperation of national stakeholder organizations. A series of education summits held between 2007 and 2009 restructured some aspects of neurosurgical residency training. Since 2010, ongoing meetings of the One Neurosurgery Summit have provided strategic coordination for specialty definition, neurosurgical education, public policy, and governance. The Summit now includes leadership representatives from the Society of Neurological Surgeons, the American Association of Neurological Surgeons, the Congress of Neurological Surgeons, the American Board of Neurological Surgery, the Review Committee for Neurological Surgery of the Accreditation Council for Graduate Medical Education, the American Academy of Neurological Surgery, and the AANS/CNS Joint Washington Committee. Together, these organizations have increased the effectiveness and efficiency of the specialty of neurosurgery in advancing educational best practices, aligning policymaking, and coordinating strategic planning in order to meet the highest standards of professionalism and promote public health.

Classifier Using Pontine Radial Diffusivity and Symptom Duration Accurately Predicts Recurrence of Trigeminal Neuralgia After Microvascular Decompression: A Pilot Study and Algorithm Description.

BACKGROUND: Preprocedure diffusion tensor magnetic resonance imaging (MRI) may predict the response of trigeminal neuralgia (TN) patients to Gamma Knife (Elekta AB) and microvascular decompression (MVD). OBJECTIVE: To test this hypothesis using pontine-segment diffusion tensor MRI radial diffusivity (RD), a known biomarker for demyelination, to predict TN recurrence following MVD. METHODS: RD from the pontine segment of the trigeminal tract was extracted in a semiautomated and blinded fashion and normalized to background pontine RD. Following validation against published results, the relationship of normalized RD to symptom duration (DS) was measured. Both parameters were then introduced into machine-learning classifiers to group patient outcomes as TN remission or recurrence. Performance was evaluated in an observational study with leave-one-out cross-validation to calculate accuracy, sensitivity, specificity, and receiver operating characteristic curves. RESULTS: The study population included 22 patients with TN type 1 (TN1). There was a negative correlation of normalized RD and preoperative symptom duration (P = .035, R2 = .20). When pontine-segment RD and DS were included as input variables, 2 classifiers predicted pain-free remission versus eventual recurrence with 85% accuracy, 83% sensitivity, and 86% specificity (leave-one-out cross-validation; P = .029) in a cohort of 13 patients undergoing MVD. CONCLUSION: Pontine-segment RD and DS accurately predict MVD outcomes in TN1 and provide further evidence that diffusion tensor MRI contains prognostic information. Use of a classifier may allow more accurate risk stratification for neurosurgeons and patients considering MVD as a treatment for TN1. These findings provide further insight into the relationship of pontine microstructure, represented by RD, and the pathophysiology of TN.

Rapid, label-free detection of diffuse glioma recurrence using intraoperative stimulated Raman histology and deep neural networks.

BACKGROUND: Detection of glioma recurrence remains a challenge in modern neuro-oncology. Noninvasive radiographic imaging is unable to definitively differentiate true recurrence versus pseudoprogression. Even in biopsied tissue, it can be challenging to differentiate recurrent tumor and treatment effect. We hypothesized that intraoperative stimulated Raman histology (SRH) and deep neural networks can be used to improve the intraoperative detection of glioma recurrence. METHODS: We used fiber laser-based SRH, a label-free, nonconsumptive, high-resolution microscopy method (<60 sec per 1 × 1 mm2) to image a cohort of patients (n = 35) with suspected recurrent gliomas who underwent biopsy or resection. The SRH images were then used to train a convolutional neural network (CNN) and develop an inference algorithm to detect viable recurrent glioma. Following network training, the performance of the CNN was tested for diagnostic accuracy in a retrospective cohort (n = 48). RESULTS: Using patch-level CNN predictions, the inference algorithm returns a single Bernoulli distribution for the probability of tumor recurrence for each surgical specimen or patient. The external SRH validation dataset consisted of 48 patients (recurrent, 30; pseudoprogression, 18), and we achieved a diagnostic accuracy of 95.8%. CONCLUSION: SRH with CNN-based diagnosis can be used to improve the intraoperative detection of glioma recurrence in near-real time. Our results provide insight into how optical imaging and computer vision can be combined to augment conventional diagnostic methods and improve the quality of specimen sampling at glioma recurrence.

Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks.

Intraoperative diagnosis is essential for providing safe and effective care during cancer surgery1. The existing workflow for intraoperative diagnosis based on hematoxylin and eosin staining of processed tissue is time, resource and labor intensive2,3. Moreover, interpretation of intraoperative histologic images is dependent on a contracting, unevenly distributed, pathology workforce4. In the present study, we report a parallel workflow that combines stimulated Raman histology (SRH)5-7, a label-free optical imaging method and deep convolutional neural networks (CNNs) to predict diagnosis at the bedside in near real-time in an automated fashion. Specifically, our CNNs, trained on over 2.5 million SRH images, predict brain tumor diagnosis in the operating room in under 150 s, an order of magnitude faster than conventional techniques (for example, 20-30 min)2. In a multicenter, prospective clinical trial (n = 278), we demonstrated that CNN-based diagnosis of SRH images was noninferior to pathologist-based interpretation of conventional histologic images (overall accuracy, 94.6% versus 93.9%). Our CNNs learned a hierarchy of recognizable histologic feature representations to classify the major histopathologic classes of brain tumors. In addition, we implemented a semantic segmentation method to identify tumor-infiltrated diagnostic regions within SRH images. These results demonstrate how intraoperative cancer diagnosis can be streamlined, creating a complementary pathway for tissue diagnosis that is independent of a traditional pathology laboratory.

Assessment of wakefulness during awake craniotomy to predict intraoperative language performance.

OBJECTIVE: Maximal safe tumor resection in language areas of the brain relies on a patient's ability to perform intraoperative language tasks. Assessing the performance of these tasks during awake craniotomies allows the neurosurgeon to identify and preserve brain regions that are critical for language processing. However, receiving sedation and analgesia just prior to experiencing an awake craniotomy may reduce a patient's wakefulness, leading to transient language and/or cognitive impairments that do not completely subside before language testing begins. At present, the degree to which wakefulness influences intraoperative language task performance is unclear. Therefore, the authors sought to determine whether any of 5 brief measures of wakefulness predicts such performance during awake craniotomies for glioma resection. METHODS: The authors recruited 21 patients with dominant hemisphere low- and high-grade gliomas. Each patient performed baseline wakefulness measures in addition to picture-naming and text-reading language tasks 24 hours before undergoing an awake craniotomy. The patients performed these same tasks again in the operating room following the cessation of anesthesia medications. The authors then conducted statistical analyses to investigate potential relationships between wakefulness measures and language task performance. RESULTS: Relative to baseline, performance on 3 of the 4 objective wakefulness measures (rapid counting, button pressing, and vigilance) declined in the operating room. Moreover, these declines appeared in the complete absence of self-reported changes in arousal. Performance on language tasks similarly declined in the intraoperative setting, with patients experiencing greater declines in picture naming than in text reading. Finally, performance declines on rapid counting and vigilance wakefulness tasks predicted performance declines on the picture-naming task. CONCLUSIONS: Current subjective methods for assessing wakefulness during awake craniotomies may be insufficient. The administration of objective measures of wakefulness just prior to language task administration may help to ensure that patients are ready for testing. It may also allow neurosurgeons to identify patients who are at risk for poor intraoperative performance.

Awake Implantation of Thoracic Spinal Cord Stimulator Paddle Electrode and Generator: 2-Dimensional Operative Video.

In this surgical video, the operative technique is presented for awake implantation of a thoracic paddle electrode for spinal cord stimulation. In the first stage, a laminotomy is performed with the patient under conscious sedation. Once the paddle is in optimal position, the patient is tested intraoperatively to confirm adequate coverage and absence of untoward side effects. This paddle electrode is used for the trial period. If the patient derives satisfactory pain relief during the trial, they are returned for the second stage implantation of pulse generator, without moving the initial paddle electrode now already placed in an optimal location. Particular attention in this video is provided toward the optimal positioning and technique for the awake laminotomy to ensure patient comfort and reliability during testing, the use of extension leads tunneled opposite the planned pulse generator site, and the method of removing extension leads to preserve the placement of the initial paddle electrode.

