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

Quantitative assessment of parenchymal and ventricular readjustment to intracranial pressure relief

A 26-year-old patient underwent endoscopic third ventriculostomy for the treatment of obstructive hydrocephalus. 3D volume data sets were obtained at 3 T before surgery and three times after surgery. Off-line analysis of individual imaging data (initial linear registration, intensity adjustment, and final nonlinear registration of pre- to postoperative MR images) yielded 3D displacement fields representing the postoperative structural brain change. In principle, such an analysis technique can be used in any clinical follow-up for which careful observation of tissue readjustment is of particular importance

Do quiescent arachnoid cysts alter CNS functional organization? An fMRI and morphometric study

OBJECTIVE: To investigate whether congenital and clinically quiescent arachnoid cysts (AC) in the left temporal fossa alter the functional organization of adjacent cortices. METHODS: fMRI mapping was applied in five right-handed asymptomatic patients to determine the functional organization of language. Moreover, morphometry was performed in each patient to gain the size of cortical surface areas and cortical thickness values in the neighboring brain adjacent to the AC and explicitly in the left opercular region. RESULTS: Four patients showed a clear left hemisphere language dominance regardless of the cyst size; a mixed laterality of language organization was found in the remaining patient. An interesting dissociation of morphometric data was assessed when comparing strongly language-related cortices in the inferior frontal gyrus with the entire neighboring cortices. Morphometry in the neighboring brain regions of the AC showed 1) overall reduced cortical surface areas and 2) a decrease in cortical thickness compared to the homologous right side. However, the surface area of the fronto-opercular region in the left inferior frontal gyrus-i.e., the pars triangularis and the pars opercularis-was larger on the left as compared to the right side. Both structures have earlier been identified to represent the morphologic substrate of language dominance in the left hemisphere. CONCLUSION: Arachnoid cysts do not disturb the normal asymmetry of hemisphere language organization despite delicate locations adjacent to the left inferior frontal gyrus

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

Differentiation of cerebral tumors using multi-section echo planar MR perfusion imaging

Objective: We have investigated the performance of magnetic resonance (MR) perfusion imaging to differentiate between astrocytomas grade II, grade III and glioblastomas in a prospective study. Materials and methods: In 33 patients with suspected supratentorial primary cerebral tumors we performed multi-section Echo Planar MR perfusion imaging. Regional cerebral blood volume (rCBV) maps were calculated and the maximum rCBV was determined from the entire lesion. This value was divided by the mean rCBV value from the contralateral side, which provided the rCBV index used in this study. The rCBV index was correlated with the histological tumor classification after stereotactic biopsy (n=7) or open resection (n=26). Results: The maximum rCBV index was 1.2±0.8 for grade II astrocytomas (n=3), 4.0±1.2 for grade III astrocytomas (n=13), and 10.3±3.3 for glioblastomas (n=17). The difference between grade III astrocytomas and glioblastomas was highly significant (P<0.001). Discussion and conclusion: The rCBV index measured with multi-section Echo Planar MR perfusion is capable of differentiating grade III astrocytomas from glioblastomas. It serves as an additional parameter to establish a diagnosis in cases where it is not possible to clearly differentiate between these types of tumors on the basis of conventional MR imaging. MR perfusion imaging also provides information about spatial heterogeneities within a tumor which might improve diagnostic performance. This technology may also be of interest for follow-up examinations after histological diagnosis and further treatment

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