Improving utility of brain tumor confocal laser endomicroscopy: objective value assessment and diagnostic frame detection with convolutional neural networks

Confocal laser endomicroscopy (CLE), although capable of obtaining images at cellular resolution during surgery of brain tumors in real time, creates as many non-diagnostic as diagnostic images. Non-useful images are often distorted due to relative motion between probe and brain or blood artifacts. Many images, however, simply lack diagnostic features immediately informative to the physician. Examining all the hundreds or thousands of images from a single case to discriminate diagnostic images from nondiagnostic ones can be tedious. Providing a real-time diagnostic value assessment of images (fast enough to be used during the surgical acquisition process and accurate enough for the pathologist to rely on) to automatically detect diagnostic frames would streamline the analysis of images and filter useful images for the pathologist/surgeon. We sought to automatically classify images as diagnostic or non-diagnostic. AlexNet, a deep-learning architecture, was used in a 4-fold cross validation manner. Our dataset includes 16,795 images (8572 nondiagnostic and 8223 diagnostic) from 74 CLE-aided brain tumor surgery patients. The ground truth for all the images is provided by the pathologist. Average model accuracy on test data was 91% overall (90.79 % accuracy, 90.94 % sensitivity and 90.87 % specificity). To evaluate the model reliability we also performed receiver operating characteristic (ROC) analysis yielding 0.958 average for the area under ROC curve (AUC). These results demonstrate that a deeply trained AlexNet network can achieve a model that reliably and quickly recognizes diagnostic CLE images.Comment: SPIE Medical Imaging: Computer-Aided Diagnosis 201

Prospects for Theranostics in Neurosurgical Imaging: Empowering Confocal Laser Endomicroscopy Diagnostics via Deep Learning

Confocal laser endomicroscopy (CLE) is an advanced optical fluorescence imaging technology that has the potential to increase intraoperative precision, extend resection, and tailor surgery for malignant invasive brain tumors because of its subcellular dimension resolution. Despite its promising diagnostic potential, interpreting the gray tone fluorescence images can be difficult for untrained users. In this review, we provide a detailed description of bioinformatical analysis methodology of CLE images that begins to assist the neurosurgeon and pathologist to rapidly connect on-the-fly intraoperative imaging, pathology, and surgical observation into a conclusionary system within the concept of theranostics. We present an overview and discuss deep learning models for automatic detection of the diagnostic CLE images and discuss various training regimes and ensemble modeling effect on the power of deep learning predictive models. Two major approaches reviewed in this paper include the models that can automatically classify CLE images into diagnostic/nondiagnostic, glioma/nonglioma, tumor/injury/normal categories and models that can localize histological features on the CLE images using weakly supervised methods. We also briefly review advances in the deep learning approaches used for CLE image analysis in other organs. Significant advances in speed and precision of automated diagnostic frame selection would augment the diagnostic potential of CLE, improve operative workflow and integration into brain tumor surgery. Such technology and bioinformatics analytics lend themselves to improved precision, personalization, and theranostics in brain tumor treatment.Comment: See the final version published in Frontiers in Oncology here: https://www.frontiersin.org/articles/10.3389/fonc.2018.00240/ful

The ketogenic diet reverses gene expression patterns and reduces reactive oxygen species levels when used as an adjuvant therapy for glioma

