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

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

Intracranial mesenchymal tumor with FET-CREB fusion - A unifying diagnosis for the spectrum of intracranial myxoid mesenchymal tumors and angiomatoid fibrous histiocytoma-like neoplasms

Intracranial mesenchymal tumors with FET-CREB fusions are a recently described group of neoplasms in children and young adults characterized by fusion of a FET family gene (usually EWSR1, but rarely FUS) to a CREB family transcription factor (ATF1, CREB1, or CREM), and have been variously termed intracranial angiomatoid fibrous histiocytoma or intracranial myxoid mesenchymal tumor. The clinical outcomes, histologic features, and genomic landscape are not well defined. Here we studied twenty patients with intracranial mesenchymal tumors proven to harbor FET-CREB fusion by next-generation sequencing (NGS). The 16 female and 4 male patients had a median age of 14 years (range 4-70). Tumors were uniformly extra-axial or intraventricular and located at the cerebral convexities (n=7), falx (2), lateral ventricles (4), tentorium (2), cerebellopontine angle (4), and spinal cord (1). NGS demonstrated that 8 tumors harbored EWSR1-ATF1 fusion, 7 had EWSR1-CREB1, 4 had EWSR1-CREM, and 1 had FUS-CREM. Tumors were uniformly well-circumscribed and typically contrast-enhancing with solid and cystic growth. Tumors with EWSR1-CREB1 fusions more often featured stellate/spindle cell morphology, mucin-rich stroma, and hemangioma-like vasculature compared to tumors with EWSR1-ATF1 fusions that most often featured sheets of epithelioid cells with mucin-poor collagenous stroma. These tumors demonstrated polyphenotypic immunoprofiles with frequent positivity for desmin, EMA, CD99, MUC4, and synaptophysin, but absence of SSTR2A, myogenin, and HMB45 expression. There was a propensity for local recurrence with a median progression-free survival of 12 months and a median overall survival of greater than 60 months, with three patients succumbing to disease (all with EWSR1-ATF1 fusions). In combination with prior case series, this study provides further insight into intracranial mesenchymal tumors with FET-CREB fusion, which represent a distinct group of CNS tumors encompassing both intracranial myxoid mesenchymal tumor and angiomatoid fibrous histiocytoma-like neoplasms

Integration of Machine Learning and Mechanistic Models Accurately Predicts Variation in Cell Density of Glioblastoma Using Multiparametric MRI

Glioblastoma (GBM) is a heterogeneous and lethal brain cancer. These tumors are followed using magnetic resonance imaging (MRI), which is unable to precisely identify tumor cell invasion, impairing effective surgery and radiation planning. We present a novel hybrid model, based on multiparametric intensities, which combines machine learning (ML) with a mechanistic model of tumor growth to provide spatially resolved tumor cell density predictions. The ML component is an imaging data-driven graph-based semi-supervised learning model and we use the Proliferation-Invasion (PI) mechanistic tumor growth model. We thus refer to the hybrid model as the ML-PI model. The hybrid model was trained using 82 image-localized biopsies from 18 primary GBM patients with pre-operative MRI using a leave-one-patient-out cross validation framework. A Relief algorithm was developed to quantify relative contributions from the data sources. The ML-PI model statistically significantly outperformed (p \u3c 0.001) both individual models, ML and PI, achieving a mean absolute predicted error (MAPE) of 0.106 ± 0.125 versus 0.199 ± 0.186 (ML) and 0.227 ± 0.215 (PI), respectively. Associated Pearson correlation coefficients for ML-PI, ML, and PI were 0.838, 0.518, and 0.437, respectively. The Relief algorithm showed the PI model had the greatest contribution to the result, emphasizing the importance of the hybrid model in achieving the high accuracy

Identifying the spatial and temporal dynamics of molecularly-distinct glioblastoma sub-populations

Glioblastomas (GBMs) are the most aggressive primary brain tumours and have no known cure. Each individual tumour comprises multiple sub-populations of genetically-distinct cells that may respond differently to targeted therapies and may contribute to disappointing clinical trial results. Image-localized biopsy techniques allow multiple biopsies to be taken during surgery and provide information that identifies regions where particular sub-populations occur within an individual GBM, thus providing insight into their regional genetic variability. These sub-populations may also interact with one another in a competitive or cooperative manner; it is important to ascertain the nature of these interactions, as they may have implications for responses to targeted therapies