Faculty Bibliography

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.

The discovery of a new mass involving the brain or spine typically prompts referral to a neurosurgeon to consider biopsy or surgical resection. Intraoperative decision-making depends significantly on the histologic diagnosis, which is often established when a small specimen is sent for immediate interpretation by a neuropathologist. Access to neuropathologists may be limited in resource-poor settings, which has prompted several groups to develop machine learning algorithms for automated interpretation. Most attempts have focused on fixed histopathology specimens, which do not apply in the intraoperative setting. The greatest potential for clinical impact probably lies in the automated diagnosis of intraoperative specimens. Successful future studies may use machine learning to automatically classify whole-slide intraoperative specimens among a wide array of potential diagnoses.

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 intensive^2,3. 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.

We recently developed and validated a bedside tissue-to-diagnosis pipeline using stimulated Raman histology (SRH), a label-free optical imaging method, and deep convolutional neural networks (CNN) in prospective clinical trial. Our CNN learned a hierarchy of interpretable histologic features found in the most common brain tumors and was able to accurately segment cancerous regions in SRH images.

BACKGROUND:Laser interstitial thermal therapy (LITT) is becoming an increasingly popular technique for the treatment of brain lesions. More minimally invasive that open craniotomy for lesion resection, LITT may be more appropriate for lesions that are harder to access through an open approach, deeper lesions, and for patients who may not tolerate open surgery. METHODS:A search of the current primary literature on LITT for brain lesions on PubMed was performed. These studies were reviewed and updates on the radiological, pathological, and long-term outcomes after LITT for brain metastases, primary brain tumors, and radiation necrosis as well as common complications are included. RESULTS:Larger extent of ablation and LITT as frontline treatment were potential predictors of favorable progression-free and overall survival for primary brain tumors. In brain metastases, larger extent of ablation was more significantly associated with survival benefit, whereas tumor size was a possible predictor. The most common complications after LITT are transient and permanent weakness, cerebral edema, hemorrhage, seizures, and hyponatremia. CONCLUSIONS:Although the current literature is limited by small sample sizes and primarily retrospective studies, LITT is a safe and effective treatment for brain lesions in the correct patient population.

BACKGROUND:Niemann-Pick disease type C is a fatal and progressive neurodegenerative disorder characterized by the accumulation of unesterified cholesterol in late endosomes and lysosomes. We sought to develop new therapeutics for this disorder by harnessing the body's endogenous cholesterol scavenging particle, high-density lipoprotein (HDL). METHODS:Here we design, optimize, and define the mechanism of action of synthetic HDL (sHDL) nanoparticles. RESULTS:We demonstrate a dose-dependent rescue of cholesterol storage that is sensitive to sHDL lipid and peptide composition, enabling the identification of compounds with a range of therapeutic potency. Peripheral administration of sHDL to Npc1 I1061T homozygous mice mobilizes cholesterol, reduces serum bilirubin, reduces liver macrophage size, and corrects body weight deficits. Additionally, a single intraventricular injection into adult Npc1 I1061T brains significantly reduces cholesterol storage in Purkinje neurons. Since endogenous HDL is also a carrier of sphingomyelin, we tested the same sHDL formulation in the sphingomyelin storage disease Niemann-Pick type A. Utilizing stimulated Raman scattering microscopy to detect endogenous unlabeled lipids, we show significant rescue of Niemann-Pick type A lipid storage. CONCLUSIONS:Together, our data establish that sHDL nanoparticles are a potential new therapeutic avenue for Niemann-Pick diseases.

Background/UNASSIGNED:Cognitive and language dysfunction is common among patients with glioma and has a significant impact on survival and health-related quality of life (HRQOL). Little is known about the factors that make individual patients more or less susceptible to the cognitive sequelae of the disease. A better understanding of the individual and population characteristics related to cognitive function in glioma patients is required to appropriately stratify patients, prognosticate, and develop more efficacious treatment regimens. There is evidence that allelic variation among genes involved in neurotransmission and synaptic plasticity are related to neurocognitive performance in states of health and neurologic disease. Methods/UNASSIGNED:, rs4680) with neurocognitive function and ability to return to work in glioma patients at diagnosis and at 3 months. We developed a functional score based on the number of high-performance alleles that correlates with the capacity for patients to return to work. Results/UNASSIGNED:Patients with higher-performing alleles have better scores on neurocognitive testing with the Repeatable Battery for the Assessment of Neuropsychological Status and Stroop test, but not the Trail Making Test. Conclusions/UNASSIGNED:A better understanding of the genetic contributors to neurocognitive performance in glioma patients and capacity for functional recovery is necessary to develop improved treatment strategies based on patient-specific factors.

BACKGROUND AND PURPOSE/OBJECTIVE:We evaluated whether dose-intensified chemoradiation alters patterns of failure and is associated with favorable survival in the temozolomide era. MATERIALS AND METHODS/METHODS:Between 2003 and 2015, 82 patients with newly diagnosed glioblastoma were treated with 66-81 Gy in 30 fractions using conventional magnetic resonance imaging. Progression-free (PFS) and overall survival (OS) were calculated using Kaplan-Meier methods. Factors associated with improved PFS, OS, and time to progression were assessed using multivariate Cox model and linear regression. RESULTS:Median follow-up was 23 months (95% CI 4-124 months). Sixty-one percent of patients underwent subtotal resection or biopsy, and 38% (10/26) of patients with available data had MGMT promoter methylation. Median PFS was 8.4 months (95% CI 7.3-11.0) and OS was 18.7 months (95% CI 13.1-25.3). Only 30 patients (44%) experienced central recurrence, 6 (9%) in-field, 16 (23.5%) marginal and 16 (23.5%) distant. On multivariate analysis, younger age (HR 0.95, 95% CI 0.93-0.97, p = 0.0001), higher performance status (HR 0.39, 95% CI 0.16-0.95, p = 0.04), gross total resection (GTR) versus biopsy (HR 0.37, 95% CI 0.16-0.85, p = 0.02) and MGMT methylation (HR 0.25, 95% CI 0.09-0.71, p = 0.009) were associated with improved OS. Only distant versus central recurrence (p = 0.03) and GTR (p = 0.02) were associated with longer time to progression. Late grade 3 neurologic toxicity was rare (6%) in patients experiencing long-term survival. CONCLUSION/CONCLUSIONS:Dose-escalated chemoRT resulted in lower rates of central recurrence and prolonged time to progression compared to historical controls, although a significant number of central recurrences were still observed. Advanced imaging and correlative molecular studies may enable targeted treatment advances that reduce rates of in- and out-of-field progression.