<p>Abstract</p> <p>Background</p> <p>Malignant brain tumors affect people of all ages and are the second leading cause of cancer deaths in children. While current treatments are effective and improve survival, there remains a substantial need for more efficacious therapeutic modalities. The ketogenic diet (KD) - a high-fat, low-carbohydrate treatment for medically refractory epilepsy - has been suggested as an alternative strategy to inhibit tumor growth by altering intrinsic metabolism, especially by inducing glycopenia.</p> <p>Methods</p> <p>Here, we examined the effects of an experimental KD on a mouse model of glioma, and compared patterns of gene expression in tumors vs. normal brain from animals fed either a KD or a standard diet.</p> <p>Results</p> <p>Animals received intracranial injections of bioluminescent GL261-luc cells and tumor growth was followed <it>in vivo</it>. KD treatment significantly reduced the rate of tumor growth and prolonged survival. Further, the KD reduced reactive oxygen species (ROS) production in tumor cells. Gene expression profiling demonstrated that the KD induces an overall reversion to expression patterns seen in non-tumor specimens. Notably, genes involved in modulating ROS levels and oxidative stress were altered, including those encoding cyclooxygenase 2, glutathione peroxidases 3 and 7, and periredoxin 4.</p> <p>Conclusions</p> <p>Our data demonstrate that the KD improves survivability in our mouse model of glioma, and suggests that the mechanisms accounting for this protective effect likely involve complex alterations in cellular metabolism beyond simply a reduction in glucose.</p

Accurate state estimation from uncertain data and models: an application of data assimilation to mathematical models of human brain tumors

<p>Abstract</p> <p>Background</p> <p>Data assimilation refers to methods for updating the state vector (initial condition) of a complex spatiotemporal model (such as a numerical weather model) by combining new observations with one or more prior forecasts. We consider the potential feasibility of this approach for making short-term (60-day) forecasts of the growth and spread of a malignant brain cancer (glioblastoma multiforme) in individual patient cases, where the observations are synthetic magnetic resonance images of a hypothetical tumor.</p> <p>Results</p> <p>We apply a modern state estimation algorithm (the Local Ensemble Transform Kalman Filter), previously developed for numerical weather prediction, to two different mathematical models of glioblastoma, taking into account likely errors in model parameters and measurement uncertainties in magnetic resonance imaging. The filter can accurately shadow the growth of a representative synthetic tumor for 360 days (six 60-day forecast/update cycles) in the presence of a moderate degree of systematic model error and measurement noise.</p> <p>Conclusions</p> <p>The mathematical methodology described here may prove useful for other modeling efforts in biology and oncology. An accurate forecast system for glioblastoma may prove useful in clinical settings for treatment planning and patient counseling.</p> <p>Reviewers</p> <p>This article was reviewed by Anthony Almudevar, Tomas Radivoyevitch, and Kristin Swanson (nominated by Georg Luebeck).</p

Sulforhodamine 101 selectively labels human astrocytoma cells in an animal model of glioblastoma

AbstractSulforhodamine 101 (SR101) is a useful tool for immediate staining of astrocytes. We hypothesized that if the selectivity of SR101was maintained in astrocytoma cells, it could prove useful for glioma research. Cultured astrocytoma cells and acute slices from orthotopic human glioma (n=9) and lymphoma (n=6) xenografts were incubated with SR101 and imaged with confocal microscopy. A subset of slices (n=18) were counter-immunostained with glial fibrillary acidic protein and CD20 for stereological assessment of SR101 co-localization. SR101 differentiated astrocytic tumor cells from lymphoma cells. In acute slices, SR101 labeled 86.50% (±1.86; p<0.0001) of astrocytoma cells and 2.19% (±0.47; p<0.0001) of lymphoma cells. SR101-labeled astrocytoma cells had a distinct morphology when compared with in vivo astrocytes. Immediate imaging of human astrocytoma cells in vitro and in ex vivo rodent xenograft tissue labeled with SR101 can identify astrocytic tumor cells and help visualize the tumor margin. These features are useful in studying astrocytoma in the laboratory and may have clinical applications

Intraoperative in vivo confocal endomicroscopy of the glioma margin: performance assessment of image interpretation by neurosurgeon users.

