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.

The MedConnect Program: Symptomatology, Return Visits, and Hospitalization of COVID-19 Outpatients Following Discharge From the Emergency Department.

Background and objective Although hospitalization is required for only a minority of those infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the high rates of morbidity and mortality among these patients have led researchers to focus on the predictors of admission and adverse outcomes in the inpatient population. However, there is scarce data on the clinical trajectory of individuals symptomatic enough to present for emergency care, but not sick enough to be admitted. In light of this, we aimed to examine the symptomatology, emergency department (ED) revisits, and hospitalization of coronavirus disease 2019 (COVID-19) outpatients after discharge from the ED. Methods Adult patients with COVID-19 infection were prospectively enrolled after discharge from the ED between May and December 2020. Patients were followed up longitudinally for 14 days via phone interviews designed to provide support and information and to track symptomatology, ED revisits, and hospitalization. Results A volunteer, medical student-run program enrolled 199 COVID-19 patients discharged from the ED during the first nine months of the pandemic. Of the 176 patients (88.4%) who completed the 14-day protocol, 29 (16.5%) had a second ED visit and 17 (9.6%) were admitted, 16 (9%) for worsening COVID-19 symptoms. Age, male sex, comorbid illnesses, and self-reported dyspnea, diarrhea, chills, and fever were associated with hospital admission for patients with a subsequent ED visit. For those who did not require admission, symptoms generally improved following ED discharge. Age >65 years and a history of cardiovascular disease (CVD) were associated with a longer duration of cough, but generally, patient characteristics and comorbidities did not significantly affect the overall number or duration of symptoms. Conclusions Nearly one in five patients discharged from the ED with COVID-19 infection had a second ED evaluation during a 14-day follow-up period, despite regular phone interactions aimed at providing support and information. More than half of them required admission for worsening COVID-19 symptoms. Established risk factors for severe disease and self-reported persistence of certain symptoms were associated with hospital admission, while those who did not require hospitalization had a steady improvement in symptoms over the 14-day period.

Ventricular Volume Change as a Predictor of Shunt-Dependent Hydrocephalus in Aneurysmal Subarachnoid Hemorrhage.

BACKGROUND: Hydrocephalus is a common complication of aneurysmal subarachnoid hemorrhage (aSAH) that often requires acute placement of an external ventricular drain (EVD). The current systems available for determining which patients will require long-term cerebrospinal fluid diversion remain subjective. We investigated the ventricular volume change (ΔVV) after EVD clamping as an objective predictor of shunt dependence in patients with aSAH. METHODS: We performed a retrospective medical record review and image analysis of patients treated for aSAH at a single academic institution who had required EVD placement for acute hydrocephalus and had undergone 1 EVD weaning trial. Head computed tomography (CT) scans obtained before and after EVD clamping were analyzed using a custom semiautomated MATLAB program (MathWorks, Natick, Massachusetts, USA), which segments each CT scan into 5 tissue types using k-means clustering. Differences in the pre- and postclamp ventricular volumes were calculated. RESULTS: A total of 34 patients with an indwelling shunt met the inclusion criteria and were sex- and age-matched to 34 controls without a shunt. The mean ΔVV was 19.8 mL in the shunt patients and 3.8 mL in the nonshunt patients (P < 0.0001). The area under the receiver operating characteristic curve was 0.84. The optimal ΔVV threshold was 11.4 mL, with a sensitivity of 76.5% and specificity of 88.2% for predicting shunt dependence. The mean ΔVV was significantly greater for the patients readmitted for shunt placement compared with the patients not requiring cerebrospinal fluid diversion (18.69 mL vs. 3.84 mL; P = 0.005). Finally, 70% of the patients with delayed shunt dependence had ΔVV greater than the identified threshold. CONCLUSIONS: The ΔVV volume between head CT scans taken before and after EVD clamping was predictive of early and delayed shunt dependence.

Risk factors and morbidity associated with surgical site infection subtypes following adult neurosurgical procedures.