Variability in the location of high frequency oscillations during prolonged intracranial EEG recordings.

The rate of interictal high frequency oscillations (HFOs) is a promising biomarker of the seizure onset zone, though little is known about its consistency over hours to days. Here we test whether the highest HFO-rate channels are consistent across different 10-min segments of EEG during sleep. An automated HFO detector and blind source separation are applied to nearly 3000 total hours of data from 121 subjects, including 12 control subjects without epilepsy. Although interictal HFOs are significantly correlated with the seizure onset zone, the precise localization is consistent in only 22% of patients. The remaining patients either have one intermittent source (16%), different sources varying over time (45%), or insufficient HFOs (17%). Multiple HFO networks are found in patients with both one and multiple seizure foci. These results indicate that robust HFO interpretation requires prolonged analysis in context with other clinical data, rather than isolated review of short data segments.

Rapid intraoperative histology of unprocessed surgical specimens via fibre-laser-based stimulated Raman scattering microscopy.

Conventional methods for intraoperative histopathologic diagnosis are labour- and time-intensive, and may delay decision-making during brain-tumour surgery. Stimulated Raman scattering (SRS) microscopy, a label-free optical process, has been shown to rapidly detect brain-tumour infiltration in fresh, unprocessed human tissues. Here, we demonstrate the first application of SRS microscopy in the operating room by using a portable fibre-laser-based microscope and unprocessed specimens from 101 neurosurgical patients. We also introduce an image-processing method - stimulated Raman histology (SRH) - which leverages SRS images to create virtual haematoxylin-and-eosin-stained slides, revealing essential diagnostic features. In a simulation of intraoperative pathologic consultation in 30 patients, we found a remarkable concordance of SRH and conventional histology for predicting diagnosis (Cohen's kappa, κ > 0.89), with accuracy exceeding 92%. We also built and validated a multilayer perceptron based on quantified SRH image attributes that predicts brain-tumour subtype with 90% accuracy. Our findings provide insight into how SRH can now be used to improve the surgical care of brain tumour patients.

Defining Glioblastoma Resectability Through the Wisdom of the Crowd: A Proof-of-Principle Study.

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.

Simulator and 2 tools: Validation of performance measures from a novel neurosurgery simulation model using the current Standards framework.

BACKGROUND: Ventriculostomy is a common neurosurgical procedure with a relatively steep learning curve. A low-cost, high-fidelity simulator paired with procedure-specific performance measures would provide a safe environment to teach ventriculostomy procedural skills. The same validated simulation model could also allow for assessment of trainees' proficiencies with measures that align with Accreditation Council for Graduate Medical Education milestones. This study extends previous work to evaluate validity evidence from the simulator, its newly developed performance assessment, the Ventricolostomy Procedural Assessment Tool, and the Objective Structured Assessment for Technical Skills. METHODS: After Institutional Review Board exemption, performance data were collected from 11 novice and 3 expert neurosurgeons (n = 14). Participants self-reported their ability to perform tasks on the simulator using the Ventricolostomy Procedural Assessment Tool, an 11-item, step-wise instrument with 5-point rating scales ranging from 1 (unable to perform) to 5 (performs easily and smoothly). De-identified operative performances were videotaped and independently rated by 3 neurosurgeons, using the Ventricolostomy Procedural Assessment Tool and Objective Structured Assessment for Technical Skills. We evaluated multiple sources of validity evidence (2014 Standards) to examine psychometric quality of the measures and to test our assumption that the tools could discriminate between novice and expert performances adequately. We used a multifacet Rasch model and traditional indices, such as Cronbach alpha, intraclass correlation, and Wilcoxon signed-rank test estimates. RESULTS: Validity evidence relevant to test content and response processes was supported adequately. Evidence of internal structure was supported by high interitem consistency (n = 0.95) and inter-rater agreement for most Ventricolostomy Procedural Assessment Tool items (Intraclass correlation coefficient = [0.00, 0.91]) and all Objective Structured Assessment for Technical Skills items (Intraclass correlation coefficient = [0.80, 0.93]). Overall, novices performed at a lower level than experts on both scales (P < .05), supporting evidence relevant to relationships to other variables. Deeper analysis of novice/expert ratings indicated novices attained lower performances ratings for all Ventricolostomy Procedural Assessment Tool and Objective Structured Assessment for Technical Skills items, but statistical significance was only achieved for the Objective Structured Assessment for Technical Skills items (P < .01). Rater bias estimates were favorable, supporting evidence relevant to consequences of testing. CONCLUSION: Despite a small sample, favorable evidence using current Standards supports the use of the novel simulator and both tools combined for skills training and performance assessment, but challenges (potential threats to validity) should be considered prior to implementation.