OBJECTIVES Confocal laser endomicroscopy (CLE) is an intraoperative real-time cellular resolution imaging technology that images brain tumor histoarchitecture. Previously, we demonstrated that CLE images may be interpreted by neuropathologists to determine the presence of tumor infiltration at glioma margins. In this study, we assessed neurosurgeons' ability to interpret CLE images from glioma margins and compared their assessments to those of neuropathologists. METHODS In vivo CLE images acquired at the glioma margins that were previously reviewed by CLE-experienced neuropathologists were interpreted by four CLE-experienced neurosurgeons. A numerical scoring system from 0 to 5 and a dichotomous scoring system based on pathological features were used. Scores from assessments of hematoxylin and eosin (H&E)-stained sections and CLE images by neuropathologists from a previous study were used for comparison. Neurosurgeons' scores were compared to the H&E findings. The inter-rater agreement and diagnostic performance based on neurosurgeons' scores were calculated. The concordance between dichotomous and numerical scores was determined. RESULTS In all, 4275 images from 56 glioma margin regions of interest (ROIs) were included in the analysis. With the numerical scoring system, the inter-rater agreement for neurosurgeons interpreting CLE images was moderate for all ROIs (mean agreement, 61%), which was significantly better than the inter-rater agreement for the neuropathologists (mean agreement, 48%) (p < 0.01). The inter-rater agreement for neurosurgeons using the dichotomous scoring system was 83%. The concordance between the numerical and dichotomous scoring systems was 93%. The overall sensitivity, specificity, positive predictive value, and negative predictive value were 78%, 32%, 62%, and 50%, respectively, using the numerical scoring system and 80%, 27%, 61%, and 48%, respectively, using the dichotomous scoring system. No statistically significant differences in diagnostic performance were found between the neurosurgeons and neuropathologists. CONCLUSION Neurosurgeons' performance in interpreting CLE images was comparable to that of neuropathologists. These results suggest that CLE could be used as an intraoperative guidance tool with neurosurgeons interpreting the images with or without assistance of the neuropathologists. The dichotomous scoring system is robust yet simple and may streamline rapid, simultaneous interpretation of CLE images during imaging

Analyzing international medical graduate research productivity for application to US neurosurgery residency and beyond: A survey of applicants, program directors, and institutional experience

BackgroundThe authors investigated perceived discrepancies between the neurosurgical research productivity of international medical graduates (IMGs) and US medical graduates (USMGs) through the perspective of program directors (PDs) and successfully matched IMGs.MethodsResponses to 2 separate surveys on neurosurgical applicant research productivity in 115 neurosurgical programs and their PDs were analyzed. Neurosurgical research participation was analyzed using an IMG survey of residents who matched into neurosurgical residency within the previous 8 years. Productivity of IMGs conducting dedicated research at the study institution was also analyzed.ResultsThirty-two of 115 (28%) PDs responded to the first research productivity survey and 43 (37%) to the second IMG research survey. PDs expected neurosurgery residency applicants to spend a median of 12–24 months on research (Q1-Q3: 0–12 to 12–24; minimum time: 0–24; maximum time: 0–48) and publish a median of 5 articles (Q1-Q3: 2–5 to 5–10; minimum number: 0–10; maximum number: 4–20). Among 43 PDs, 34 (79%) ranked “research institution or associated personnel” as the most important factor when evaluating IMGs' research. Forty-two of 79 (53%) IMGs responding to the IMG-directed survey reported a median of 30 months (Q1-Q3: 18–48; range: 4–72) of neurosurgical research and 12 published articles (Q1-Q3: 6–24; range: 1–80) before beginning neurosurgical residency. Twenty-two PDs (69%) believed IMGs complete more research than USMGs before residency. Of 20 IMGs conducting dedicated neuroscience/neurosurgery research at the study institution, 16 of 18 who applied matched or entered a US neurosurgical training program; 2 applied and entered a US neurosurgical clinical fellowship.ConclusionThe research work of IMGs compared to USMGs who apply to neurosurgery residency exceeds PDs' expectations regarding scientific output and research time. Many PDs perceive IMG research productivity before residency application as superior to USMGs. Although IMGs comprise a small percentage of trainees, they are responsible for a significant amount of US-published neurosurgical literature. Preresidency IMG research periods may be improved with dedicated mentoring and advising beginning before the research period, during the period, and within a neurosurgery research department, providing a formal structure such as a research fellowship or graduate program for IMGs aspiring to train in the US