OBJECTIVE: Studies on surgical site infection (SSI) in adult neurosurgery have presented all subtypes of SSIs as the general 'SSI'. Given that SSIs constitute a broad range of infections, we hypothesized that clinical outcomes and management vary based on SSI subtype. METHODS: A retrospective analysis of all neurosurgical SSI from 2012-2019 was conducted at a tertiary care institution. SSI subtypes were categorized as deep and superficial incisional SSI, brain, dural or spinal abscesses, meningitis or ventriculitis, and osteomyelitis. RESULTS: 9620 craniotomy, shunt, and fusion procedures were studied. 147 procedures (1.5%) resulted in postoperative SSI. 87 (59.2%) of these were associated with craniotomy, 36 (24.5%) with spinal fusion, and 24 (16.3%) with ventricular shunting. Compared with superficial incisional primary SSI, rates of reoperation to treat SSI were highest for deep incisional primary SSI (91.2% vs 38.9% for superficial, p < 0.001) and second-highest for intracranial SSI (90.9% vs 38.9%, p = 0.0001). Postoperative meningitis was associated with the highest mortality rate (14.9%). Compared with superficial incisional SSI, the rate of readmission for intracranial SSI was highest (57.6% vs 16.7%, p = 0.022). CONCLUSION: Deep incisional and organ space SSI demonstrate a greater association with morbidity relative to superficial incisional SSI. Future studies should assess subtypes of SSI given these differences.

Length of Stay Beyond Medical Readiness in a Neurosurgical Patient Population and Associated Healthcare Costs.

BACKGROUND: Length of stay beyond medical readiness (LOS-BMR) leads to increased expenses and higher morbidity related to hospital-acquired conditions. OBJECTIVE: To determine the proportion of admitted neurosurgical patients who have LOS-BMR and associated risk factors and costs. METHODS: We performed a prospective, cohort analysis of all neurosurgical patients admitted to our institution over 5 mo. LOS-BMR was assessed daily by the attending neurosurgeon and neuro-intensivist with a standardized criterion. Univariate and multivariate logistic regressions were performed. RESULTS: Of the 884 patients admitted, 229 (25.9%) had a LOS-BMR. The average LOS-BMR was 2.7 ± 3.1 d at an average daily cost of $9 148.28 ± $12 983.10, which resulted in a total cost of $2 076 659.32 over the 5-mo period. Patients with LOS-BMR were significantly more likely to be older and to have hemiplegia, dementia, liver disease, renal disease, and diabetes mellitus. Patients with a LOS-BMR were significantly more likely to be discharged to a subacute rehabilitation/skilled nursing facility (40.2% vs 4.1%) or an acute/inpatient rehabilitation facility (22.7% vs 1.7%, P < .0001). Patients with Medicare insurance were more likely to have a LOS-BMR, whereas patients with private insurance were less likely (P = .048). CONCLUSION: The most common reason for LOS-BMR was inefficient discharge of patients to rehabilitation and nursing facilities secondary to unavailability of beds at discharge locations, insurance clearance delays, and family-related issues.

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.

Neurosurgical patients admitted via the emergency department initiating comfort care measures: a prospective cohort analysis.

BACKGROUND: Given the serious nature of many neurosurgical pathologies, it is common for hospitalized patients to elect comfort care (CC) over aggressive treatment. Few studies have evaluated the incidence and risk factors of CC trends in patients admitted for neurosurgical emergencies. OBJECTIVES: To analyze all neurosurgical patients admitted to a tertiary care academic referral center via the emergency department (ED) to determine incidence and characteristics of those who initiated CC measures during their initial hospital admission. METHODS: We performed a prospective, cohort analysis of all consecutive adult patients admitted to the neurosurgical service via the ED between October 2018 and May 2019. The primary outcome was the initiation of CC measures during the patient's hospital admission. CC was defined as cessation of life-sustaining measures and a shift in focus to maintaining the comfort and dignity of the patient. RESULTS: Of the 428 patients admitted during the 7-month period, 29 (6.8%) initiated CC measures within 4.0 ± 4.0 days of admission. Patients who entered CC were significantly more likely to have a medical history of cerebrovascular disease (58.6% vs. 33.3%, p = 0.006), dementia (17.2% vs. 1.5%, p = 0.0004), or cancer with metastatic disease (24.1% vs. 7.0%, p = 0.001). Patients with a presenting pathology associated with cerebrovascular disease were significantly more likely to initiate CC (62.1% vs. 35.3, p = 0.04). Patients who underwent emergent surgery were significantly more likely to enter CC compared with those who had elective surgery (80.0% vs. 42.7%, p = 0.02). Only 10 of the 29 (34.5%) patients who initiated CC underwent a neurosurgical operation (p = 0.002). Twenty of the 29 (69.0%) patients died within 0.8 ± 0.8 days after the initiation of CC measures. CONCLUSION: CC measures were initiated in 6.8% of patients admitted to the neurosurgical service via the ED, with the majority of patients entering CC before an operation and presenting with a cerebrovascular pathology.

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.

Control of autoimmune inflammation using liposomes to deliver positive allosteric modulators of metabotropic glutamate receptors.