Gemcitabine Plus Radiation Therapy for High-Grade Glioma: Long-Term Results of a Phase 1 Dose-Escalation Study.

PURPOSE: To evaluate the tolerability and efficacy of gemcitabine plus radiation therapy (RT) in this phase 1 study of patients with newly diagnosed malignant glioma (HGG). PATIENTS AND METHODS: Between 2004 and 2012, 29 adults with HGG were enrolled. After any extent of resection, RT (60 Gy over 6 weeks) was given concurrent with escalating doses of weekly gemcitabine. Using a time-to-event continual reassessment method, 5 dose levels were evaluated starting at 500 mg/m(2) during the last 2 weeks of RT and advanced stepwise into earlier weeks. The primary objective was to determine the recommended phase 2 dose of gemcitabine plus RT. Secondary objectives included progression-free survival, overall survival (OS), and long-term toxicity. RESULTS: Median follow-up was 38.1 months (range, 8.9-117.5 months); 24 patients were evaluable for toxicity. After 2005 when standard practice changed, patients with World Health Organization grade 4 tumors were no longer enrolled. Median progression-free survival for 22 patients with grade 3 tumors was 26.0 months (95% confidence interval [CI] 15.6-inestimable), and OS was 48.5 months (95% CI 26.8-inestimable). In 4 IDH mutated, 1p/19q codeleted patients, no failures occurred, with all but 1 alive at time of last follow-up. Seven with IDH mutated, non-codeleted tumors with ATRX loss had intermediate OS of 73.5 months (95% CI 32.8-inestimable). Six nonmutated, non-codeleted patients had a median OS of 26.5 months (95% CI 25.4-inestimable). The recommended phase 2 dose of gemcitabine plus RT was 750 mg/m(2)/wk given the last 4 weeks of RT. Dose reductions were most commonly due to grade 3 neutropenia; no grade 4 or 5 toxicities were seen. CONCLUSIONS: Gemcitabine concurrent with RT is well-tolerated and yields promising outcomes, including in patients with adverse molecular features. It is a candidate for further study, particularly for poor-prognosis patient subgroups with HGG.

Detection of human brain tumor infiltration with quantitative stimulated Raman scattering microscopy.

Differentiating tumor from normal brain is a major barrier to achieving optimal outcome in brain tumor surgery. New imaging techniques for visualizing tumor margins during surgery are needed to improve surgical results. We recently demonstrated the ability of stimulated Raman scattering (SRS) microscopy, a nondestructive, label-free optical method, to reveal glioma infiltration in animal models. We show that SRS reveals human brain tumor infiltration in fresh, unprocessed surgical specimens from 22 neurosurgical patients. SRS detects tumor infiltration in near-perfect agreement with standard hematoxylin and eosin light microscopy (κ = 0.86). The unique chemical contrast specific to SRS microscopy enables tumor detection by revealing quantifiable alterations in tissue cellularity, axonal density, and protein/lipid ratio in tumor-infiltrated tissues. To ensure that SRS microscopic data can be easily used in brain tumor surgery, without the need for expert interpretation, we created a classifier based on cellularity, axonal density, and protein/lipid ratio in SRS images capable of detecting tumor infiltration with 97.5% sensitivity and 98.5% specificity. Quantitative SRS microscopy detects the spread of tumor cells, even in brain tissue surrounding a tumor that appears grossly normal. By accurately revealing tumor infiltration, quantitative SRS microscopy holds potential for improving the accuracy of brain tumor surgery.