Multiple sclerosis (MS) is an autoimmune disease where myelin is incorrectly recognized as foreign and attacked by the adaptive immune system. Dendritic cells (DCs) direct adaptive immunity by presenting antigens to T cells, therefore serving as a target for autoimmune therapies. N-Phenyl-7-(hydroxyimino) cyclopropa[b]chromen-1a-carboxamide (PHCCC), a positive allosteric modulator of metabotropic glutamate receptor 4 (mGluR4), can promote regulatory T cells by altering cytokine secretion to bias T cell differentiation. The therapeutic potential of PHCCC, however, is hindered by dose-limiting toxicity, poor solubility, and the need for frequent dosing. We hypothesized liposomal delivery of PHCCC might enable safe, effective delivery of this hydrophobic drug to exploit metabolism as a means of controlling inflammation in self-reactive immune cells. PHCCC was readily encapsulated in liposomes modified with polyethylene glycol. Under sink conditions, controlled release resulted in 58% of drug released into media over 18 hours. Culture of primary DCs with PHCCC liposomes reduced pro-inflammatory cytokine secretion while reducing toxicity four-fold compared with soluble PHCCC. During co-culture of DCs with myelin-reactive T cells from transgenic mice, PHCCC liposomes reduced T cell proliferation and interferon gamma secretion. These results support the potential of using liposomes to promote tolerance through biocompatible delivery of metabolic modulators. © 2017 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 105A: 2977-2985, 2017.

Pulsed ultrasound expands the extracellular and perivascular spaces of the brain.

Diffusion within the extracellular and perivascular spaces of the brain plays an important role in biological processes, therapeutic delivery, and clearance mechanisms within the central nervous system. Recently, ultrasound has been used to enhance the dispersion of locally administered molecules and particles within the brain, but ultrasound-mediated effects on the brain parenchyma remain poorly understood. We combined an electron microscopy-based ultrastructural analysis with high-resolution tracking of non-adhesive nanoparticles in order to probe changes in the extracellular and perivascular spaces of the brain following a non-destructive pulsed ultrasound regimen known to alter diffusivity in other tissues. Freshly obtained rat brain neocortical slices underwent sham treatment or pulsed, low intensity ultrasound for 5min at 1MHz. Transmission electron microscopy revealed intact cells and blood vessels and evidence of enlarged spaces, particularly adjacent to blood vessels, in ultrasound-treated brain slices. Additionally, ultrasound significantly increased the diffusion rate of 100nm, 200nm, and 500nm nanoparticles that were injected into the brain slices, while 2000nm particles were unaffected. In ultrasound-treated slices, 91.6% of the 100nm particles, 20.7% of the 200nm particles, 13.8% of the 500nm particles, and 0% of the 2000nm particles exhibited diffusive motion. Thus, pulsed ultrasound can have meaningful structural effects on the brain extracellular and perivascular spaces without evidence of tissue disruption.

Controlled delivery of a metabolic modulator promotes regulatory T cells and restrains autoimmunity.

Autoimmune disorders occur when the immune system abnormally recognizes and attacks self-molecules. Dendritic cells (DCs) play a powerful role in initiating adaptive immune response, and are therefore a recent target for autoimmune therapies. N-Phenyl-7-(hydroxyimino)cyclopropa[b]chromen-1a-carboxamide (PHCCC), a small molecule glutamate receptor enhancer, alters how DCs metabolize glutamate, skewing cytokine secretion to bias T cell function. These effects provide protection in mouse models of multiple sclerosis (MS) by polarizing T cells away from inflammatory TH17 cells and toward regulatory T cells (TREG) when mice receive daily systemic injections of PHCCC. However, frequent, continued treatment is required to generate and maintain therapeutic benefits. Thus, the use of PHCCC is limited by poor solubility, the need for frequent dosing, and cell toxicity. We hypothesized that controlled release of PHCCC from degradable nanoparticles (NPs) might address these challenges by altering DC function to maintain efficacy with reduced treatment frequency and toxicity. This idea could serve as a new strategy for harnessing biomaterials to polarize immune function through controlled delivery of metabolic modulators. PHCCC was readily encapsulated in nanoparticles, with controlled release of 89% of drug into media over three days. Culture of primary DCs or DC and T cell co-cultures with PHCCC NPs reduced DC activation and secretion of pro-inflammatory cytokines, while shifting T cells away from TH17 and toward TREG phenotypes. Importantly, PHCCC delivered to cells in NPs was 36-fold less toxic compared with soluble PHCCC. Treatment of mice with PHCCC NPs every three days delayed disease onset and decreased disease severity compared with mice treated with soluble drug at the same dose and frequency. These results highlight the potential to promote tolerance through controlled delivery of metabolic modulators that alter DC signaling to polarize T cells, and suggest future gains that could be realized by engineering materials that provide longer term release.